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References Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann and Zachary Ives. 2007. DBpedia: A nucleus for a web of open data. In Proceedings of the 6th International Semantic Web Conference: 11–15. Ken Barker et al. 2007. Learning by reading: A prototype system, performance baseline and lessons learned, Proceedings of the 22nd National Conference on Artificial Intelligence, AAAI Press. K. Bollacker, R. Cook, and P. Tufts. 2007. Freebase: A Shared Database of Structured General Human Knowledge. Proceedings of the National Conference on Artificial Intelligence (Volume 2): 1962-1963. Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008. Open information extraction from the web. Communications of the ACM 51, 12 (December): 68-74. Takaaki Hasegawa, Satoshi Sekine, and Ralph Grishman. 2004. Discovering relations among named entities from large corpora. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics: 415-422. Jun’ichi Kazama and Kentaro Torisawa. 2007. Exploiting Wikipedia as external knowledge for named entity recognition. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning: 698–707. Paul McNamee and Hoa Trang Dang. 2009. Overview of the TAC 2009 knowledge base population track. In Proceedings of the 2009 Text Analysis Conference. National Institute of Standards and Technology, November. Rada Mihalcea and Andras Csomai. 2007. Wikify!: linking documents to encyclopedic knowledge. In Proceedings of the 16th ACM Conference on Information and Knowledge Management: 233–242. George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine Miller. 1990. WordNet: An on-line lexical database. International Journal of Lexicography, 3:235–244. 86 Dat P. T. Nguyen, Yutaka Matsuo, and Mitsuru Ishizuka. 2007. Subtree mining for relation extraction from Wikipedia. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics:125–128. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2008. Yago: A large ontology from Wikipedia and WordNet. Web Semantics, 6(3):203– 217. Zareen Syed, Tim Finin, Varish Mulwad and Anupam Joshi. 2010. Exploiting a Web of Semantic Data for Interpreting Tables, Proceedings of the Second Web Science Conference. Simone P. Ponzetto and Roberto Navigli. 2009. Largescale taxonomy mapping for restructuring and integrating Wikipedia. In Proceedings of the Twenty- First International Joint Conference on Artificial Intelligence: 2083–2088. Yusuke Shinyama and Satoshi Sekine. 2006. Pre-emptive information extraction using unrestricted relation discovery. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics:. Daniel S. Weld, Raphael Hoffmann, and Fei Wu. 2008. Using Wikipedia to bootstrap open information extrac-tion. SIGMOD Record, 37(4): 62–68. Fei Wu and Daniel S. Weld. 2008. Automatically refining the Wikipedia infobox ontology. In Proceedings of the 17th International World Wide Web Conference, pages 635–644. Wikipedia. 2008. Wikipedia, the free encyclopedia. Yulan Yan, Naoaki Okazaki, Yutaka Matsuo, Zhenglu Yang, and Mitsuru Ishizuka. 2009. Unsupervised relation extraction by mining Wikipedia texts using information from the web. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics: Volume 2: 1021–1029.
Machine Reading at the University of Washington Hoifung Poon, Janara Christensen, Pedro Domingos, Oren Etzioni, Raphael Hoffmann, Chloe Kiddon, Thomas Lin, Xiao Ling, Mausam, Alan Ritter, Stefan Schoenmackers, Stephen Soderland, Dan Weld, Fei Wu, Congle Zhang Department of Computer Science & Engineering University of Washington Seattle, WA 98195 {hoifung,janara,pedrod,etzioni,raphaelh,chloe,tlin,xiaoling,mausam, aritter,stef,soderland,weld,wufei,clzhang}@cs.washington.edu Abstract Machine reading is a long-standing goal of AI and NLP. In recent years, tremendous progress has been made in developing machine learning approaches for many of its subtasks such as parsing, information extraction, and question answering. However, existing end-to-end solutions typically require substantial amount of human efforts (e.g., labeled data and/or manual engineering), and are not well poised for Web-scale knowledge acquisition. In this paper, we propose a unifying approach for machine reading by bootstrapping from the easiest extractable knowledge and conquering the long tail via a self-supervised learning process. This self-supervision is powered by joint inference based on Markov logic, and is made scalable by leveraging hierarchical structures and coarse-to-fine inference. Researchers at the University of Washington have taken the first steps in this direction. Our existing work explores the wide spectrum of this vision and shows its promise. 1 Introduction Machine reading, or learning by reading, aims to extract knowledge automatically from unstructured text and apply the extracted knowledge to end tasks such as decision making and question answering. It has been a major goal of AI and NLP since their early days. With the advent of the Web, the billions of online text documents contain virtually unlimited amount of knowledge to extract, further increasing the importance and urgency of machine reading. In the past, there has been a lot of progress in automating many subtasks of machine reading by machine learning approaches (e.g., components in the traditional NLP pipeline such as POS tagging and syntactic parsing). However, end-to-end solutions are still rare, and existing systems typically require substantial amount of human effort in manual engineering and/or labeling examples. As a result, they often target restricted domains and only extract limited types of knowledge (e.g., a pre-specified relation). Moreover, many machine reading systems train their knowledge extractors once and do not leverage further learning opportunities such as additional text and interaction with end users. Ideally, a machine reading system should strive to satisfy the following desiderata: End-to-end: the system should input raw text, extract knowledge, and be able to answer questions and support other end tasks; High quality: the system should extract knowledge with high accuracy; Large-scale: the system should acquire knowledge at Web-scale and be open to arbitrary domains, genres, and languages; Maximally autonomous: the system should incur minimal human effort; Continuous learning from experience: the system should constantly integrate new information sources (e.g., new text documents) and learn from user questions and feedback (e.g., via performing end tasks) to continuously improve its performance. These desiderata raise many intriguing and challenging research questions. Machine reading research at the University of Washington has explored 87 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 87–95, Los Angeles, California, June 2010. c○2010 Association for Computational Linguistics
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NAACL HLT 2010 First International
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Introduction It has been a long ter
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Table of Contents Machine Reading a
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Sunday, June 6, 2010 (continued) Se
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"coherent", based on criteria such
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(SRL). While there are a number of
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epeat select a clause chain Cu of
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even if those words reflect somethi
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Building an end-to-end text reading
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found to be wrong or correct (by su
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Text Queue Parser 4 Summary Text Mi
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formalized procedure to attach elem
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Steve_Walsh:throw:pass Steve_Walsh:
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NVNPN 2 'person':'intercept':'pass'
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6 Related Work To build the knowled
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Large Scale Relation Detection ∗
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1000 900 800 700 600 500 400 300 20
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to run against a web-scale corpus a
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Relation Prec Rec F1 Tuples Seeds i
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of the pattern space for any given
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Mining Script-Like Structures from
Page 46 and 47: e1 [ enter, nsubj, {customer, John}
Page 48 and 49: To identify such pairs, the topic s
Page 50 and 51: ≈ 57% 2. More words (bold) were j
Page 52 and 53: References Sergey Brin and Lawrence
Page 54 and 55: strengthsandweaknessesofthesystemin
Page 56 and 57: Fine-grainedRSTparser CollapsedRSTp
Page 58 and 59: Inference accuracy 0.3 0.25 0.2 0.1
Page 60 and 61: wehaveintroducedinferenceprocedures
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Page 64 and 65: inference. Pruning involves using a
Page 66 and 67: TEXTRUNNER SRL-IE P R F1 P R F1 Bin
Page 68 and 69: that maximizes information gain div
Page 70 and 71: References Eugene Agichtein and Lui
Page 72 and 73: 1. Patterns based on words vs. pred
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Page 76 and 77: ence of an attack. For instance, th
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Page 80 and 81: Towards Learning Rules from Natural
Page 82 and 83: tions of the learner suit the obser
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Page 86 and 87: Accuracy Accuracy 100 95 90 85 80 7
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Page 90 and 91: to categorize them. The article on
Page 92 and 93: ized label) or the most specialized
Page 94 and 95: Similarity Function k (L=2) F (L=2)
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Page 102 and 103: TextRunner, Kylin, KOG, WOE, WPE).
Page 104 and 105: Doug Downey, Matthew Broadhead, and
Page 106 and 107: Analogical Dialogue Acts: Supportin
Page 108 and 109: Heat flows from one place to anothe
Page 110 and 111: Source Text Translation* QRG-CE Tex
Page 112 and 113: 1987). We simplified the syntax of
Page 114 and 115: Gentner, D. (1983). Structure-Mappi
Page 116 and 117: sources can help in tasks like name
Page 118 and 119: werset in the previous step and bas
Page 120 and 121: we were able to construct a list of
Page 122 and 123: found at threshold of 2. There were
Page 124 and 125: Supporting rule-based representatio
Page 126 and 127: the domain of the spatial argument
Page 128 and 129: the time of the movement. This link
Page 130 and 131: don’t seem to be plausible candid
Page 132 and 133: PRISMATIC: Inducing Knowledge from
Page 134 and 135: Figure 1: System Overview by a suit
Page 136 and 137: ferent dimensions. Continuing with
Page 139: Author Index Barbella, David, 96 Ba
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