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Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
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In: DTIC (2013)
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Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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From Unstructured to Structured Information in Military Intelligence - Some Steps to Improve Information Fusion
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In: DTIC (2004)
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Spatial Language for Human-Robot Dialogs
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In: DTIC AND NTIS (2003)
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Automatic Predicate Argument Analysis of the Penn TreeBank
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In: DTIC (2001)
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Abstract:
One of the primary tasks of Information Extraction is recognizing all of the different guises in which a particular type of event can appear. For instance, a meeting between two dignitaries can be referred to as A meets B or A and B meet, or a meeting between A and B took place/was held/opened/convened/finished/dragged on or A had/presided over a meeting/conference with B There are several different lexical items that can be used to refer to the same type of event, and several different predicate argument patterns that can be used to specify the participants. Correctly identifying the type of the event and the roles of the participants is a critical factor in accurate information extraction. In this paper we refer to the specific subtask of participant role identification as predicate argument tagging. The type of syntactic and semantic information associated with verbs in Levin's Preliminary Classification of English verbs, [Levin,93] can be a useful resource for an automatic predicate argument tagging system. For instance, the meet class includes the following members, meet, consult, debate and visit, which can all be used to refer to the meeting event type described above. In addition, the following types of syntactic frames are associated with these verbs: A met/visited/debated/consulted B A met/visited/debated/consulted with B. A and B met/visited/debated/consulted (with each other). For the purposes of this paper we will only be considering sense distinctions based on different predicate argument structures. We begin by giving more information about the Levin classes and then describe the system that automatically labels the arguments in a predicate argument structure. We end by giving the results of evaluating this system versus human annotators performing the same task. ; Presented at the International Conference on Human Language Technology Research (1st), held in San Diego, CA on 18-21 Mar 2001. Pub. in the Proceedings of the International Conference on Human Language Technology Research (1st), p1-5, 2001. Sponsored in part by National Science Foundation grant NSF 9800658. The original document contains color images.
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Keyword:
*AUTOMATIC PREDICATE ARGUMENT ANALYSIS; *CLASSIFICATION; *HIERARCHIES; *INFORMATION RETRIEVAL; *LEXICOGRAPHY; ACCURACY; ALGORITHMS; AUTOMATA; Cybernetics; IDENTIFICATION; Information Science; Linguistics; MATCHING; PATTERNS; PENN TREEBANK TAGGING; PHRASE STRUCTURE GRAMMARS; SEMANTICS; SYMPOSIA; SYNTATIC FRAMES; SYNTAX; WORDS(LANGUAGE)
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460592
http://www.dtic.mil/docs/citations/ADA460592
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| 7 |
BBN: Description of the PLUM System as Used for MUC-6
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In: DTIC (1995)
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BBN: Description of the PLUM System as Used for MUC-5
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In: DTIC (1993)
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BBN's PLUM Probabilistic Language Understanding System
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In: DTIC (1993)
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Detection and Correction of Repairs in Human-Computer Dialog
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In: DTIC (1992)
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Tipster Shogun System (Joint GE-CMU): MUC-4 Test Results and Analysis
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In: DTIC (1992)
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BBN: Description of the PLUM System as Used for MUC-4
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In: DTIC (1992)
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BBN: Description of the PLUM System as Used for MUC-3
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In: DTIC (1991)
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| 15 |
The Rate of Progress in Natural Language Processing
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In: DTIC (1987)
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| 17 |
Transportability and Generality in a Natural-Language Interface System
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In: DTIC (1983)
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| 19 |
An Approach to Acquiring and Applying Knowledge
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In: DTIC (1980)
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