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Controlled English for Effective Communication during Coalition Operations
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In: DTIC (2013)
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Enabling Efficient Intelligence Analysis in Degraded Environments
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In: DTIC (2013)
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An Approach Using MIP Products for the Development of the Coalition Battle Management Language Standard
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In: DTIC (2013)
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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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Entity List Completion Using Set Expansion Techniques
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In: DTIC (2011)
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Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
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In: DTIC (2011)
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Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
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In: DTIC (2009)
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Patent Retrieval in Chemistry based on Semantically Tagged Named Entities
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In: DTIC (2009)
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Applying A Formal Language of Command and Control For Interoperability Between Systems
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In: DTIC (2008)
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Odds of Successful Transfer of Low-level Concepts: A Key Metric for Bidirectional Speech-to-Speech Machine Translation in DARPA's TRANSTAC Program
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In: DTIC (2008)
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A Methodology to Predict Specific Communication Themes from Overall Communication Volume for Individuals and Teams
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In: DTIC (2006)
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Deep Versus Broad Methods for Automatic Extraction of Intelligence Information From Text
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In: DTIC (2005)
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Quality of Service-Driven Requirements Analyses for Component Composition: A Two-Level Grammar++ Approach
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In: DTIC (2005)
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Inferring Parts of Speech for Lexical Mappings via the Cyc KB
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In: DTIC (2004)
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Abstract:
We present an automatic approach to learning criteria for classifying the parts-of-speech used in lexical mappings. This will further automate our knowledge acquisition system for non-technical users. The criteria for the speech parts are based on the types of the denoted terms along with morphological and corpus-base clues. Associations among these and the parts-of-speech are learned using the lexical mappings contained in the Cyc knowledge base as training data. With over 30 speech parts to choose from, the classifier achieves good results (77.8% correct). Accurate results (93.0%) are achieved in the special case of the mass-count distinction for nouns. Comparable results are also obtained using OpenCyc (73.1% general and 88.4% mass-count). ; Presented at the International Conference on Computational Linguistics (20th), held in Geneva, Switzerland, on 23-27 2004. Published in the Proceedings of the International Conference on Computational Linguistics (20th), 2004. Prepared in cooperation with Cycorp, Inc., Austin, TX. Sponsored in part by National Institute of Standards and Technology (NIST), and by Department of Education grant no's. EIA-9810732 and EIA-0220590.
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Keyword:
*CLASSIFICATION; *LEARNING; *PARTS; *PARTS OF SPEECH; *SPEECH; COMPUTATIONAL LINGUISTICS; KNOWLEDGE BASED SYSTEMS; LEXICOGRAPHY; Linguistics; MAPPING; MORPHOLOGY; ONTOLOGY; SEMANTICS; SYMPOSIA
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URL: http://www.dtic.mil/docs/citations/ADA460959 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460959
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Analysis of Free-Form Battlefield Reports with Shallow Parsing Techniques
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In: DTIC AND NTIS (2004)
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