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Building an end-to-end text reading system based on a packed representation Doo Soon Kim Dept. of Computer Science University of Texas Austin, TX, 78712 onue5@cs.utexas.edu Abstract We previously proposed a packed graphical representation to succinctly represent a huge number of alternative semantic representations of a given sentence. We also showed that this representation could improve text interpretation accuracy considerably because the system could postpone resolving ambiguity until more evidence accumulates. This paper discusses our plan to build an end-to-end text reading system based on our packed representation. 1 Introduction Our goal is to build an end-to-end text understanding system by assembling together existing components for parsing, semantic interpretation, co-reference resolution and so on. Commonly, these components are combined in a pipeline in which each one passes forward a single best interpretation (see (a) in fig. 1). Although this approach is relatively straightforward, it can suffer from overly aggressive pruning; a component might prune those interpretations that downstream components might have been able to recognize as correct. Similarly, a component might prune an interpretation that would be validated by reading subsequent texts. The system’s accuracy would almost certainly improve if it were able to delay pruning until sufficient evidence accumulates to make a principled commitment. There is a naïve way of delaying pruning decisions in a pipelined architecture: each component passes forward not just a single interpretation, but Ken Barker Dept. of Computer Science University of Texas Austin, TX, 78712 kbarker@cs.utexas.edu Bruce Porter Dept. of Computer Science University of Texas Austin, TX, 78712 porter@cs.utexas.edu multiple alternatives, thereby creating multiple interpretation paths (see (b) in fig 1). Then, the system might choose the best interpretation at the last step of the pipeline. However, this approach is intractable due to the combinatorial explosion in the number of interpretation paths. In previous work (Kim et al., 2010), we proposed an alternative approach in which each component passes forward multiple interpretations which are compressed into an intensional representation that we call a packed graphical (PG) representation (see (c) in fig. 1). Our experiment showed that the approach could improve the interpretation accuracy considerably by delaying ambiguity resolution while avoiding combinatorial explosion. In this paper, we discuss our plan to build an endto-end text understanding system using the PG representation. We first introduce the language interpretation system we are currently building, which produces a PG representation from the parse of each sentence. Then, we propose an architecture for an end-to-end reading system that is based on the PG representation. The architecture allows the system to improve as it acquires knowledge from reading texts. In the following sections, we briefly describe the PG representation and its disambiguation algorithm (see (Kim et al., 2010) for details). Then, we present the plan and the current status in the development of an end-to-end text understanding system. 2 Packed graphical representation The PG representation compresses a huge number of alternative interpretations by locally represent- 10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 10–14, Los Angeles, California, June 2010. c○2010 Association for Computational Linguistics
Parser WSD SI (a) single interpretation, single component ¤¥¦¡§¨©§¢©¤¨©¤¥§ ¡¢£ Parser WSD WSD SI … WSD SI SI … (b) single interpretation, multiple components SI SI Parser WSD SI (c) single PG representation, single component Figure 1: The three different architectures for text understanding system: In (a), each component passes forward a single interpretation. (b) can improve (a) by considering multiple interpretation paths, but suffers from combinatorial explosion. (c) is our approach in which the system considers multiple alternative interpretations (in contrast to (a)) while avoiding combinatorial explosion by packing the alternatives (in contrast to (b)). ¡£¡§¨¨§¥¥§§¥ ing common types of ambiguities and other types of constraints among the interpretations. Section 2.1 presents these ambiguity and constraint representations. Section 2.2 introduces an algorithm which aims to resolve the ambiguities captured in a PG representation. 2.1 Representation Fig. 2 shows a PG representation produced from the interpretation of the following sentence: S1 : The engine ignites the gasoline with its spark plug. With this example, we will explain the ambiguity representations and the other types of constraints expressed in the PG representation. Type ambiguity. Ambiguity in the assignment of a type for a word. In PG1, for example, the node engine-2a (corresponding to the word “engine”) has type annotation [LIVING-ENTITY .3 | DEVICE .7]. It means that the two types are candidates for the type of engine-2a and their probabilities are respectively .3 (Living-Entity) and .7 (Device) . Relational ambiguity. Ambiguity in the assignment of semantic relation between nodes. The edge from ignite-3 to engine-2a in PG1 has relation annotation . It means that engine- 2a is either agent (probability .6) or location (probability .4) of ignite-3. Structural ambiguity. It represents structural alternatives in different interpretations. In PG1, for example, D and E represent an ambiguity of prepositional phrase attachment for “with its spark plug”; 11 Figure 2: The PG representation for S1 (PG1) the phrase can be attached to “ignites” (D) or “spark plug” (E). The annotation {D .3 | E .7} means either D or E (not both) is correct and the probability of each choice is respectively .3 and .7. Co-reference ambiguity. A “co-reference” edge represents a possibility of co-reference between two nodes. For example, the edge labeled represents that the probability of engine-2a and its- 7a being co-referent is .7. Besides ambiguity representations above, the PG representation can also represent dependencies among different interpretations. Simple dependency. It represents that the existence of one interpretation depends on the existence of another. For example, A → C means that if LIVING-ENTITY is found to be a wrong type for engine-2a (by subsequent evidence), the agent relation should be discarded, too. Mutual dependency. It represents that the interpretations in a mutual dependency set depend on one another – if any interpretation in the set is
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 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 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
Page 60 and 61: 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
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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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