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1987). We simplified the syntax of the original texts into QRG-CE, using the appropriate surface forms for the analogy dialogue acts that we perceived in the text. One of the analogies is illustrated in Figure 1, with part of its translation is shown in Figure 3. Table 1 summarizes properties of the original texts and the simplification process. Original: Similarly, a full can of water will leak volume from a hole in the side of the can. QRG-CE: A hot brick brick005 is like a can can001 of water water001. There is a hole hole001 in can can001. The water water001 exits can can001 through hole hole001. Figure 3: Example of translation to QRG-CE. The specific individuals are added to factor out anaphora processing. Cues to analogical dialogue acts spread across multiple sentences in the original text are combined into single sentences during the translation process. To test the effectiveness of knowledge capture, 12 comprehension questions similar to those found in middle-school science texts were generated by independent readers of the texts (see Figure 4 for an example). All questions were designed to require understanding the analogy in order to answer them. Moreover, some of the questions require combining information from the knowledge base with knowledge gleaned from the text. Question: What disappears as the heat leaks from the brick? Predicate calculus version: (and (inputsDestroyed ?d ?ourAnswer) (after-Underspecified ?d ?leaving) (objectMoving ?leaving heat005) (isa ?heat ThermalEnergy) (isa ?leaving LeavingAPlace) (fromLocation ?leaving brick005)) Figure 4: A question for the analogy of Figure 1, in English and the hand-generated predicate calculus generated from it. Four experimental conditions were run, based on a 2x2 design here the factors were whether or not analogy was used (+A) or not used (-A), and whether what was learned from the text was augmented with information from the knowledge base (+K) or not (-K). Table 2 shows the results. The system was able to answer all twelve questions when it understood the analogy and combined what it learned by reading with information from the knowledge base. 102 Condition # correct % -A, -K 0 0 +A, -K 7 58 -A, +K 0 0 +A, +K 12 100 Table 2: Results for Q/A. +/- means with/without, A means analogy, K means facts retrieved from KB That this was due to understanding the analogy can be seen from the other conditions. The information from the text alone is insufficient to answer any of the questions (-A, -K), as is the information from the KB alone (-A, +K). Analogy by itself over what was learned by reading the passages can handle over half the questions (+A, -K), but the rest require combining facts learned by reading with facts from the KB (+A, +K). 6 Related Work There has been very little work on modeling analogies in dialogue. One of the few efforts has been Lulis & Evans (2003), who examined the use of analogies by human tutors for potential extensions to their intelligent tutoring system for cardiac function. Recently they have begun incorporating analogies into their tutor (Lulis, Evans, & Michael, 2004), but they have not focused on understanding novel analogies presented via language. Because EA NLU is designed to explore issues of understanding, it is focused more on semantic coverage than on syntactic coverage. The most similar system is Boeing’s BLUE (Clark & Harrison, 2008), which also uses simplified syntax and focuses on integrating language with a knowledge base and reasoning. Aside from SME, we suspect that the only other current widely tested model of analogy that might be able to handle this task is IAM (Keane & Brayshaw 1988). CAB (Larkey & Love 2003) does not model inference, and hence could not model this task. Although LISA (Hummel & Holyoak, 2003) can model some analogical inferences, the number of relations (see Table 3) in these analogies is beyond the number of relationships it can currently handle (2 or 3). The first simulation of analogy to use natural language input was Winston’s (1982, 1986), which used a simple domain-specific parser in modeling the learning of if-then rules and censors. EA NLU
enefits from subsequent progress in natural language research, enabling it to handle a wider range of phenomena. Example #S #BA #BR #TA #TR Gold mining/Collecting solar energy 8 26 32 4 4 Water flow/heat flow 11 14 21 13 16 depth of water in buck- 8 12 19 9 12 et/temperature of house Bucket with hole/house leaking heat 4 14 20 8 6 Bucket/Solar collector 5 13 15 4 4 7 12 19 11 14 Earth’s atmosphere/greenhouse Mean 7.2 15.2 21 8.2 9.3 Table 3: Statistics of base and target domains produced by EA NLU. #S = number of sentences, B/T = Base, Target; A/T = Attributes/Relations 7 Discussion and Future Work Modeling the roles that analogy plays in understanding language is an important problem in learning by reading. This paper is an initial exploration of how analogy can be integrated into dialogue act theories, focusing on instructional analogies in text. We presented a catalog of analogical dialogue acts, based on an analysis of how the functional constraints of analogical mapping and case construction interact with the properties of discourse. We showed that a simulation using these ideas, combined with a natural language understanding system to semi-automatically produce input representations, can indeed learn information from simplified English analogies, which is encouraging evidence for these ideas. The next step is to expand the corpus substantially, including more examples of all the ADAs, to better test our model. We also need to implement the rest of the ADAs, and experiment with a wider range of processing strategies. To better model how ADAs can be identified in natural texts, we plan to use a large-scale webbased corpus analysis. We have focused on text here, but we believe that these ideas apply to spoken dialogue as well. We predict more opportunities for blocking in spoken dialogue, due to opportunities for feedback. Our goal is to incorporate these ideas into a 2nd generation learning by reading system (e.g., Forbus et al 2007; Forbus et al 2009a), along with other dialogue processing, to better interpret larger-scale texts (e.g., Lockwood & Forbus, 2009). This will 103 be built using the Companions cognitive architecture (Forbus et al 2009b), to more easily model a wider range of processing strategies, and so that the system can learn to improve its interpretation processes. Acknowledgments This research was supported by the Intelligent and Autonomous Systems Program of the Office of Naval Research. References Allen, J.F. (1994). Natural Language Understanding. (2nd Ed.) Redwood City, CA: Benjamin/Cummings. Allen, J. F. & C. R. Perrault (1980). Analyzing Intention in Utterances. Artificial Intelligence 15(3). Buckley, S. (1979). From Sun Up to Sun Down. New York: McGraw-Hill. Clark, P. & Harrison, P. (2008). Boeing’s NLP system and the challenges of semantic representation Clark, P., Harrison, P., Jenkins, T., Thompson, J. & Wojcik, R. (2005). Acquiring and using world knowledge using a restricted subset of English. 18th International FLAIRS Conference. Falkenhainer, B., Forbus, K. & Gentner, D. (1989). The Structure-Mapping Engine: Algorithms and Examples. Artificial Intelligence, 41, 1-63. Forbus, K., Ferguson, R. & Gentner, D. (1994) Incremental structure-mapping. Proceedings of CogSci94. Forbus, K., Lockwood, K. & Sharma, A. (2009). Steps towards a 2nd generation learning by reading system. AAAI Spring Symposium on Learning by Reading, Spring 2009. Forbus, K., Klenk, M., & Hinrichs, T. , (2009). Companion Cognitive Systems: Design Goals and Lessons Learned So Far. IEEE Intelligent Systems, vol. 24, no. 4, pp. 36-46, July/August. Forbus, K., Mostek, T. & Ferguson, R. (2002). An analogy ontology for integrating analogical processing and first-principles reasoning. Proceedings of IAAI- 02, July. Forbus, K. Riesbeck, C., Birnbaum, L., Livingston, K., Sharma, A., & Ureel, L. (2007). Integrating natural language, knowledge representation and reasoning, and analogical processing to learn by reading. Proceedings of AAAI-07 Vancouver, BC.
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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
Page 62 and 63: Semantic Role Labeling for Open Inf
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
Page 74 and 75: state of the art information extrac
Page 76 and 77: ence of an attack. For instance, th
Page 78 and 79: manually annotated examples, which
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
Page 88 and 89: Unsupervised techniques for discove
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)
Page 96 and 97: References Sören Auer, Christian B
Page 98 and 99: a wide spectrum of solutions to the
Page 100 and 101: tion from the Web corpus at large s
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 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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