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Methods for Evaluating Text Extraction Toolkits: An Exploratory Investigation
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In: DTIC (2015)
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| 2 |
Proactive Intelligence (PAINT) Simulated Exploration of Executable Design Strategies (SEEDS)
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In: DTIC (2009)
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| 3 |
Novel Topic Impact on Authorship Attribution
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In: DTIC (2009)
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| 4 |
Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
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In: DTIC (2007)
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| 5 |
Techniques for Automatically Generating Biographical Summaries from News Articles
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In: DTIC (2007)
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| 6 |
Effectively Using Syntax for Recognizing False Entailment
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In: DTIC (2006)
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| 7 |
Syntactic Simplification for Improving Content Selection in Multi-Document Summarization
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In: DTIC (2004)
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| 8 |
HITIQA: Towards Analytical Question Answering
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In: DTIC (2004)
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| 9 |
Consolidating the Results of the CIRCSIM-Tutor Project and Further Consolidation of the Results of the CIRCSIM-Tutor Project
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In: DTIC AND NTIS (2003)
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| 10 |
Large Scale Language Independent Generation Using Thematic Hierarchies
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In: DTIC (2001)
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| 11 |
Towards a Unified Approach to Memory- and Statistical-Based Machine Translation
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In: DTIC (2001)
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| 12 |
A Survey of Current Paradigms in Machine Translation
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In: DTIC (1998)
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| 14 |
Eucalyptus: Integrating Natural Language Input with a Graphical User Interface
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In: DTIC AND NTIS (1994)
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| 15 |
Adaptive Statistical Language Modeling; A Maximum Entropy Approach
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In: DTIC AND NTIS (1994)
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Abstract:
Language modeling is the attempt to characterize, capture and exploit regularities in natural language. In statistical language modeling, large amounts of text are used to automatically determine the model's parameters. Language modeling is useful in automatic speech recognition, machine translation, and any other application that processes natural language with incomplete knowledge. In this thesis, I view language as an information source which emits a stream of symbols from a finite alphabet (the vocabulary). The goal of language modeling is then to identify and exploit sources of information in the language stream, so as to minimize its perceived entropy. Most existing statistical language models exploit the immediate past only. To extract information from further back in the document's history, I use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from many sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, I apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the NE solution. Language modeling, Adaptive language modeling, Statistical language modeling, Maximum entropy, Speech recognition.
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Keyword:
*NATURAL LANGUAGE; *SPEECH RECOGNITION; ALGORITHMS; AUTOMATIC; BEARINGS; COMPUTER AIDED INSTRUCTION; COMPUTER APPLICATIONS; Cybernetics; ENTROPY; ERRORS; ESTIMATES; INTERPOLATION; LANGUAGE TRANSLATION; Linguistics; MACHINE TRANSLATION; PARAMETERS; PROBABILITY DISTRIBUTION FUNCTIONS; STATISTICS; SYMBOLS; TEST AND EVALUATION; THESES; VOCABULARY
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA281027
http://www.dtic.mil/docs/citations/ADA281027
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| 16 |
Using Case-Based Reasoning in Natural Language Processing
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In: DTIC AND NTIS (1993)
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| 17 |
AMAR: A Computational Model of Autosegmental Phonology
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In: DTIC AND NTIS (1993)
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| 18 |
Methods for Parallelizing Search Paths in Phrasing
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In: DTIC AND NTIS (1993)
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| 19 |
Talking to InterFIS: Adding Speech Input to a Natural Language Interface
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In: DTIC AND NTIS (1992)
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| 20 |
A Practical Methodology for the Evaluation of Spoken Language Systems
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In: DTIC (1992)
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