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Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal
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In: DTIC (2014)
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| 2 |
Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses
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In: DTIC (2013)
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Making Semantic Information Work Effectively for Degraded Environments
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In: DTIC (2013)
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| 4 |
Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media
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In: DTIC (2013)
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| 5 |
QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents
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In: DTIC (2012)
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| 6 |
Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program-Awareness
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In: DTIC (2012)
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| 7 |
Integrating Hard and Soft Information Sources for D2D Using Controlled Natural Language
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In: DTIC (2012)
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| 8 |
SAWUS: Siena's Automatic Wikipedia Update System
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In: DTIC (2012)
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| 9 |
Entity List Completion Using Set Expansion Techniques
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In: DTIC (2011)
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| 10 |
A Smarter Common Operational Picture: The Application of Abstraction Hierarchies to Naval Command and Control
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In: DTIC (2011)
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| 11 |
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
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In: DTIC (2011)
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| 12 |
Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
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In: DTIC (2011)
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Abstract:
Written information for military purposes is available in abundance. Documents are written in many languages. The question is how we can automate the content extraction of these documents. One possible approach is based on shallow parsing (information extraction) with application specific combination of analysis results. One example of this, the ZENON research system, does a partial content analysis of some English, Dari, and Tajik texts. Another principal approach for content extraction is based on a combination of deep and shallow parsing with logical inferences on the analysis results. In the project "Multilingual content analysis with semantic inference on military relevant texts" (mIE) we followed the second approach. In this paper, we present the results of the mIE project. First, we briefly contrast the ZENON project to the mIE project. In the main part of the paper, the mIE project is presented. After explaining the combined deep and shallow parsing approach with Head-driven Phrase Structured Grammars, the inference process is introduced. Then we show how background knowledge (WordNet, YAGO) is integrated into the logical inferences to increase the extent, quality, and accuracy of the content extraction. The prototype also is presented. The presentation includes briefing charts. ; Presented at the International Command and Control Research and Technology Symposium (ICCRTS 2011) (16th) held in Quebec City, Canada, on 21-23 June 2011. Published in the Proceedings of the 16th International Command and Control Research and Technology Symposium, June 2011.
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Keyword:
*AUTOMATION; *DATA FUSION; *DOCUMENTS; *EXTRACTION; *FOREIGN LANGUAGES; *INFORMATION PROCESSING; *MILITARY INTELLIGENCE; *MULTILINGUAL CONTENT EXTRACTION; ACCURACY; BRIEFING CHARTS; DEEP PARSING; FOREIGN REPORTS; GERMANY; INFORMATION EXTRACTION; Information Science; Linguistics; LOGICAL INFERENCES; MIE PROJECT; MILITARY APPLICATIONS; Military Intelligence; MULTILINGUAL CONTENT ANALYSIS; NATURAL LANGUAGE; NATURAL LANGUAGE PROCESSING; PARSERS; PARTIAL CONTENT ANALYSIS; QUALITY; SEMANTIC TEXT ANALYSIS; SEMANTICS; SHALLOW PARSING; SYMPOSIA; ZENON PROJECT
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA546910
http://www.dtic.mil/docs/citations/ADA546910
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| 13 |
Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
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In: DTIC (2010)
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| 14 |
A Multi-Disciplinary University Research Initiative in Hard and Soft Information Fusion: Overview, Research Strategies and Initial Results
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In: DTIC (2010)
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| 15 |
Enabling Multinational Communications with CENTRIXS
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In: DTIC (2010)
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| 16 |
Towards Real-time Program Awareness via Lexical Link Analysis
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In: DTIC (2010)
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| 18 |
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
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In: DTIC (2009)
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| 19 |
Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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| 20 |
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
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In: DTIC (2009)
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