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Hits 1 – 11 of 11

1
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection ...
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2
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization ...
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3
Sarcasm detection on Twitter
Lyu, Hao. - 2016
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4
Crowdsourcing construction of information retrieval test collections for conversational speech
Zhou, Haofeng. - 2015
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5
Frontiers, Challenges, and Opportunities for Information Retrieval – Report from SWIRL 2012, The Second Strategic Workshop on Information Retrieval in Lorne
Kelly, Diane; Clarke, Charles L.A.; Moffat, Alistair. - : KTH, Teoretisk datalogi, TCS, 2012. : ACM, 2012
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6
Reading between the lines : object localization using implicit cues from image tags
Hwang, Sung Ju. - 2010
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7
Incorporating Relevance and Psuedo-Relevance Feedback in the Markov Random Field Model
In: DTIC (2008)
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8
Recognizing disfluencies in conversational speech
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 14 (2006) 5, 1566-1573
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9
Linguistic Resources for Speech Parsing
In: DTIC (2006)
Abstract: We report on the success of a two-pass approach to annotating metadata, speech effects and syntactic structure in English conversational speech: separately annotating transcribed speech for structural metadata, or structural events, (fillers, speech repairs (or edit dysfluencies) and SUs, or syntactic/semantic units) and for syntactic structure (treebanking constituent structure and shallow argument structure). The two annotations were then combined into a single representation. Certain alignment issues between the two types of annotation led to the discovery and correction of annotation errors in each, resulting in a more accurate and useful resource. The development of this corpus was motivated by the need to have both metadata and syntactic structure annotated in order to support synergistic work on speech parsing and structural event detection. Automatic detection of these speech phenomena would simultaneously improve parsing accuracy and provide a mechanism for cleaning up transcriptions for downstream text processing. Similarly, constraints imposed by text processing systems such as parsers can be used to help improve identification of dysfluencies and sentence boundaries. This paper reports on our efforts to develop a linguistic resource providing both spoken metadata and syntactic structure information, and describes the resulting corpus of English conversational speech. ; Sponsored in part by the National Science Foundation Grant no. 0121285. Presented at the International Conference on Language Resources and Evaluation (5th), LREC 2006, held in Genoa, Italy on 22-28 May 2006. Published online by the Linguistic Data Consortium.
Keyword: *COMPUTATIONAL LINGUISTICS; *SPEECH; Cybernetics; ENGLISH LANGUAGE; Linguistics; METADATA; NATURAL LANGUAGE; Operations Research; PARSERS; SYMPOSIA; SYNTACTIC STRUCTURE INFORMATION; SYNTACTIC/SEMANTIC UNITS
URL: http://www.dtic.mil/docs/citations/ADA456754
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA456754
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10
Recognizing disfluencies in conversational speech
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11
Effective Use of Prosody in Parsing Conversational Speech
Kahn, J; Lease, Matthew; Charniak, Eugene. - : East Stroudsburg, PA : Association for Computational Linguistics, 2005
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