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Sunday, June 6, 2010 (continued) Session 3 2:00–2:20 Semantic Role Labeling for Open Information Extraction Janara Christensen, Mausam, Stephen Soderland and Oren Etzioni 2:20–2:40 Empirical Studies in Learning to Read Marjorie Freedman, Edward Loper, Elizabeth Boschee and Ralph Weischedel 2:40–3:00 Learning Rules from Incomplete Examples: A Pragmatic Approach Janardhan Rao Doppa, Mohammad NasrEsfahani, Mohammad Sorower, Thomas G. Dietterich, Xiaoli Fern and Prasad Tadepalli 3:00–3:30 Break 3:30–4:30 Poster Session 4:30–5:00 Discussion Unsupervised techniques for discovering ontology elements from Wikipedia article links Zareen Syed and Tim Finin 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 and Congle Zhang Analogical Dialogue Acts: Supporting Learning by Reading Analogies David Barbella and Kenneth Forbus A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing Zareen Syed and Evelyne Viegas Supporting rule-based representations with corpus-derived lexical information. Annie Zaenen, Cleo Condoravdi, Daniel Bobrow and Raphael Hoffmann PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource James Fan, David Ferrucci, David Gondek and Aditya Kalyanpur x
Machine Reading as a Process of Partial Question-Answering Peter Clark and Phil Harrison Boeing Research & Technology The Boeing Company, PO Box 3707, Seattle, WA 98124, USA {peter.e.clark,philip.harrison}@boeing.com Abstract This paper explores the close relationship between question answering and machine reading, and how the active use of reasoning to answer (and in the process, disambiguate) questions can also be applied to reading declarative texts, where a substantial proportion of the text’s contents is already known to (represented in) the system. In question answering, a question may be ambiguous, and it may only be in the process of trying to answer it that the "right" way to disambiguate it becomes apparent. Similarly in machine reading, a text may be ambiguous, and may require some process to relate it to what is already known. Our conjecture in this paper is that these two processes are similar, and that we can modify a question answering tool to help "read" new text that augments existing system knowledge. Specifically, interpreting a new text T can be recast as trying to answer, or partially answer, the question "Is it true that T?", resulting in both appropriate disambiguation and connection of T to existing knowledge. Some preliminary investigation suggests this might be useful for proposing knowledge base extensions, extracted from text, to a knowledge engineer. 1 Introduction Machine reading is not just a task of language processing, but an active interplay between knowledge and language; Prior knowledge should guide interpretation of new text, and new interpretations should augment that prior knowledge. Such interaction is essential if ambiguities in language are to be resolved "correctly" (with respect to what is known), and if the resulting interpretations are to be integrated with existing knowledge. The main insight of this paper is that this interaction is similar to that required for knowledge-based question answering, which also requires searching a knowledge base (KB) for a valid interpretation of the question. In our earlier work on question answering (Clark and Harrison, 2010), we found that some disambiguation decisions for question interpretation could be deferred, to be resolved during question answering, guided by what was found in the KB. In this paper, we show how a similar approach can be applied to interpreting declarative text, so that a similar interplay between language and knowledge is achieved. "Machine reading" itself is a loosely-defined notion, ranging from extracting selective facts to constructing complex, inference-supporting representations of text. One approach for selective extraction is the use of semantic templates ("scripts", "frames") to provide a set of roles (slots) and constraints on objects playing those roles (fillers) to be expected in text, and might be filled by methods ranging from simply skimming text, e.g., FRUMP (DeJong, 1979), to full language processing, e.g., (Dahlgren et al., 1991). Other work has looked at techniques for learning phrasal patterns likely to contain slot fillers (Riloff, 1996; Sekine, 2006) or contain information semantically similar to a set of seed examples (Carlson et al, 20<strong>09</strong>). At the other end of the spectrum, some systems attempt a full understanding of text, i.e., have the ambitious goal of building a complete representation of the text's contents (e.g., Zadrozny 1991, Hobbs et al, 1993). A common thread of these approaches is to search a space of alternative disambiguations and elaborations and select the most 1 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 1–9, Los Angeles, California, June 2010. c○2010 Association for Computational Linguistics
Page 1 and 2: NAACL HLT 2010 First International
Page 3: Introduction It has been a long ter
Page 7: Table of Contents Machine Reading a
Page 12 and 13: "coherent", based on criteria such
Page 14 and 15: (SRL). While there are a number of
Page 16 and 17: epeat select a clause chain Cu of
Page 18 and 19: even if those words reflect somethi
Page 20 and 21: Building an end-to-end text reading
Page 22 and 23: found to be wrong or correct (by su
Page 24 and 25: Text Queue Parser 4 Summary Text Mi
Page 26 and 27: formalized procedure to attach elem
Page 28 and 29: Steve_Walsh:throw:pass Steve_Walsh:
Page 30 and 31: NVNPN 2 'person':'intercept':'pass'
Page 32 and 33: 6 Related Work To build the knowled
Page 34 and 35: Large Scale Relation Detection ∗
Page 36 and 37: 1000 900 800 700 600 500 400 300 20
Page 38 and 39: to run against a web-scale corpus a
Page 40 and 41: Relation Prec Rec F1 Tuples Seeds i
Page 42 and 43: of the pattern space for any given
Page 44 and 45: 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
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wehaveintroducedinferenceprocedures
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Semantic Role Labeling for Open Inf
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inference. Pruning involves using a
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TEXTRUNNER SRL-IE P R F1 P R F1 Bin
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that maximizes information gain div
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References Eugene Agichtein and Lui
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1. Patterns based on words vs. pred
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state of the art information extrac
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ence of an attack. For instance, th
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manually annotated examples, which
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Towards Learning Rules from Natural
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tions of the learner suit the obser
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Accuracy Accuracy (Aggressive−Nov
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Accuracy Accuracy 100 95 90 85 80 7
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Unsupervised techniques for discove
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to categorize them. The article on
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ized label) or the most specialized
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Similarity Function k (L=2) F (L=2)
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References Sören Auer, Christian B
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a wide spectrum of solutions to the
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tion from the Web corpus at large s
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TextRunner, Kylin, KOG, WOE, WPE).
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Doug Downey, Matthew Broadhead, and
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Analogical Dialogue Acts: Supportin
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Heat flows from one place to anothe
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Source Text Translation* QRG-CE Tex
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1987). We simplified the syntax of
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Gentner, D. (1983). Structure-Mappi
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sources can help in tasks like name
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werset in the previous step and bas
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we were able to construct a list of
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found at threshold of 2. There were
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Supporting rule-based representatio
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the domain of the spatial argument
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the time of the movement. This link
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don’t seem to be plausible candid
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PRISMATIC: Inducing Knowledge from
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Figure 1: System Overview by a suit
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ferent dimensions. Continuing with
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Author Index Barbella, David, 96 Ba
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