Bio


Christopher Manning is the inaugural Thomas M. Siebel Professor of Machine Learning in the Departments of Linguistics and Computer Science at Stanford University, Director of the Stanford Artificial Intelligence Laboratory (SAIL), and an Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). His research goal is computers that can intelligently process, understand, and generate human languages. Manning was an early leader in applying Deep Learning to Natural Language Processing (NLP), with well-known research on the GloVe model of word vectors, attention, machine translation, question answering, self-supervised model pre-training, tree-recursive neural networks, machine reasoning, dependency parsing, sentiment analysis, and summarization. He also focuses on computational linguistic approaches to parsing, natural language inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning has coauthored leading textbooks on statistical approaches to NLP (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), as well as linguistic monographs on ergativity and complex predicates. His online CS224N Natural Language Processing with Deep Learning videos have been watched by hundreds of thousands of people. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL (2015). His research has won ACL, Coling, EMNLP, and CHI Best Paper Awards, an ACL Test of Time Award, and the IEEE John von Neumann Medal (2024). He has a B.A. (Hons) from The Australian National University and a Ph.D. from Stanford in 1994, and an Honorary Doctorate from U. Amsterdam in 2023, and he held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP and Stanza software.

Academic Appointments


Administrative Appointments


  • Associate Director, Human-Centered Artificial Intelligence Initiative (HAI) (2018 - Present)
  • Director, Stanford Artificial Intelligence Laboratory (2018 - Present)

Honors & Awards


  • John von Neumann Medal, IEEE (2024)
  • Honorary Doctorate, University of Amsterdam (2023)
  • Fellow, ACL
  • Fellow, AAAI
  • Fellow, ACM

Program Affiliations


  • Symbolic Systems Program

Professional Education


  • PhD, Stanford University (1994)

2024-25 Courses


Stanford Advisees


All Publications


  • Handwritten Code Recognition for Pen-and-Paper CS Education Islam, M., Doumbouya, M., Manning, C. D., Piech, C., ASSOC COMPUTING MACHINERY ASSOC COMPUTING MACHINERY. 2024: 200-210
  • ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS Wu, Z., Manning, C. D., Potts, C. 2023; 11: 1719-1733
  • Backpack Language Models Hewitt, J., Thickstun, J., Manning, C. D., Liang, P., Rogers, A., Boyd-Graber, J., Okazaki, N. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 9103-9125
  • Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models Henderson, P., Mitchell, E., Manning, C. D., Jurafsky, D., Finn, C., ACM ASSOC COMPUTING MACHINERY. 2023: 287-296
  • Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection Ogunremi, T., Jurafsky, D., Manning, C. D., Augenstein, Vlachos, A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 1251-1266
  • Grokking of Hierarchical Structure in Vanilla Transformers Murty, S., Sharma, P., Andreas, J., Manning, C. D., Boyd-Graber, J., Okazaki, N., Rogers, A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 439-448
  • Human Language Understanding & Reasoning DAEDALUS Manning, C. D. 2022; 151 (2): 127-138
  • Synthetic Disinformation Attacks on Automated Fact Verification Systems Du, Y., Bosselut, A., Manning, C. D., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2022: 10581-10589
  • Memory-Based Model Editing at Scale Mitchell, E., Lin, C., Bosselut, A., Manning, C. D., Finn, C., Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022
  • Biomedical and clinical English model packages for the Stanza Python NLP library. Journal of the American Medical Informatics Association : JAMIA Zhang, Y., Zhang, Y., Qi, P., Manning, C. D., Langlotz, C. P. 2021

    Abstract

    OBJECTIVE: The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text.MATERIALS AND METHODS: We implement and train biomedical and clinical English NLP pipelines by extending the widely used Stanza library originally designed for general NLP tasks. Our models are trained with a mix of public datasets such as the CRAFT treebank as well as with a private corpus of radiology reports annotated with 5 radiology-domain entities. The resulting pipelines are fully based on neural networks, and are able to perform tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for both biomedical and clinical text. We compare our systems against popular open-source NLP libraries such as CoreNLP and scispaCy, state-of-the-art models such as the BioBERT models, and winning systems from the BioNLP CRAFT shared task.RESULTS: For syntactic analysis, our systems achieve much better performance compared with the released scispaCy models and CoreNLP models retrained on the same treebanks, and are on par with the winning system from the CRAFT shared task. For NER, our systems substantially outperform scispaCy, and are better or on par with the state-of-the-art performance from BioBERT, while being much more computationally efficient.CONCLUSIONS: We introduce biomedical and clinical NLP packages built for the Stanza library. These packages offer performance that is similar to the state of the art, and are also optimized for ease of use. To facilitate research, we make all our models publicly available. We also provide an online demonstration (http://stanza.run/bio).

    View details for DOI 10.1093/jamia/ocab090

    View details for PubMedID 34157094

  • Universal Dependencies COMPUTATIONAL LINGUISTICS de Marneffe, M., Manning, C. D., Nivre, J., Zeman, D. 2021; 47 (2): 255-308
  • Answering Open-Domain Questions of Varying Reasoning Steps from Text Qi, P., Lee, H., Sido, O., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 3599-3614
  • DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference Murty, S., Hashimoto, T. B., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 1113-1125
  • Human-like informative conversations: Better acknowledgements using conditional mutual information Paranjape, A., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 768-781
  • Challenges for Information Extraction from Dialogue in Criminal Law Hong, J., Voss, C., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 71-81
  • Large-Scale Quantitative Evaluation of Dialogue Agents' Response Strategies against Offensive Users Li, H., Soylu, D., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS. 2021: 556-561
  • Effective Social Chatbot Strategies for Increasing User Initiative Hardy, A., Paranjape, A., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS. 2021: 99-110
  • Understanding and predicting user dissatisfaction in a neural generative chatbot See, A., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS. 2021: 1-12
  • Conditional probing: measuring usable information beyond a baseline Hewitt, J., Ethayarajh, K., Liang, P., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2021: 1626-1639
  • ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS INFORMATION SYSTEMS RESEARCH Clark, K., Luong, M., Le, Q. V., Manning, C. D. 2020
  • Emergent linguistic structure in artificial neural networks trained by self-supervision. Proceedings of the National Academy of Sciences of the United States of America Manning, C. D., Clark, K. n., Hewitt, J. n., Khandelwal, U. n., Levy, O. n. 2020

    Abstract

    This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks.

    View details for DOI 10.1073/pnas.1907367117

    View details for PubMedID 32493748

  • Pre-Training Transformers as Energy-Based Cloze Models Clark, K., Luong, M., Le, Q. V., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 285-294
  • RNNs can generate bounded hierarchical languages with optimal memory Hewitt, J., Hahn, M., Ganguli, S., Liang, P., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 1978-2010
  • Stanza: A Python Natural Language Processing Toolkit for Many Human Languages Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 101–8
  • Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation Dhole, K. D., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 752–65
  • SLM: Learning a Discourse Language Representation with Sentence Unshuffling Lee, H., Hudson, D. A., Lee, K., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2020: 1551-1562
  • GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering visualreasoning.net Hudson, D. A., Manning, C. D., IEEE Comp Soc IEEE. 2019: 6693–6702
  • CoQA: A Conversational Question Answering Challenge TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS Reddy, S., Chen, D., Manning, C. D. 2019; 7: 249-266
  • A Structural Probe for Finding Syntax in Word Representations Hewitt, J., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 4129-4138
  • Answering Complex Open-domain Questions Through Iterative Query Generation Qi, P., Lin, X., Mehr, L., Wang, Z., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 2590-2602
  • What does BERT look at? An Analysis of BERT's Attention Clark, K., Khandelwal, U., Levy, O., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 276–86
  • Learning by Abstraction: The Neural State Machine Hudson, D. A., Manning, C. D., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • BAM! Born-Again Multi-Task Networks for Natural Language Understanding Clark, K., Minh-Thang Luong, Khandelwal, U., Manning, C. D., Le, Q. V., ACL, Korhonen, A., Traum, D., Marquez, L. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 5931–37
  • Simpler but More Accurate Semantic Dependency Parsing Dozat, T., Manning, C. D., Gurevych, Miyao, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 484–90
  • Semi-Supervised Sequence Modeling with Cross-View Training Clark, K., Luong, M., Manning, C. D., Le, Q. V., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 1914-1925
  • Graph Convolution over Pruned Dependency Trees Improves Relation Extraction Zhang, Y., Qi, P., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 2205-2215
  • HOTPOTQA: A Dataset for Diverse, Explainable Multi-hop Question Answering Yang, Z., Peng, Q., Zhang, S., Bengiov, Y., Cohent, W. W., Salakhutdinov, R., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 2369-2380
  • Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts Lamm, M., Chaganty, A., Manning, C. D., Jurafsky, D., Liang, P., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2018: 82-92
  • Arc-swift: A Novel Transition System for Dependency Parsing Qi, P., Manning, C. D., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 110–17
  • Key-Value Retrieval Networks for Task-Oriented Dialogue Eric, M., Krishnan, L., Charette, F., Manning, C. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS. 2017: 37-49
  • Get To The Point: Summarization with Pointer-Generator Networks See, A., Liu, P. J., Manning, C. D., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 1073–83
  • Naturalizing a Programming Language via Interactive Learning Wang, S. I., Ginn, S., Liang, P., Manning, C. D., Barzilay, R., Kan, M. Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2017: 929–38
  • A comparison of Named-Entity Disambiguation and Word Sense Disambiguation Chang, A. X., Spitkovsky, V., Manning, C. D., Agirre, E., Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2016: 860–67
  • A Fast Unified Model for Parsing and Sentence Understanding Bowman, S. R., Gauthier, J., Rastogi, A., Gupta, R., Manning, C. D., Potts, C., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1466–77
  • Learning Language Games through Interaction Wang, S. I., Liang, P., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 2368–78
  • Understanding Human Language: Can NLP and Deep Learning Help? Manning, C., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2016: 1
  • Combining Natural Logic and Shallow Reasoning for Question Answering Angeli, G., Nayak, N., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 442–52
  • A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task Chen, D., Bolton, J., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 2358–67
  • Improving Coreference Resolution by Learning Entity-Level Distributed Representations Clark, K., Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 643–53
  • Universal Dependencies v1: A Multilingual Treebank Collection Nivre, J., de Marneffe, M., Ginter, F., Goldberg, Y., Hajic, J., Manning, C. D., McDonald, R., Petrov, S., Pyysalo, S., Silveira, N., Tsarfaty, R., Zeman, D., Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2016: 1659–66
  • Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks Schuster, S., Manning, C. D., Calzolari, N., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2016: 2371–78
  • Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models Minh-Thang Luong, Manning, C. D., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1054–63
  • Computational Linguistics and Deep Learning COMPUTATIONAL LINGUISTICS Manning, C. D. 2015; 41 (4): 701-707
  • Natural Language Translation at the Intersection of AI and HCI COMMUNICATIONS OF THE ACM Green, S., Heer, J., Manning, C. D. 2015; 58 (9): 47-54

    View details for DOI 10.1145/2767151

    View details for Web of Science ID 000360214000018

  • Advances in natural language processing SCIENCE Hirschberg, J., Manning, C. D. 2015; 349 (6245): 261-266

    Abstract

    Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area.

    View details for DOI 10.1126/science.aaa8685

    View details for Web of Science ID 000358218600041

    View details for PubMedID 26185244

  • Text to 3D Scene Generation with Rich Lexical Grounding Chang, A., Monroe, W., Savva, M., Potts, C., Manning, C. D., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 53–62
  • Forum77: An Analysis of an Online Health Forum Dedicated to Addiction Recovery MacLean, D., Gupta, S., Lembke, A., Manning, C., Heer, J., ACM ASSOC COMPUTING MACHINERY. 2015: 1511–26
  • Robust Subgraph Generation Improves Abstract Meaning Representation Parsing Werling, K., Angeli, G., Manning, C. D., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 982–91
  • Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks Tai, K., Socher, R., Manning, C. D., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1556–66
  • Leveraging Linguistic Structure For Open Domain Information Extraction Angeli, G., Premkumar, M., Manning, C. D., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 344–54
  • On-the-Job Learning with Bayesian Decision Theory Werling, K., Chaganty, A., Liang, P., Manning, C. D., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • Entity-Centric Coreference Resolution with Model Stacking Clark, K., Manning, C. D., Zong, C., Strube, M. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2015: 1405–15
  • Induced lexico-syntactic patterns improve information extraction from online medical forums. Journal of the American Medical Informatics Association Gupta, S., MacLean, D. L., Heer, J., Manning, C. D. 2014; 21 (5): 902-909

    Abstract

    To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries.Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. Identification of SC and DT terms in PAT would enable exploration of efficacy and side effects for not only pharmaceutical drugs, but also for home remedies and components of daily care.We use SC and DT term dictionaries compiled from online sources to label several discussion forums from MedHelp (http://www.medhelp.org). We then iteratively induce lexico-syntactic patterns corresponding strongly to each entity type to extract new SC and DT terms.Our system is able to extract symptom descriptions and treatments absent from our original dictionaries, such as 'LADA', 'stabbing pain', and 'cinnamon pills'. Our system extracts DT terms with 58-70% F1 score and SC terms with 66-76% F1 score on two forums from MedHelp. We show improvements over MetaMap, OBA, a conditional random field-based classifier, and a previous pattern learning approach.Our entity extractor based on lexico-syntactic patterns is a successful and preferable technique for identifying specific entity types in PAT. To the best of our knowledge, this is the first paper to extract SC and DT entities from PAT. We exhibit learning of informal terms often used in PAT but missing from typical dictionaries.

    View details for DOI 10.1136/amiajnl-2014-002669

    View details for PubMedID 24970840

    View details for PubMedCentralID PMC4147618

  • Two Knives Cut Better Than One: Chinese Word Segmentation with Dual Decomposition Wang, M., Voigt, R., Manning, C. D., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 193–98
  • Robust Logistic Regression using Shift Parameters Tibshirani, J., Manning, C. D., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 124-129
  • Word Segmentation of Informal Arabic with Domain Adaptation Monroe, W., Green, S., Manning, C. D., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 206-211
  • TransPhoner: Automated Mnemonic Keyword Generation Savva, M., Chang, A. X., Manning, C. D., Hanrahan, P., ACM ASSOC COMPUTING MACHINERY. 2014: 3725-3734
  • Faster Phrase-Based Decoding by Refining Feature State Heafield, K., Kayser, M., Manning, C. D., Toutanova, K., Wu, H. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 130-135
  • Natural Logic and Natural Language Inference COMPUTING MEANING, VOL 4 MacCartney, B., Manning, C. D., Bunt, H., Bos, J., Pulman, S. 2014; 47: 129–47
  • A Gold Standard Dependency Corpus for English Silveira, N., Dozat, T., de Marneffe, M., Bowman, S. R., Connor, M., Bauer, J., Manning, C. D., Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2014: 2897–2904
  • Event Extraction Using Distant Supervision Reschke, K., Jankowiak, M., Surdeanu, M., Manning, C. D., Jurafsky, D., Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2014: 4527–31
  • Universal Stanford Dependencies: A cross-linguistic typology de Marneffe, M., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., Manning, C. D., Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. 2014: 4585–92
  • Learning Distributed Representations for Structured Output Prediction Srikumar, V., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
  • Global Belief Recursive Neural Networks Paulus, R., Socher, R., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
  • Simple MAP Inference via Low-Rank Relaxations Frostig, R., Wang, S., Liang, P., Manning, C. D., Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
  • The Stanford CoreNLP Natural Language Processing Toolkit Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., McClosky, D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2014: 55–60
  • Differentiating language usage through topic models POETICS McFarland, D. A., Ramage, D., Chuang, J., Heer, J., Manning, C. D., Jurafsky, D. 2013; 41 (6): 607-625
  • Parsing Models for Identifying Multiword Expressions COMPUTATIONAL LINGUISTICS Green, S., de Marneffe, M., Manning, C. D. 2013; 39 (1): 195-227
  • The Efficacy of Human Post-editing for Language Translation CHI Green, S., Heer, J., Manning, C. D. 2013
  • Learning a Product of Experts with Elitist Lasso. Wang, M., Manning, Christopher, D. 2013
  • Bilingual Word Embeddings for Phrase-Based Machine Translation Zou, Will, Y., Socher, R., Cer, D., Manning, Christopher, D. 2013
  • Effect of Nonlinear Deep Architecture in Sequence Labeling. Wang, M., Manning, Christopher, D. 2013
  • Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A. 2013
  • Zero-Shot Learning Through Cross-Modal Transfer Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C., D., Ng, A., Y. 2013
  • Named Entity Recognition with Bilingual Constraints. Che, W., Wang, M., Manning, Christopher, D., Liu, T. 2013
  • Effective Bilingual Constraints for Semi-supervised Learning of Named Entity Recognizers. Wang, M., Che, W., Manning, Christopher, D. 2013
  • Parsing With Compositional Vector Grammars Socher, R., Bauer, J., Manning, Christopher, D., Ng, Andrew, Y. 2013
  • Differentiating Language Usage through Topic Models Poetics Daniel A. McFarland, Daniel Ramage, Jason Chuang, Jeffrey Heer, and Christopher D. Manning McFarland, Daniel, A., Ramage, D., Chuang, J., Heer, J., Manning, Christopher, D. 2013
  • Fast and Adaptive Online Training of Feature-Rich Translation Models Proc. ACL Green, S., Wang, S., Cer, D., Manning, C. D. 2013
  • Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition. Wang, M., Che, W., Manning, Christopher, D. 2013
  • Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions. Recasens, M., Can, M., Jurafsky, D. 2013
  • Tradition and Modernity in 20th Century Chinese Poetry. Voigt, R., Jurafsky, D. 2013
  • Generating Recommendation Dialogs by Extracting Information from User Reviews. Reschke, K., Vogel, A., Jurafsky, D. 2013
  • Linguistic Models for Analyzing and Detecting Biased Language. Recasens, M., Danescu-Niculescu-Mizil, C., Jurafsky, D. 2013
  • A computational approach to politeness with application to social factors Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., Potts, C. 2013
  • Effect of Non-linear Deep Architecture in Sequence Labeling. Wang, M., Manning, Christopher, D. 2013
  • Philosophers are Mortal: Inferring the Truth of Unseen Facts. Angeli, G., Manning, C. 2013
  • Semantic Parsing on Freebase from Question-Answer Pairs. Berant, J., Chou, A., Frostig, R., Liang, P. 2013
  • Learning Biological Processes with Global Constraints. Scaria, A. T., Berant, J., Wang, M., Clark, P., Lewis, J., Harding, B., Manning, Christopher, D. 2013
  • Language-Independent Discriminative Parsing of Temporal Expressions Angeli, G., Uszkoreit, J. 2013
  • Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning. Transactions of the Association for Computational Linguistics Luong, M., Frank, Michael, C., Johnson, M. 2013; 3 (1): 315-323
  • Zero Shot Learning Through Cross-Modal Transfer. In Advances in Neural Information Processing Systems Socher, R., Ganjoo, M., Manning, Christopher, D., Ng, Andrew, Y. 2013; 26
  • The Life and Death of Discourse Entities: Identifying Singleton Mentions Recasens, M., Marneffe, M. d., Potts, C. 2013
  • Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs. Vogel, A., Potts, C., Jurafsky, D. 2013
  • Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors Chen, D., Socher, R., Manning, Christopher, D., Ng, Andrew, Y. 2013
  • Better Word Representations with Recursive Neural Networks for Morphology. Luong, M., Socher, R., Manning, Christopher, D. 2013
  • No country for old members: User lifecycle and linguistic change in online communities. Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C. 2013
  • Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction. Spitkovsky, Valentin, I., Alshawi, H., Jurafsky, D. 2013
  • Reasoning With Neural Tensor Networks For Knowledge Base Completion. In Advances in Neural Information Processing Systems Socher, R., Chen, D., Manning, Christopher, D., Ng, Andrew, Y. 2013; 26
  • Crowdsourcing and the Crisis-affected Population Information Retrieval Munro, R. 2013; 2 (16): 210-266
  • "Without the Clutter of Unimportant Words": Descriptive Keyphrases for Text Visualization ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION Chuang, J., Manning, C. D., Heer, J. 2012; 19 (3)
  • Combining joint models for biomedical event extraction Conference on BioNLP Shared Task McClosky, D., Riedel, S., Surdeanu, M., McCallum, A., Manning, C. D. BIOMED CENTRAL LTD. 2012

    Abstract

    We explore techniques for performing model combination between the UMass and Stanford biomedical event extraction systems. Both sub-components address event extraction as a structured prediction problem, and use dual decomposition (UMass) and parsing algorithms (Stanford) to find the best scoring event structure. Our primary focus is on stacking where the predictions from the Stanford system are used as features in the UMass system. For comparison, we look at simpler model combination techniques such as intersection and union which require only the outputs from each system and combine them directly.First, we find that stacking substantially improves performance while intersection and union provide no significant benefits. Second, we investigate the graph properties of event structures and their impact on the combination of our systems. Finally, we trace the origins of events proposed by the stacked model to determine the role each system plays in different components of the output. We learn that, while stacking can propose novel event structures not seen in either base model, these events have extremely low precision. Removing these novel events improves our already state-of-the-art F1 to 56.6% on the test set of Genia (Task 1). Overall, the combined system formed via stacking ("FAUST") performed well in the BioNLP 2011 shared task. The FAUST system obtained 1st place in three out of four tasks: 1st place in Genia Task 1 (56.0% F1) and Task 2 (53.9%), 2nd place in the Epigenetics and Post-translational Modifications track (35.0%), and 1st place in the Infectious Diseases track (55.6%).We present a state-of-the-art event extraction system that relies on the strengths of structured prediction and model combination through stacking. Akin to results on other tasks, stacking outperforms intersection and union and leads to very strong results. The utility of model combination hinges on complementary views of the data, and we show that our sub-systems capture different graph properties of event structures. Finally, by removing low precision novel events, we show that performance from stacking can be further improved.

    View details for Web of Science ID 000306140800009

    View details for PubMedID 22759463

    View details for PubMedCentralID PMC3395172

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  • Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection In Advances in Neural Information Processing Systems Socher, R., Huang, Eric, H., Pennington, J., Ng, Andrew, Y. 2011; 24
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  • Assessing the relationship between excess argon content and recrystallization of ultrahigh-pressure metamorphic rocks Conference on Goldschmidt 2010 - Earth, Energy, and the Environment Menold, C. A., Grove, M., Manning, C. E. PERGAMON-ELSEVIER SCIENCE LTD. 2010: A698–A698
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  • The NXT-format Switchboard Corpus: a rich resource for investigating the syntax, semantics, pragmatics and prosody of dialogue. Language Resources \& Evaluation 44: Calhoun, S., Carletta, J., Brenier, Jason, M., Mayo, N., Jurafsky, D., Steedman, M. 2010: 387-419
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  • "Was it good? It was provocative." Learning the meaning of scalar adjectives de Marneffe, M., Manning, C. D., Potts, C., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS. 2010: 167–76
  • Characterizing Microblogs with Topic Models Ramage, D., Dumais, S., Liebling, D. 2010
  • Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks Socher, R., Manning, Christopher, D., Ng, Andrew, Y. 2010
  • Legal Claim Identification: Information Extraction with Hierarchically Labeled Data Surdeanu, M., Nallapati, R., Manning, Christopher, D. 2010
  • Learning to Follow Navigational Directions Vogel, A., Jurafsky, D. 2010
  • Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data Finkel, J. R., Manning, Christopher, D. 2010
  • Accurate Non-Hierarchical Phrase-Based Translation Galley, M., Manning, Christopher, D. 2010
  • Better Arabic Parsing: Baselines, Evaluations, and Analysis Green, S., Manning, Christopher, D. 2010
  • Who should I cite? Learning literature search models from citation behavior. Bethard, S., Jurafsky, D. 2010
  • Automatic Domain Adaptation for Parsing McClosky, D., Charniak, E., Johnson, M. 2010
  • How good are humans at solving CAPTCHAs? A large scale evaluation Bursztein, E., Bethard, S., Mitchell, John, C., Jurafsky, D., Fabry, C. 2010
  • Improved Models of Distortion Cost for Statistical Machine Translation Green, S., Galley, M., Manning, Christopher, D. 2010
  • Ensemble Models for Dependency Parsing: Cheap and Good? Surdeanu, M., Manning, Christopher, D. 2010
  • Stanford-UBC Entity Linking at TAC-KBP Chang, Angel, X., Spitkovsky, Valentin, I., Yeh, E., Agirre, E., Manning, Christopher, D. 2010
  • Parsing to Stanford Dependencies: Trade-offs between speed and accuracy Cer, D., Marneffe, M. d., Jurafsky, D., Manning, Christopher, D. 2010
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  • Was it good? It was provocative. Learning the meaning of scalar adjectives Marneffe, M. d., Manning, Christopher, D., Potts, C. 2010
  • Improving the Use of Pseudo-Words for Evaluating Selectional Preferences Chambers, N., Jurafsky, D. 2010
  • Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering Wang, M., Manning, Christopher, D. 2010
  • A Simple Distant Supervision Approach for the TAC-KBP Slot Filling Task Surdeanu, M., McClosky, D., Tibshirani, J., Bauer, J., Chang, Angel, X., Spitkovsky, Valentin, I., Manning, Christopher, D. 2010
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  • Modeling semantic containment and exclusion in natural language inference MacCartney, B., Manning, Christopher, D. 2008
  • Legal Docket Classification: Where Machine Learning Stumbles Nallapati, R., Manning, Christopher, D. 2008
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  • Maximum Conditional Likelihood Linear Regression and Maximum a Posteriori for Hidden Conditional Random Fields Speaker Adaptation Sung, Y., Boulis, C., Jurafsky, D. 2008
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  • Which words are hard to recognize? Lexical, prosodic, and disfluency factors that increase ASR error rates Goldwater, S., Jurafsky, D., Manning, Christopher, D. 2008
  • Finding Contradictions in Text Goldwater, S., Jurafsky, D., Manning, Christopher, D. 2008
  • Social Tag Prediction Heymann, P., Ramage, D., Garcia-Molina, H. 2008
  • Semantic Role Assignment for Event Nominalisations by Leveraging Verbal Data Padó, S., Pennacchiotti, M., Sporleder, C. 2008
  • The Stanford typed dependencies representation Marneffe, M. d., Manning, Christopher, D. 2008
  • Jointly Combining Implicit Constraints Improves Temporal Ordering Chambers, N., Jurafsky, D. 2008
  • Regularization and Search for Minimum Error Rate Training Cer, D., Jurafsky, D., Manning, Christopher, D. 2008
  • Finding Contradictions in Text Marneffe, M. d., Rafferty, Anna, N., Manning, Christopher, D. 2008
  • Studying the History of Ideas Using Topic Models Hall, D., Jurafsky, D., Manning, Christopher, D. 2008
  • A Structured Vector Space Model for Word Meaning in Context Erk, K., Padó, S. 2008
  • Efficient, Feature-based, Conditional Random Field Parsing. Finkel, J. R., Kleeman, A., Manning, Christopher, D. 2008
  • Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. Manning, Christopher, D., Jurafsky, D., Martin, James, H. Prentice-Hall. 2008
  • Constructing Integrated Corpus and Lexicon Models for Multi-Layer Annotation in OWL DL Linguistic Issues in Language Technologies Burchardt, A., Padó, S., Spohr, D., Frank, A., Heid, U. 2008; 1: 1-33
  • Comparing and Combining Semantic Verb Classifications. Journal of Language Resources and Evaluation Čulo, O., Erk, K., Padó, S., im Walde, S. S. 2008; 3 (42)
  • ntroduction to Information Retrieval. Cambridge: Cambridge University Press. Manning, Christopher, D., Raghavan, P., Schütze, H. 2008
  • A phrase-based alignment model for natural language inference MacCartney, B., Galley, M., Manning, Christopher, D. 2008
  • A Simple and Effective Hierarchical Phrase Reordering Model Galley, M., Manning, Christopher, D. 2008
  • Enforcing Transitivity in Coreference Resolution Finkel, J. R., Manning, Christopher, D. 2008
  • Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks Snow, R., O'Connor, B., Jurafsky, D., Ng, Andrew, Y. 2008
  • Unsupervised Learning of Narrative Event Chains Chambers, N., Jurafsky, D. 2008
  • A Simple and Effective Hierarchical Phrase Reordering Model Galley, M., Manning, Christopher, D. 2008
  • Optimizing Chinese Word Segmentation for Machine Translation Performance Chang, P., Galley, M., Manning, Christopher, D. 2008
  • Enforcing Transitivity in Coreference Resolution. Finkel, J. R., Manning, Christopher, D. 2008
  • Which Words Are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors that Increase ASR Error Rates Goldwater, S., Jurafsky, D., Manning, Christopher, D. 2008
  • Efficient, Feature-based, Conditional Random Field Parsing Finkel, J. R., Kleeman, A., Manning, Christopher, D. 2008
  • Regularization, adaptation, and non-independent features improve Hidden Conditional Random Fields for phone classification IEEE Workshop on Automatic Speech Recognition and Understanding Sung, Y., Boulis, C., Manning, C., Jurafsky, D. IEEE. 2007: 347–352
  • Modelling Prominence and Emphasis Improves Unit-Selection Synthesis Strom, V., Nenkova, A., Clark, R., Vazquez-Alvarez, Y., Brenier, J., King, S. 2007
  • Aligning semantic graphs for textual inference and machine reading. Marneffe, M. d., Grenager, T., MacCartney, B., Cer, D., Ramage, D., Kiddon, C., Manning, Christopher, D. 2007
  • Measuring Importance and Query Relevance in Topic-focused Multi-document Summarization Gupta, S., Nenkova, A., Jurafsky, D. 2007
  • Classifying Temporal Relations Between Events Chambers, N., Wang, S., Jurafsky, D. 2007
  • Learning Alignments and Leveraging Natural Logic Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., Manning, Christopher, D. 2007
  • Aligning semantic graphs for textual inference and machine reading Marneffe, M. d., Grenager, T., MacCartney, B., Cer, D., Ramage, D., Kiddon, C., Manning, Christopher, D. 2007
  • The Infinite Tree Finkel, J. R., Grenager, T., Manning, Christopher, D. 2007
  • Learning to Merge Word Senses Snow, R., Prakash, S., Jurafsky, D., Ng, Andrew, Y. 2007
  • The Effect of Lexical Frequency on Tone Production Zhao, Y., Jurafsky, D. 2007
  • Lexical Semantic Relatedness with Random Graph Walks Hughes, T., Ramage, D. 2007
  • Disambiguating Between Generic and Referential “You” in Dialog Gupta, S., Purver, M., Jurafsky, D. 2007
  • Natural logic for textual inference MacCartney, B., Manning, Christopher, D. 2007
  • A fully Bayesian approach to unsupervised part-of-speech tagging. Goldwater, S., Griffiths, Thomas, L. 2007
  • A Discriminative Syntactic Word Order Model for Machine Translation Chang, P., Toutanova, K. 2007
  • Learning Alignments and Leveraging Natural Logic Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., Manning, Christopher, D. 2007
  • Natural logic for textual inference. MacCartney, B., Manning, Christopher, D. 2007
  • Probabilistic models of language processing and acquisition Workshop on Probabilistic Models of Cognition - The Mathematics of Mind Chater, N., Manning, C. D. ELSEVIER SCIENCE LONDON. 2006: 335–44

    Abstract

    Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.

    View details for DOI 10.1016/j.tics.2006.05.006

    View details for Web of Science ID 000239648200008

    View details for PubMedID 16784883

  • An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition 21st International Conference on Computational Linguistics/44th Annual Meeting of the Association for Computational Linguistics Krishnan, V., Manning, C. D. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2006: 1121–1128
  • Graphical model representations of word lattices Ji, G., Bilmes, J., Michels, J., Kirchhoff, K., Manning, C., IEEE IEEE. 2006: 162-+
  • Detection of Word Fragments in Mandarin Telephone Conversation Chu, C., Sung, Y., Zhao, Y., Jurafsky, D. 2006
  • Learning to distinguish valid textual entailments Marneffe, M. d., MacCartney, B., Grenager, T., Cer, D., Rafferty, A., Manning, Christopher, D. 2006
  • Automatically Detecting Action Items in Audio Meeting Recordings Morgan, W., Chang, P., Gupta, S., Brenier, Jason, M. 2006
  • Unsupervised Discovery of a Statistical Verb Lexicon. Grenager, T., Manning, Christopher, D. 2006
  • Tregex and Tsurgeon: tools for querying and manipulating tree data structures. Levy, R., Andrew, G. 2006
  • Generating Typed Dependency Parses from Phrase Structure Parses Marneffe, M. d., MacCartney, B., Manning, Christopher, D. 2006
  • Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines Finkel, J. R., Manning, Christopher, D., Ng, Andrew, Y. 2006
  • The (Non)Utility of Linguistic Features for Predicting Prominence in Spontaneous Speech Brenier, Jason, M., Nenkova, A., Kothari, A., Whitton, L., Beaver, D., Jurafsky, D. 2006
  • Learning to recognize features of valid textual entailments. MacCartney, B., Grenager, T., Marneffe, M. d., Cer, D., Manning, Christopher, D. 2006
  • Learning to distinguish valid textual entailments. Marneffe, M. d., MacCartney, B., Grenager, T., Cer, D., Rafferty, A., Manning, Christopher, D. 2006
  • olving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines. Finkel, J. R., Manning, Christopher, D., Ng, Andrew, Y. 2006
  • Graphical Model Representations of Word Lattices Ji, G., Bilmes, J., Michels, J., Kirchhoff, K., Manning, C. 2006
  • Ergativity Encyclopedia of Language & Linguistics, Second Edition Manning, Christopher, D. edited by Keith Brown, Ergativity., In Oxford: Elsevier. 2006: 210–217
  • Learning to recognize features of valid textual entailments MacCartney, B., Grenager, T., Marneffe, M. d., Cer, D., Manning, Christopher, D. 2006
  • Semantic Taxonomy Induction from Heterogenous Evidence Snow, R., Jurafsky, D., Ng, Andrew, Y. 2006
  • Local Textual Inference: It's hard to circumscribe, but you know it when you see it - and NLP needs it MS, Stanford University Manning, Christopher, D. 2006
  • Generating Typed Dependency Parses from Phrase Structure Parses Marneffe, M. d., MacCartney, B., Manning, Christopher, D. 2006
  • Unsupervised Discovery of a Statistical Verb Lexicon Grenager, T., Manning, Christopher, D. 2006
  • Programming for linguists: Java (TM) technology for language researchers. (Book Review) LANGUAGE Book Review Authored by: Manning, C. 2005; 81 (3): 740-742
  • Natural language grammar induction with a generative constituent-context model 40th Annual Meeting of the Association-for-Computational-Linguistics Klein, D., Manning, C. D. ELSEVIER SCI LTD. 2005: 1407–19
  • Exploring the boundaries: gene and protein identification in biomedical text BMC BIOINFORMATICS Finkel, J., Dingare, S., Manning, C. D., Nissim, M., Alex, B., Grover, C. 2005; 6

    Abstract

    Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.

    View details for DOI 10.1186/1471-2105-6-S1-S5

    View details for Web of Science ID 000236061400005

    View details for PubMedID 15960839

    View details for PubMedCentralID PMC1869019

  • A system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations ISMB BioLink 2004 Meeting Dingare, S., Nissim, M., Finkel, J., Manning, C., Grover, C. HINDAWI PUBLISHING CORPORATION. 2005: 77–85

    Abstract

    We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal.

    View details for DOI 10.1002/cfg.457

    View details for Web of Science ID 000227860600007

    View details for PubMedID 18629295

    View details for PubMedCentralID PMC2448599

  • Robust Textual Inference using Diverse Knowledge Sources Raina, R., Haghighi, A., Cox, C., Finkel, J., Michels, J., Toutanova, K., Manning, Christopher, D. 2005
  • Incorporating non-local information into information extraction systems by Gibbs sampling. Finkel, J., Grenager, T., Manning, Christopher, D. 2005
  • Accent Detection and Speech Recognition for Shanghai-Accented Mandarin Zheng, Y., Sproat, R., Gu, L., Shafran, I., Zhou, H., Su, Y. 2005
  • A Joint Model for Semantic Role Labeling Haghighi, A., Toutanova, K., Manning, Christopher, D. 2005
  • A Conditional Random Field Word Segmenter Tseng, H., Chang, P., Andrew, G., Jurafsky, D., Manning, C. 2005
  • Stochastic HPSG Parse Disambiguation using the Redwoods Corpus. Research in Language and Computation Toutanova, K., Manning, Christopher, D., Flickinger, D., Oepen, S. 2005
  • LinGO Redwoods: A Rich and Dynamic Treebank for HPSG Research in Language and Computation Oepen, S., Flickinger, D., Toutanova, K., Manning, Christopher, D. 2005
  • Robust Textual Inference via Graph Matching HLT-EMNLP Haghighi, A., Ng, Andrew, Y., Manning, Christopher, D. 2005: 387-394
  • Pitch Accent Prediction: Effects of Genre and Speaker Yuan, J., Brenier, Jason, M., Jurafsky, D. 2005
  • The Detection of Emphatic Words Using Acoustic and Lexical Features Brenier, Jason, M., Cer, D., Jurafsky, D. 2005
  • Unsupervised learning of field segmentation models for information extraction Grenager, T., Klein, D., Manning, Christopher, D. 2005
  • Stochastic HPSG Parse Disambiguation using the Redwoods Corpus Toutanova, K., Manning, Christopher, D., Flickinger, D., Oepen, S. 2005
  • Morphological features help POS tagging of unknown words across language varieties Tseng, H., Jurafsky, D., Manning, C. 2005
  • Unsupervised Learning of Field Segmentation Models for Information Extraction Grenager, T., Klein, D., Manning, Christopher, D. 2005
  • Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Finkel, J. R., Grenager, T., Manning, C. 2005
  • Template Sampling for Leveraging Domain Knowledge in Information Extraction Cox, C., Nicolson, J., Finkel, J. R., Manning, C., Langley, P. 2005
  • A preliminary study of Mandarin filled pauses. Zhao, Y., Jurafsky, D. 2005
  • Robust textual inference via learning and abductive reasoning. Raina, R., Ng, Andrew, Y., Manning, C. 2005
  • Joint learning imrpoves semantic role labeling Toutanova, K., Haghighi, A., Manning, Christopher, D. 2005
  • Semantic Role Labeling Using Different Syntactic Views Pradhan, S., Ward, W., Hacioglu, K., Martin, J., Jurafsky, D. 2005
  • A Conditional Random Field Word Segmenter Tseng, H., Chang, P., Andrew, G., Jurafsky, D., Manning, C. 2005
  • Joint Learning Improves Semantic Role Labeling Toutanova, K., Haghighi, A., Manning, Christopher, D. 2005
  • Learning syntactic patterns for automatic hypernym discovery Snow, R., Jurafsky, D., Ng, A. 2005
  • A Rich and Dynamic Treebank for HPSG Oepen, S., Flickinger, D., Toutanova, K., Manning, Christopher, D. 2005
  • How useful and usable are dictionaries for speakers of Australian indigenous languages? INTERNATIONAL JOURNAL OF LEXICOGRAPHY Corris, M., Manning, C., Poetsch, S., Simpson, J. 2004; 17 (1): 33-68
  • Using feature conjunctions across examples for learning pairwise classifiers 15th European Conference on Machine Learning/8th European Conference on Principles and Practice of Knowledge Discovery in Databases Oyama, S., Manning, C. D. SPRINGER-VERLAG BERLIN. 2004: 322–333
  • Log-linear models for label ranking Dekel, O., Manning, C. D., Singer, Y., Thrun, S., Saul, K., Scholkopf, B. MIT PRESS. 2004: 497-504
  • Solving logic puzzles: from robust processing to precise semantics. Lev, I., MacCartney, B., Manning, Christopher, D., Levy, R. 2004
  • Deep dependencies from context-free statistical parsers: correcting the surface dependency approximation. Levy, R., Manning, Christopher, D. 2004
  • A System For Identifying Named Entities in Biomedical Text: How Results From Two Evaluations Reflect on Both the System and the Evaluations Dingare, S., Finkel, J., Nissim, M., Manning, C., Grove, C. 2004
  • Exploiting Context for Biomedical Entity Recognition: From Syntax to the Web Finkel, J., Dingare, S., Nguyen, H., Nissim, M., Manning, C., Sinclair, G. 2004
  • Shallow semantic parsing using support vector machines. Pradhan, S., Ward, W., Hacioglu, K., Martin, J., Jurafsky, D. 2004
  • Exploiting Context for Biomedical Entity Recognition: From Syntax to the Web Finkel, J., Dingare, S., Nguyen, H., Nissim, M., Manning, C., Sinclair, G. 2004
  • Deep dependencies from context-free statistical parsers: correcting the surface dependency approximation Levy, R., Manning, Christopher, D. 2004
  • Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency Klein, D., Manning, Christopher, D. 2004
  • Solving Logic Puzzles: From Robust Processing to Precise Semantics Lev, I., MacCartney, B., Manning, Christopher, D., Levy, R. 2004
  • Max-Margin Parsing Taskar, B., Klein, D., Collins, M., Koller, D., Manning, C. 2004
  • Log-Linear Models for Label Ranking Advances in Neural Information Processing Systems 16 (NIPS 2003). Dekel, O., Manning, Christopher, D., Singer, Y. edited by Thrun, S., Saul, Lawrence, K., Schölkopf, B. Cambridge, MA: MIT Press. 2004: 497–504
  • Corpus-Based Induction of Syntactic Structure: Models of Constituency and Dependency Language Learning: An Interdisciplinary Perspective. Klein, D., Manning, Christopher, D. edited by Cohen, P., Clark, A., Hovy, E. AAAI Spring Symposium. 2004: 32–38
  • Exploring Sentiment Summarization Exploring Attitude and Affect in Text: Theories and Applications Beineke, P., Hastie, T., Manning, C., Vaithyanathan, S. edited by Qu, I. Y., Shanahan, J., Wiebe, J. AAAI Spring Symposium Technical Report SS-04-07. 2004: 12–15
  • Automatic extraction of option propositions and their holders Bethard, S., Yu, H., Thornton, A., Hativassiloglou, V., Jurafsky, D. 2004
  • Parsing arguments of nominalizations in English and Chinese. Pradhan, S., Sun, H., Ward, W., Martin, J., Jurafsky, D. 2004
  • Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks Andrew, G., Grenager, T., Manning, C. 2004
  • Parsing and Hypergraphs. New Developments in Parsing Technology. Klein, D., Manning, Christopher, D. edited by Bunt, H., Carroll, J., Satta, G. Dordrecht: Kluwer Academic Publishers. 2004: 351–372
  • Automatic tagging of arabic text: from raw text to base phrase chunks. Diab, M., Hacioglu, K., Jurafsky, D. 2004
  • Log-Linear Models for Label Ranking Advances in Neural Information Processing Systems 16 (NIPS 2003) Dekel, O., Manning, Christopher, D., Singer, Y. edited by Thrun, S., Saul, Lawrence, K., Schölkopf, B. Cambridge. 2004: 497–504
  • Exploring the Boundaries: Gene and Protein Identification in Biomedical Text Dingare, S., Finkel, J., Manning, C., Nissim, M., Alex, B. 2004
  • Learning Random Walk Models for Inducing Word Dependency Distributions Toutanova, K., Manning, Christopher, D., Ng, Andrew, Y. 2004
  • The Leaf Projection Path View of Parse Trees: Exploring String Kernels for HPSG Parse Selection Toutanova, K., Markova, P., Manning, C. 2004
  • Parsing and hypergraphs 7th International Workshop on Parsing Technology Klein, D., Manning, C. D. SPRINGER. 2004: 351–372
  • Is it harder to parse Chinese, or the Chinese treebank? 41st Annual Meeting of the Association-for-Computational-Linguistics Levy, R., Manning, C. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2003: 439–446
  • Spectral Learning Kamvar, Sepandar, D., Klein, D., Manning, Christopher, D. 2003
  • The EigenTrust Algorithm for Reputation Management in P2P Networks Kamvar, Sepandar, D., Schlosser, Mario, T., Garcia-Molina, H. 2003
  • Addressing the Non-Cooperation Problem in Competitive P2P Networks Yang, B., Kamvar, Sepandar, D., Garcia-Molina, H. 2003
  • Incentives for Combatting Freeriding on P2P Networks Kamvar, Sepandar, D., Schlosser, Mario, T., Garcia-Molina, H. 2003
  • Adaptive Methods for the Computation of PageRank In Linear Algebra and its Applications, Special Issue on the Numerical Solution of Markov Chains Kamvar, Sepandar, D., Haveliwala, Taher, H., Golub, Gene, H. 2003
  • The Second Eigenvalue of the Google Matrix Stanford University Technical Report, March Haveliwala, Taher, H., Kamvar, Sepandar, D. 2003
  • Named Entity Recognition with Character-Level Models Klein, D., Smarr, J., Nguyen, H., Manning, Christopher, D. 2003
  • Fast Exact Inference with a Factored Model for Natural Language Parsing Advances in Neural Information Processing Systems 15 (NIPS 2002) Klein, D., Manning, Christopher, D. edited by Becker, S., Thrun, S., Obermayer, K. Cambridge, MA: MIT Press. 2003: 3–10
  • An Analytical Comparison of Approaches to Personalizing PageRank Stanford University Technical Report, June Haveliwala, Taher, H., Kamvar, Sepandar, D., Jeh, G. 2003
  • Parse Selection on the Redwoods Corpus: 3rd Growth Results. Technical report dbpubs 2003-64, Stanford University. Toutanova, K., Manning, Christopher, D., Oepen, S., Flickinger, D. 2003
  • Extrapolation Methods for Accelerating PageRank Computations Kamvar, Sepandar, D., Haveliwala, Taher, H., Manning, Christopher, D., Golub, Gene, H. 2003
  • Extrapolation Methods for Accelerating PageRank Computations. Kamvar, Sepandar, D., Haveliwala, Taher, H., Manning, Christopher, D., Golub, Gene, H. 2003
  • Factored A* Search for Models over Sequences and Trees Klein, D., Manning, Christopher, D. 2003
  • Computing PageRank using Power Extrapolation Stanford University Technical Report dbpubs/2003-45 Haveliwala, T., Kamvar, S., Klein, D., Manning, C., Golub, G. 2003
  • Spectral Learning Kamvar, Sepandar, D., Klein, D., Manning, Christopher, D. 2003
  • Factored A* Search for Models over Sequences and Trees Klein, D., Manning, Christopher, D. 2003
  • Finding Educational Resources on the Web: Exploiting Automatic Extraction of Metadata Thompson, Cynthia, A., Smarr, J., Nguyen, H., Manning, C. 2003
  • Exploiting the Block Structure of the Web for Computing PageRank Stanford University Technical Report, June Kamvar, Sepandar, D., Haveliwala, Taher, H., Manning, Christopher, D., Golub, Gene, H. 2003
  • The Condition Number of the PageRank Problem Stanford University Technical Report, June Kamvar, Sepandar, D., Haveliwala, Taher, H. 2003
  • Exploiting the Block Structure of theWeb for Computing PageRank tanford University Technical Report dbpubs/2003-17 Kamvar, Sepandar, D., Haveliwala, Taher, H., Manning, Christopher, D., Golub, Gene, H. 2003
  • A generative model for semantic role labeling 14th European Conference on Machine Learning Thompson, C. A., Levy, R., Manning, C. D. SPRINGER-VERLAG BERLIN. 2003: 397–408
  • Accurate unlexicalized parsing 41st Annual Meeting of the Association-for-Computational-Linguistics Klein, D., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2003: 423–430
  • Probabilistic syntax Symposium on Probability Theory in Linguistics held at the Linguistic-Society-of-America Meeting Manning, C. D. M I T PRESS. 2003: 289–341
  • A* parsing: Fast extract viterbi parse selection Human Language Technology Conference Klein, D., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2003: 119–126
  • Feature-rich part-of-speech tagging with a cyclic dependency network Human Language Technology Conference Toutanova, K., Klein, D., Manning, C. D., Singer, Y. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2003: 252–259
  • Optimizing local probability models for statistical parsing 14th European Conference on Machine Learning Toutanova, K., Mitchell, M., Manning, C. D. SPRINGER-VERLAG BERLIN. 2003: 409–420
  • Statistical approaches to natural language processing Encyclopedia of Cognitive Science. Manning, Christopher, D. edited by Nadel, L. London: Nature Publishing Group. 2003: 1
  • Beyond grammar: An experiences theory of language (Book Review) JOURNAL OF LINGUISTICS Book Review Authored by: Manning, C. D. 2002; 38 (2): 441-442
  • Extensions to HMM-based statistical word alignment models Conference on Empirical Methods in Natural Language Processing Toutanova, K., Ilhan, H. T., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2002: 87–94
  • Parse Disambiguation for a Rich HPSG Grammar. Toutanova, K., Manning, Christopher, D., Shieber, Stuart, M., Flickinger, D., Oepen, S. 2002
  • From Instance-level Constraints to Space-level Constraints: Making the Most of Prior Knowledge in Data Clustering Klein, D., Kamvar, Sepandar, D., Manning, Christopher, D. 2002
  • LinGO Redwoods. A Rich and Dynamic Treebank for HPSG Oepen, S., Flickinger, D., Toutanova, K., Manning, Christopher, D. 2002
  • Combining Heterogeneous Classifiers for Word-Sense Disambiguation Klein, D., Toutanova, K., Ilhan, H., Tolga, Kamvar, Sepandar, D., Manning, Christopher, D. 2002
  • Evaluating Strategies for Similarity Search on the Web Haveliwala, T., Gionis, A., Klein, D., Indyk, P. 2002
  • LinGO Redwoods: A Rich and Dynamic Treebank for HPSG Oepen, S., Callahan, E., Flickinger, D., Manning, Christopher, D., Toutanova, K. 2002
  • Simulating a File-Sharing P2P Network Schlosser, Mario, T., Condie, Tyson, E., Kamvar, Sepandar, D. 2002
  • Pronunciation Modeling for Improved Spelling Correction Toutanova, K., Moore, Robert, C. 2002
  • nterpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach Kamvar, Sepandar, D., Klein, D., Manning, Christopher, D. 2002
  • The LinGO Redwoods Treebank: Motivation and Preliminary Applications Oepen, S., Toutanova, K., Shieber, S., Manning, C., Flickinger, D., Brants, T. 2002
  • LinGO Redwoods: A Rich and Dynamic Treebank for HPSG Oepen, S., Callahan, E., Flickinger, D., Manning, Christopher, D., Toutanova, K. 2002
  • LinGO Redwoods. A Rich and Dynamic Treebank for HPSG. Oepen, S., Flickinger, D., Toutanova, K., Manning, Christopher, D. 2002
  • Parse Disambiguation for a Rich HPSG Grammar Toutanova, K., Manning, Christopher, D., Shieber, Stuart, M., Flickinger, D., Oepen, S. 2002
  • Natural Language Grammar Induction using a Constituent-Context Model. Advances in Neural Information Processing Systems 14 (NIPS 2001) Klein, D., Manning, Christopher, D. edited by Dietterich, Thomas, G., Becker, S., Ghahramani, Z. Cambridge, MA: MIT Press. 2002: 35–42
  • Review of Rens Bod, Beyond Grammar: An Experience-based Theory of Language. Journal of Linguistics Manning, C. 2002; 2 (38): 441-442
  • The LinGO Redwoods Treebank: Motivation and Preliminary Applications. Oepen, S., Toutanova, K., Shieber, S., Manning, C., Flickinger, D., Brants, T. 2002
  • Feature Selection for a Rich HPSG Grammar Using Decision Trees Toutanova, K., Manning, Christopher, D. 2002
  • Inducing Novel Gene-Drug Interactions from the Biomedical Literature Stanford University Technical Report, December Kamvar, Sepandar, D., Oliver, Diane, E., Manning, Christopher, D., Altman, Russ, B. 2002
  • Combining Heterogeneous Classifiers for Word-Sense Disambiguation. Klein, D., Toutanova, K., Ilhan, H., Tolga, Kamvar, Sepandar, D., Manning, Christopher, D. 2002
  • Dictionaries and Endangered Languages Language Endangerment and Language Maintenance. Corris, M., Manning, C., Poetsch, S. edited by Bradley, D., Bradley, M. London: RoutledgeCurzon. 2002: 329–347
  • A* Parsing: Fast Exact Viterbi Parse Selection. Stanford University Technical Report dbpubs/2002-16 Klein, D., Manning, Christopher, D. 2002
  • Review of Rens Bod, Beyond Grammar: An Experience-based Theory of Language Journal of Linguistics Manning, C. 2002; 2 (38): 441-442
  • From Instance-level Constraints to Space-level Constraints: Making the Most of Prior Knowledge in Data Clustering. Klein, D., Kamvar, Sepandar, D., Manning, Christopher, D. 2002
  • Feature Selection for a Rich HPSG Grammar Using Decision Trees Toutanova, K., Manning, Christopher, D. 2002
  • Natural language grammar induction using a constituent-context model 15th Annual Conference on Neural Information Processing Systems (NIPS) Klein, D., Manning, C. D. M I T PRESS. 2002: 35–42
  • Conditional structure versus conditional estimation in NLP models Conference on Empirical Methods in Natural Language Processing Klein, D., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2002: 9–16
  • A generative constituent-context model for improved grammar induction 40th Annual Meeting of the Association-for-Computational-Linguistics Klein, D., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2002: 128–135
  • Parsing with treebank grammars: Empirical bounds, theoretical models, and the structure of the Penn Treebank 39th Annual Meeting of the Association-for-Computational-Linguistics Klein, D., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2001: 330–337
  • Parsing and Hypergraphs Klein, D., Manning, Christopher, D. 2001
  • Text Classification in a Hierarchical Mixture Model for Small Training Sets Toutanova, K., Chen, F., Popat, K., Hofmann, T. 2001
  • An O(n3) Agenda-Based Chart Parser for Arbitrary Probabilistic Context-Free Grammars Klein, D., Manning, Christopher, D. 2001
  • What's needed for lexical databases? Experiences with Kirrkirr Manning, C., Parton, K. 2001
  • Parsing and Hypergraphs Klein, D., Manning, Christopher, D. 2001
  • An O(n3) Agenda-Based Chart Parser for Arbitrary Probabilistic Context-Free Grammars Stanford Technical Report dbpubs/2001-16 Klein, D., Manning, Christopher, D. 2001
  • Kirrkirr: Software for browsing and visual exploration of a structured Warlpiri dictionary. Literary and Linguistic Computing, Manning, Christopher, D., Jansz, K., Indurkhya, N. 2001; 2 (16): 123-139
  • Soft Constraints Mirror Hard Constraints: Voice and Person in English and Lummi Bresnan, J., Dingare, S., Manning, Christopher, D. 2001
  • An Oncology Patient Interface to Medline Bernstam, E., Kamvar, Sepandar, D., Meric, F., Dugan, J., Chizek, S., Stave, C. 2001
  • Combining Heterogeneous Classifiers for Word-Sense Disambiguation Ilhan, H., Tolga, Kamvar, Sepandar, D., Klein, D., Manning, Christopher, D., Toutanova, K. 2001
  • What's needed for lexical databases? Experiences with Kirrkirr Manning, C., Parton, K. 2001
  • Distributional Phrase Structure Induction Klein, D., Manning, Christopher, D. 2001
  • Kirrkirr: Software for browsing and visual exploration of a structured Warlpiri dictionary Literary and Linguistic Computing Manning, Christopher, D., Jansz, K., Indurkhya, N. 2001; 2 (16): 135-151
  • Synovial tissue in rheumatoid arthritis is a source of osteoclast differentiation factor 4th International Synovitis Workshop Gravallese, E. M., Manning, C., Tsay, A., Naito, A., Pan, C., Amento, E., GOLDRING, S. R. WILEY-BLACKWELL. 2000: 250–58

    Abstract

    Osteoclast differentiation factor (ODF; also known as osteoprotegerin ligand, receptor activator of nuclear factor kappaB ligand, and tumor necrosis factor-related activation-induced cytokine) is a recently described cytokine known to be critical in inducing the differentiation of cells of the monocyte/macrophage lineage into osteoclasts. The role of osteoclasts in bone erosion in rheumatoid arthritis (RA) has been demonstrated, but the exact mechanisms involved in the formation and activation of osteoclasts in RA are not known. These studies address the potential role of ODF and the bone and marrow microenvironment in the pathogenesis of osteoclast-mediated bone erosion in RA.Tissue sections from the bone-pannus interface at sites of bone erosion were examined for the presence of osteoclast precursors by the colocalization of messenger RNA (mRNA) for tartrate-resistant acid phosphatase (TRAP) and cathepsin K in mononuclear cells. Reverse transcriptase-polymerase chain reaction (RT-PCR) was used to identify mRNA for ODF in synovial tissues, adherent synovial fibroblasts, and activated T lymphocytes derived from patients with RA.Multinucleated cells expressing both TRAP and cathepsin K mRNA were identified in bone resorption lacunae in areas of pannus invasion into bone in RA patients. In addition, mononuclear cells expressing both TRAP and cathepsin K mRNA (preosteoclasts) were identified in bone marrow in and adjacent to areas of pannus invasion in RA erosions. ODF mRNA was detected by RT-PCR in whole synovial tissues from patients with RA but not in normal synovial tissues. In addition, ODF mRNA was detected in cultured adherent synovial fibroblasts and in activated T lymphocytes derived from RA synovial tissue, which were expanded by exposure to anti-CD3.TRAP-positive, cathepsin K-positive osteoclast precursor cells are identified in areas of pannus invasion into bone in RA. ODF is expressed by both synovial fibroblasts and by activated T lymphocytes derived from synovial tissues from patients with RA. These synovial cells may contribute directly to the expansion of osteoclast precursors and to the formation and activation of osteoclasts at sites of bone erosion in RA.

    View details for Web of Science ID 000085362800003

    View details for PubMedID 10693863

  • What's related? Generalizing approaches to related articles in medicine Annual Symposium of the American-Medical-Informatics-Association Strasberg, H. R., Manning, C. D., Rindfleisch, T. C., Melmon, K. L. HANLEY & BELFUS INC. 2000: 838–842

    Abstract

    We did formative evaluations of several variations to the computation of related articles for non-bibliographic resources in the medical domain.A binary model and several variations of the vector space model were used to measure similarity between documents. Two corpora were studied, using a human expert as the gold standard.Variations in term weights and stopword choices made little difference to performance. Performance was worse when documents were characterized by title words alone or by MeSH terms extracted from document references.Further studies are needed to evaluate these methods in medical information retrieval systems.

    View details for Web of Science ID 000170207500171

    View details for PubMedID 11080002

  • Using XSL And XQL For Efficient Customised Access To Dictionary Information. Jansz, K., Sng, W. J., Indurkhya, N., Manning, C. 2000
  • Kirrkirr: Software for browsing and visual exploration of a structured Warlpiri dictionary Manning, Christopher, D., Jansz, K., Indurkhya, N. 2000
  • What's related? Generalizing approaches to related articles in medicine. Strasberg, Howard, R., Manning, Christopher, D., Rindfleisch, Thomas, C., Melmon, Kenneth, L. 2000
  • Probabilistic Parsing Using Left Corner Language Models Advances in Probabilistic and Other Parsing Technologies Manning, Christopher, D., Carpenter, B. edited by Bunt, H., Nijholt, A. Kluwer Academic Publishers. 2000: 105–124
  • What's related? Generalizing approaches to related articles in medicine HR, S., CD, M., TC, R., KL, M. 2000
  • Using XSL And XQL For Efficient Customised Access To Dictionary Information Jansz, K., Sng, W. J., Indurkhya, N., Manning, C. 2000
  • Medline IRaCS: An Information Retrieval and Clustering System for Genomic Knowledge Acquisition Kamvar, Sepandar, D., Giladi, E., Loring, J., Walker, M. 2000
  • Bilingual Dictionaries for Australian Languages: User studies on the place of paper and electronic dictionaries Corris, M., Manning, C., Poetsch, S., Simpson, J. 2000
  • Probabilistic Parsing Using Left Corner Language Models. Advances in Probabilistic and Other Parsing Technologies. Manning, Christopher, D., Carpenter, B. Kluwer Academic Publishers. 2000: 105–124
  • Kirrkirr: Software for browsing and visual exploration of a structured Warlpiri dictionary Paper presented at ALLC/ACH 2000. Revised version appears in Literary and Linguistic Computing Manning, Christopher, D., Jansz, K., Indurkhya, N. 2000; 1 (16): 123-139
  • Enriching the knowledge sources used in a maximum entropy part-of-speech tagger Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora held in Conjunction with the 38th Annual Meeting of the Association-for-Computational-Linguistics Toutanova, K., Manning, C. D. ASSOCIATION COMPUTATIONAL LINGUISTICS. 2000: 63–70
  • Cognition and Function in Language. Manning, Christopher, D. edited by Fox, Barbara, A., Jurafsky, D., Michaelis, Laura, A. Stanford, CA: CSLI Publications.. 1999
  • Dissociations between Argument Structure and Grammatical Relations Lexical And Constructional Aspects of Linguistic Explanation Manning, Christopher, D., Sag, Ivan, A. edited by Webelhuth, G., Koenig, J. P., Kathol, A. CSLI Publications. 1999: 63–78
  • Dictionaries and endangered languages Corris, M., Manning, C., Poetsch, S., Simpson, J. Paper presented at the Endangered Languages Workshop, La Trobe University, [ps, rtf; published version above, under 2002; earlier version presented at the 1999 Perth Congress of the Applied Linguistics Association of Australia]. 1999
  • Complex Predicates and Information Spreading in LFG Stanford, CA: CSLI Publications Andrews, Avery, D., Manning, Christopher, D. 1999
  • The Lexical Integrity of Japanese Causatives Studies in Contemporary Phrase Structure Grammar Manning, Christopher, D., Sag, Ivan, A., Iida, M. edited by Levine, Robert, D., Green, Georgia, M. Cambridge: Cambridge University Press. 1999: 39–79
  • The Lexical Integrity of Japanese Causatives. Studies in Contemporary Phrase Structure Grammar Manning, Christopher, D., Sag, Ivan, A., Iida, M. edited by Levine, Robert, D., Green, Georgia, M. Cambridge: Cambridge University Press. 1999: 39–79
  • Kirrkirr: Interactive Visualisation And Multimedia From A Structured Warlpiri Dictionary Jansz, K., Manning, Christopher, D., Indurkhya, N. 1999
  • Dictionaries and endangered languages Corris, M., Manning, C., Poetsch, S., Simpson, J. 1999
  • Dissociations between Argument Structure and Grammatical Relations. Lexical And Constructional Aspects of Linguistic Explanation Manning, Christopher, D., Sag, Ivan, A. CSLI Publications. 1999: 63–78
  • Rethinking text segmentation models: An information extraction case study. Technical report SULTRY-98-07-01, University of Sydney Manning, C. 1998
  • A dictionary database template for Australian Languages Baker, B., Manning, C. 1998
  • Argument Structure, Valence, and Binding. Nordic Journal of Linguistics Manning, Christopher, D., Sag, Ivan, A. 1998; 2 (21): 107-144
  • Voice and grammatical relations in Indonesian: A new perspective Arka, I. W., Manning, C. edited by Austin, Peter, K., Musgrave, S. 1998
  • Review of David Pesetsky Zero Syntax: Experiencers and Cascades. Language 73: Manning, Christopher, D. 1997: 608-611
  • Grammatical relations versus binding: On the distinctness of argument structure Manning, C., Corblin, F., Godard, D., Marandin, J. M. PETER LANG AG. 1997: 79–101
  • Probabilistic Parsing Using Left Corner Language Models Manning, Christopher, D., Carpente, B. 1997
  • Grammatical Relations versus Binding: On the Distinctness of Argument Structure. Empirical Issues in Formal Syntax and Semantics Manning, Christopher, D. edited by Corblin, F., Godard, D., Marandin, J., M. Bern: Peter Lang ISBN 3-906757-73-0. 1997: 1
  • Ergativity: Argument Structure and Grammatical Relations Manning, Christopher, D. Stanford, CA: CSLI Publications/Cambridge University Press Dissertations in Linguistics series. ISBN: 1575860368 (pbk), 1575860376 (hbk).. 1996
  • Argument structure as a locus for binding theory Manning, Christopher, D. 1996
  • Romance Complex Predicates: In defence of the right-branching structure Manning, Christopher, D. 1996
  • A Theory of Non-constituent Coordination based on Finite State Rules Maxwell III, John, T., Manning, Christopher, D. 1996
  • Ergativity: Argument Structure and Grammatical Relations Manning, Christopher, D. 1995
  • Dissociations between Argument Structure and Grammatical Relations Manning, Christopher, D., Sag, Ivan, A. 1995
  • Dissociating functor-argument structure from surface phrase structure: Manning, Christopher, D. 1995
  • Valency versus binding: On the distinctness of argument structure Manning, Christopher, D. 1995
  • Ergativity: Argument Structure and Grammatical Relations, PhD Thesis, Stanford The revised version has been published by CSLI Publications (see 1996), and this version is not available on the web. Manning, Christopher, D. 1994
  • The lexical integrity of Japanese causatives Iida, M., Manning, Christopher, D., O'Neill, P., Sag, Ivan, A. 1994
  • Information Spreading and Levels of Representation in LFG CSLI Technical Report CSLI-93-176, Stanford University, Stanford CA. Andrews, A., D., Manning, Christopher, D. 1993
  • Automatic acquisition of a large subcategorization dictionary from corpora Manning, Christopher, D. 1993
  • Analyzing the verbal noun: Internal and external constraints Japanese/Korean Linguistics 3, Stanford Manning, Christopher, D. edited by Choi, S. CA: Stanford Linguistics Association. 1993: 236–253
  • Romance is so complex Technical Report CSLI-92-168, Stanford University, Stanford CA. Manning, Christopher, D. 1992
  • Presents embedded under pasts ms., Stanford University Manning, Christopher, D. 1992
  • LFG within King's descriptive formalism ms. Stanford University, Stanford CA. Manning, Christopher, D. 1991
  • Lexical Conceptual Structure and Marathi ms. Stanford University, Stanford CA Manning, Christopher, D. 1991