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Gentner, D. (1983). Structure-Mapping: A Theoretical Framework for Analogy. Cognitive Science, 7: 155- 170. Gentner, D., Bowdle, B., Wolff, P., & Boronat, C. (2001). Metaphor is like analogy. In Gentner, D., Holyoak, K., and Kokinov, B. (Eds.) The analogical mind: Perspective from cognitive science. pp. 199- 253, Cambridge, MA: MIT Press. Hummel, J. E., & Holyoak, K. J. (2003). A symbolicconnectionist theory of relational inference and generalization. Psychological Review, 110, 220-264. Kamp, H. & Reyle, U. (1993). From Discourse to Logic: Introduction to Model-theoretic Semantics of Natural Language. Kluwer Academic Dordrecht: Boston. Keane, M., and Brayshaw, M. (1988). The Incremental Analogy machine: A computational model of analogy. European Working Session on Learning. Larkey, L. & Love, B. (2003). CAB: Connectionist Analogy Builder. Cognitive Science 27,781-794. Lehr, P. E., Burnett, R. W., & Zim, H. S. (1987). Weather. New York, NY: Golden Books Publishing Company, Inc. Lockwood, K. & Forbus, K. 2009. Multimodal knowledge capture from text and diagrams. Proceedings of KCAP-2009. Lulis, E. & Evans, M. (2003). The use of analogies in human tutoring dialogues. AAAI Technical Report SS-03-06. Lulis, E., Evans, M. & Michael, J. (2004). Implementing analogies in an electronic tutoring system. In Lecture Notes in Computer Science, Vol 3220, pp. 228-231, Springer Berlin/Heidelberg. Macleod, C., Grisham, R., & Meyers, A. (1998). COMLEX Syntax Reference Manual, Version 3.0. Linguistic Data Consortium, University of Pennsylvania: Philadelphia, PA. Mostek, T., Forbus, K, & Meverden, C. (2000). Dynamic case creation and expansion for analogical reasoning. Proceedings of AAAI-2000. Austin, TX. Tomai, E. & Forbus, K. (2009). EA NLU: Practical Language Understanding for Cognitive Modeling. Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference. Sanibel Island, Florida. Traum, David R. (2000). 20 Questions on Dialogue Act Taxonomies. Journal of Semantics, 17, 7-30. Winston, P.H. 1982. Learning new principles from precedents and exercises. Artificial Intelligence 23(12). 104 Winston, P. 1986. Learning by augmenting rules and accumulating censors. In Michalski, R., Carbonell, J. and Mitchell, T. (Eds.) Machine Learning: An Artificial Intelligence Approach, Volume 2. Pp. 45-62. Morgan-Kaufman.
A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing Zareen Syed Evelyne Viegas University of Maryland Baltimore County Microsoft Research 1000 Hilltop Circle One Microsoft Way Baltimore, MD 21229, USA Redmond, WA 98052, USA Abstract This paper describes a hybrid approach for unsupervised and unrestricted relation discovery between entities using output from linguistic analysis and semantic typing information from a knowledge base. We use Factz (encoded as subject, predicate and object triples) produced by Powerset as a result of linguistic analysis. A particular relation may be expressed in a variety of ways in text and hence have multiple facts associated with it. We present an unsupervised approach for collapsing multiple facts which represent the same kind of semantic relation between entities. Then a label is selected for the relation based on the input facts and entropy based label ranking of context words. Finally, we demonstrate relation discovery between entities at different levels of abstraction by leveraging semantic typing information from a knowledge base. 1 Introduction There are a number of challenges involved when using facts extracted from text to enrich a knowledge base (KB) with semantic relations between entities: co-reference resolution as there are many co-referent objects; entity resolution in order to link the entities mentioned in text to the right entities in the KB; handling co-referent relations, as a particular semantic relation between entities can be expressed in a variety of ways in the text and therefore have multiple facts associated between the entities. In addition, the facts extracted from linguistic analysis are usually noisy and sparse. Our work focuses on a recent line of exploratory work in the direction of Unrestricted Relation Discovery which is defined as: the automatic identification of different relations in text without specifying a relation or set of relations in advance (Shinyama and Sekine, 2006). We use the facts which are the output of linguistic analysis from Powerset (www.Powerset.com). Powerset is an online search engine for querying Wikipedia using Natural Language Queries. Powerset performs a linguistic analysis of the sentences within Wikipedia and outputs facts in the form of subject, predicate and object triples which can be queried through the online interface. For most entities like persons, places and things, Powerset shows a summary of facts from across Wikipedia (figure 1). In our approach we use the readily available “Factz” from Powerset as input to our system. Powerset is Wikipedia independent and can run on any corpus with well-formed sentences and hence our approach is also not limited to Wikipedia. The Factz output from Powerset may represent relations between named entities or just nouns for example, Bank of America bank Bank of America Merrill Lynch Bank of America building Linguistic analysis has been recently described as an effective technique for relation extraction (Yan et al., 2009; Kambhatla, 2004; Nguyen et al., 2007). Following that trend, we incorporate Factz, that are the output of linguistic analysis done by Powerset, to discover semantic relations between entities. Information from existing knowledge re- Figure 1. Demonstration of Powerset Factz available online 105 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 105–113, 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
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e1 [ enter, nsubj, {customer, John}
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To identify such pairs, the topic s
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≈ 57% 2. More words (bold) were j
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References Sergey Brin and Lawrence
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strengthsandweaknessesofthesystemin
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Fine-grainedRSTparser CollapsedRSTp
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Inference accuracy 0.3 0.25 0.2 0.1
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wehaveintroducedinferenceprocedures
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Semantic Role Labeling for Open Inf
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Page 70 and 71: References Eugene Agichtein and Lui
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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
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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
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Page 118 and 119: werset in the previous step and bas
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Page 124 and 125: Supporting rule-based representatio
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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
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Page 139: Author Index Barbella, David, 96 Ba
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