Machine Reading

This Fall, 590a will be on "Machine Reading" - the process of extracting high-level knowledge from natural language text, for example from Web pages. Most people know about the work on machine reading which is happening here at UW CSE (e.g. KnowItAll, TextRunner, Kylin, and the work based on Markov Logic Networks), but the area is also heating up dramatically at other Universities and research labs. We'll spend the quarter reading the latest papers on the topic and brainstorming about new directions. Participants may be asked to help lead class discussion of papers or to talk about their ongoing research on the topic.

Mailing List

We will not use the cse590a mailing list. Instead, announcements about the seminar will go to uw-ai. If you do not already subscribe to uw-ai, then join by sending mail to uw-ai-request@cs.washington.edu, with the line "subscribe listname" in the body of the message. You are welcome to discuss the presentations on the mailing list.

Calendar

A (Partial) List of papers

• Reducing semantic drift in lexicon bootstrapping
• Tara McIntosh and James R. Curran Weighted Mutual Exclusion Bootstrapping for Domain Independent Lexicon and Template Acquisition.. In Proceedings of ALTA 2008.
• Tara McIntosh and James R. Curran Reducing Semantic Drift with Bagging and Distributional Similarity. In Proceedings of ACL-IJCNLP 2009.
• Tara McIntosh Unsupervised Discovery of Negative Categories in Lexicon Bootstrapping. . In Proceedings of EMNLP 2010.
• Distant Supervision for Relation Extraction
• Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of ACL-IJCNLP 2009.
• Limin Yao,Sebastian Riedel,Andrew McCallum Collective Cross-Document Relation Extraction Without Labelled Data. In Proceedings of EMNLP-2010.
• Sebastian Riedel, Limin Yao, Andrew McCallum Modeling Relations and Their Mentions without Labeled Text. In Proceedings of ECML-2010.
• Joint Models for Coreference
• Aria Haghighi, Dan Klein Coreference Resolution in a Modular, Entity-Centered Model. In Proceedings of HLT-NAACL 2010.
• Aria Haghighi, Dan Klein An Entity-Level Approach to Information Extraction. In Proceedings of ACL 2010.
• Aria Haghighi, Dan Klein Simple Coreference Resolution with Rich Syntactic and Semantic Features . In proceedings of EMNLP 2009.
• Joint Models: Named Entity and Relation Extraction
• Rohit Kate and Raymond Mooney Joint Entity and Relation Extraction using Card-Pyramid Parsing. In proceedings of COLING 2010.
• Dan Roth, Wen-tau Yih A linear programming formulation for global inference in natural language tasks. In proceedings of CoNLL 2004.
• Yejin Choi, Eric Breck, Claire Cardie Joint Extraction of Entities and Relations for Opinion Recognition. In proceedings of EMNLLP 2006.
• Other Joint Models
• Jenny Rose Finkel and Christopher D. Manning Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data. In proceedings of ACL 2010.
• Pascal Denis and Jason Baldridge Global joint models for coreference resolution and named entity classification. Procesamiento del Lenguaje Natural 42.
• Xiaofeng Yu, Wai Lam Bidirectional Integration of Pipeline Models. In proceedings of AAAI 2010.
• Benat Zapirain, Eneko Agirre, Lluis Marquez, and Mihai Surdeanu. Improving Semantic Role Classification with Selectional Preferences.. In Proceedings of NAACL-2010.
• Supervised Relation Extraction
• Yee Seng Chan and Dan RothExploiting Background Knowledge for Relation Extraction.. In proceedings of COLING 2010.
• Aron Culotta, Jeffrey Sorensen Dependency Tree Kernels for Relation Extraction. In proceedings of ACL 2004.
• Frank Reichartz, Hannes Korte, Gerhard Paass Semantic Relation Extraction With Kernels Over Typed Dependency Trees. In proceedings of KDD 2010.
• Ontology
• P. Clark, P. Harrison. Large-Scale Extraction and Use of Knowledge From Text. In: Proc Fifth Int Conf on Knowledge Capture (KCap'09), 2009
• Mathias Niepert, Christian Meilicke, and Heiner Stuckenschmidt A Probabilistic-Logical Framework for Ontology Matching. In proceedings of AAAI 2010.
• Xin Dong, Alon Y.Halevy Cong Yu Data integration with uncertainty. In proceedings of AAAI 2010.
• More expressive Semantic Models
• Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman. Inducing Probabilistic CCG Grammars from Logical Form with Higher-order Unification .ENMLP, 2010
• Grounded language learning
• Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, and Mark Steedman. Learning Semantic Correspondences with Less Supervision. ACL 2009