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ferent dimensions. Continuing with the annexation example, we can use the V-OT frame cut to learn that a predominant object-type for the verb “annex” is also “Region”, by seeing lots of frames of the form [verb=“annex”, object-type=“Region”] in our data. To make frame-cuts more flexible, we allow them to specify optional value constraints for slots. For example, we can define an S-V-O frame cut, where both the subject (S) and object (O) slot values are constrained to be proper nouns, thereby creating strictly extensional frames, i.e. frames containing data about instances, e.g., [subject=“United States” verb=“annex” object=“Texas”]. The opposite effect is achieved by constraining S and O slot values to common nouns, creating intensional frames such as [subject=“Political-Entity” verb=“annex” object=“Region”]. The separation of extensional from intensional frame information is desirable, both from a knowledge understanding and an applications perspective, e.g. the former can be used to provide factual evidence in tasks such as question answering, while the latter can be used to learn entailment rules as seen in the annexation case. 7 Data The corpora we used to produce the initial PRIS- MATIC are based on a selected set of sources, such as the complete Wikipedia, New York Times archive and web page snippets that are on the topics listed in wikipedia. After cleaning and html detagging, there are a total of 30 GB of text. From these sources, we extracted approximately 1 billion frames, and from these frames, we produce the most commonly used cuts such as S-V-O, S-V-P-O and S-V-O-IO. 8 Potential Applications 8.1 Type Inference and Its Related Uses As noted in Section 6, we use frame-cuts to dissect frames along different slot dimensions, and then aggregate statistics for the resultant frames across the entire dataset, in order to induce relationships among the various frame slots, e.g., learn the predominant types for subject/object slots in verb and noun phrases. Given a new piece of text, we can apply this knowledge to infer types for named entities. For example, since the aggregate statistics shows the most common type for the object of 126 the verb “annex” is Region, we can infer from the sentence “Napoleon annexed Piedmont in 1859”, that “Piedmont” is most likely to be a Region. Similarly, consider the sentence: “He ordered a Napoleon at the restaurant”. A dictionary based NER is very likely to label “Napoleon” as a Person. However, we can learn from a large amount of data, that in the frame: [subject type=“Person” verb=“order” object type=[?] verb prep=“at” object prep=“restaurant”], the object type typically denotes a Dish, and thus correctly infer the type for “Napoleon” in this context. Learning this kind of fine-grained type information for a particular context is not possible using traditional hand-crafted resources like VerbNet or FrameNet. Unlike previous work in selectional restriction (Carroll and Mc- Carthy, 2000; Resnik, 1993), PRISMATIC based type inference does not dependent on a particular taxonomy or previously annotated training data: it works with any NER and its type system. The automatically induced-type information can also be used for co-reference resolution. For example, given the sentence: “Netherlands was ruled by the UTP party before Napolean annexed it”, we can use the inferred type constraint on “it” (Region) to resolve it to “Netherlands” (instead of the “UTP Party”). Finally, typing knowledge can be used for word sense disambiguation. In the sentence, “Tom Cruise is one of the biggest stars in American Cinema”, we can infer using our frame induced type knowledge base, that the word “stars” in this context refers to a Person/Actor type, and not the sense of “star” as an astronomical object. 8.2 Factual Evidence Frame data, especially extensional data involving named entities, captured over a large corpus can be used as factual evidence in tasks such as question answering. 8.3 Relation Extraction Traditional relation extraction approach (Zelenko et al., 2003; Bunescu and Mooney, 2005) relies on the correct identification of the types of the argument. For example, to identify “employs” relation between “John Doe” and “XYZ Corporation”, a relation extractor heavily relies on “John Doe” being annotated
as a “PERSON” and “XYZ Corporation” an “OR- GANIZATION” since the “employs” relation is defined between a “PERSON” and an “ORGANIZA- TION”. We envision PRISMATIC to be applied to relation extraction in two ways. First, as described in section 8.1, PRISMATIC can complement a named entity recognizer (NER) for type annotation. This is especially useful for the cases when NER fails. Second, since PRISMATIC has broad coverage of named entities, it can be used as a database to check to see if the given argument exist in related frame. For example, in order to determine if “employs” relation exists between “Jack Welch” and “GE” in a sentence, we can look up the SVO cut of PRISMATIC to see if we have any frame that has “Jack Welch” as the subject, “GE” as the object and “work” as the verb, or frame that has “Jack Welch” as the object, “GE” as the subject and “employs” as the verb. This information can be passed on as an feature along with other syntactic and semantic features to th relation extractor. 9 Conclusion and Future Work In this paper, we presented PRISMATIC, a large scale lexicalized relation resource that is built automatically over massive amount of text. It provides users with knowledge about predicates and their arguments. We have focused on building the infrastructure and gathering the data. Although we are still in the early stages of applying PRISMATIC, we believe it will be useful for a wide variety of AI applications as discussed in section 8, and will pursue them in the near future. References Stephen Soderland Alan Ritter and Oren Etzioni. 2009. What is this, anyway: Automatic hypernym discovery. In Proceedings of the 2009 AAAI Spring Symposium on Learning by Reading and Learning to Read. Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998. The berkeley framenet project. In Proceedings of the 17th international conference on Computational linguistics, pages 86–90, Morristown, NJ, USA. Association for Computational Linguistics. Michele Banko, Michael J Cafarella, Stephen Soderl, Matt Broadhead, and Oren Etzioni. 2007. Open 127 information extraction from the web. In In International Joint Conference on Artificial Intelligence, pages 2670–2676. Razvan C. Bunescu and Raymond J. Mooney. 2005. A shortest path dependency kernel for relation extraction. In HLT ’05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 724–731, Morristown, NJ, USA. Association for Computational Linguistics. John Carroll and Diana McCarthy. 2000. Word sense disambiguation using automatically acquired verbal preferences. Computers and the Humanities Senseval Special Issue, 34. Christiane Fellbaum, 1998. WordNet: An Electronic Lexical Database. Beth Levin, 1993. English Verb Classes and Alternations: A Preliminary Investigation. Dekang Lin and Patrick Pantel. 2001. Dirt - discovery of inference rules from text. In In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 323–328. Thomas Lin, Oren Etzioni, and James Fogarty. 2009. Identifying interesting assertions from the web. In CIKM ’09: Proceeding of the 18th ACM conference on Information and knowledge management, pages 1787– 1790, New York, NY, USA. ACM. Michael C. McCord. 1990. Slot grammar: A system for simpler construction of practical natural language grammars. In Proceedings of the International Symposium on Natural Language and Logic, pages 118–145, London, UK. Springer-Verlag. Philip Resnik. 1993. Selection and Information: A Class-Based Approach to Lexical Relationships. Ph.D. thesis, University of Pennsylvania. Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2003. Kernel methods for relation extraction. J. Mach. Learn. Res., 3:1083–1106.
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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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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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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 112 and 113: 1987). We simplified the syntax of
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 139: Author Index Barbella, David, 96 Ba
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