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Short text authorship attribution via sequence kernels, Markov chains and author unmasking: An investigation
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In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing ; http://acl.ldc.upenn.edu/W/W06/#W06-1600 (2015)
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Toward Determining the Comprehensibility of Machine Translations
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In: DTIC (2012)
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| 4 |
Novel Topic Impact on Authorship Attribution
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In: DTIC (2009)
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| 5 |
Syntactic Simplification for Improving Content Selection in Multi-Document Summarization
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In: DTIC (2004)
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| 6 |
Cross-Document Coreference on a Large Scale Corpus
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In: DTIC (2004)
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| 7 |
A Similarity-Based Approach and Evaluation Methodology for Reduction of Drug Name Confusion
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In: DTIC (2003)
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| 8 |
The Bible, Truth, and Multilingual OCR Evaluation
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In: DTIC (1998)
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| 9 |
Experiments in Spoken Document Retrieval at CMU
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In: DTIC (1997)
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| 10 |
Efficient Algorithms for Speech Recognition.
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In: DTIC AND NTIS (1996)
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| 11 |
Overview of Results of the MUC-6 Evaluation
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In: DTIC (1995)
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| 12 |
Phonological Parsing for Bi-directional Letter-to-Sound/Sound-to-Letter Generation
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In: DTIC (1994)
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Abstract:
In this paper, we describe a reversible letter-to-sound/sound to-letter generation system based on an approach which combines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as morphology, stress, syllabification, phonemics and graphemics. Long-distance constraints are propagated by enforcing local constraints throughout the hierarchy. Our training and testing corpora are derived from the high-frequency portion of the Brown Corpus (10,000 words), augmented with markers indicating stress and word morphology. We evaluated our performance based on an unseen test set. The percentage of nonparsable words for letter-to-sound and sound-to-letter generation were 6% and 5% respectively. Of the remaining words our system achieved a word accuracy of 71.8% and a phoneme accuracy of 92.5% for letter-to-sound generation, and a word accuracy of 55.8% and letter accuracy of 89.4% for sound-to-letter generation. We also compared our hierarchical approach with an alternative, single-layer approach to demonstrate how the hierarchy provides a parsimonious description for English orthographic-phonological regularities, while simultaneously attaining competitive generation accuracy. ; Supported in part by DARPA.
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Keyword:
*PARSERS; *WORDS(LANGUAGE); ACCURACY; COMPETITION; HIERARCHIES; INDICATORS; LEXICOGRAPHY; Linguistics; MARKERS; MORPHOLOGY; PHONETICS; PROBABILITY; STRATEGY; STRESSES; TEST SETS
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URL: http://www.dtic.mil/docs/citations/ADA458591
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458591
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| 13 |
Signal Processing for Robust Speech Recognition
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In: DTIC (1994)
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| 14 |
Adaptive Natural Language Processing
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In: DTIC AND NTIS (1991)
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| 15 |
Global Optimization of Digital Circuits.
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In: DTIC AND NTIS (1991)
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Integrating Syntax, Semantics,and Discourse DARPA (Defense Advanced Research Projects Agency) Natural Language Understanding Program
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In: DTIC AND NTIS (1989)
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| 17 |
Integration of Speech and Natural Language
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In: DTIC AND NTIS (1989)
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| 18 |
PROGRAMMING LANGUAGE FOR AUTOMATIC CHECKOUT EQUIPMENT. VOLUME II. ADAPTED PLACE FOR THE BENDIX AN/GJQ-9.
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In: DTIC AND NTIS (1963)
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