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USB memory sticks produced by Omnipress Inc. 2600 Anderson Street Madison, WI 53707 USA c○2010 The Association for Computational Linguistics Association for Computational Linguistics (ACL) 2<strong>09</strong> N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 acl@aclweb.org ii
Introduction It has been a long term vision of Artificial Intelligence to develop Learning by Reading systems that can capture knowledge from naturally occurring texts, convert it into a deep logical notation and perform some inferences/reasoning on them. Such systems directly build on relatively mature areas of research, including Information Extraction (for picking out relevant information from the text), Commonsense and AI Reasoning (for deriving inferences from the knowledge acquired), Bootstrapped Learning (for using the learned knowledge to expand the knowledge base) and Question Answering (for providing evaluation mechanisms for Learning by Reading systems). In Natural Language Processing, statistical learning techniques have provided new solutions and breakthroughs in various areas over the last decade. In Knowledge Representation and Reasoning, systems have achieved impressive performance and scale in far more complex problems than the past. Learning by Reading is a two-part process. One part deals with extracting interesting information from naturally occurring texts, and the other is to use this extracted knowledge to expand the knowledge base and consequently the system’s inference capabilities. Previous systems have chosen either a ”broad and shallow” or a ”narrow and deep” knowledge acquisition and reasoning strategy. These techniques are constrained by either their limited reasoning ability or their extreme domain dependence. The goal of this workshop is to draw together researchers to explore the nature and degree of integration possible between symbolic and statistical techniques for knowledge acquisition and reasoning. In particular, given these developments, what is the role of commonsense knowledge and reasoning in language understanding? What are the limitations of each style of processing, and how can they be overcome by complementary strengths of the other? What are appropriate evaluation metrics for Learning by Reading systems? iii
Page 1: NAACL HLT 2010 First International
Page 7: Table of Contents Machine Reading a
Page 10 and 11: Sunday, June 6, 2010 (continued) Se
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
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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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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
Page 70 and 71:
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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