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Simple Search
Hits 1 – 11 of 11
1
Text preprocessing
Palmer, David D.
In:
Handbook of natural language processing
(Boca Raton, Fla., 2010), p. 9-30
MPI für Psycholinguistik
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2
Improving out-of-vocabulary name resolution
Palmer, David D.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
19 (2005) 1, 107-128
BLLDB
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3
Improving out-of-vocabulary name resolution
Palmer, David D.
;
Ostendorf, Mari
In:
Computer speech and language. - Amsterdam [u.a.] : Elsevier
19 (2005) 1, 107
OLC Linguistik
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4
Multilingual Video and Audio News Alerting
Palmer, David D.
;
Bray, Patrick
;
Reichman, Marc
...
In: DTIC (2004)
BASE
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5
Robust information extraction from automatically generated speech transcriptions
Palmer, David D.
;
Ostendorf, Mari
;
Burger, John D.
In:
Speech communication. - Amsterdam [u.a.] : Elsevier
32 (2000) 1, 95-110
OLC Linguistik
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6
Accessing information in spoken audio
Renals, Steve
(Hrsg.);
Robinson, Tony
(Hrsg.);
Abberley, Dave
(Mitarb.)...
In:
Speech communication. - Amsterdam [u.a.] : Elsevier
32 (2000) 1-2, 1-154
BLLDB
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7
Tokenisation and sentence segmentation
Palmer, David D.
In:
Handbook of natural language processing. - New York [u.a.] : Dekker
(2000), 11-35
BLLDB
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8
Adaptive Multilingual Sentence Boundary Disambiguation
Palmer, David D
;
Hearst, Marti A
In:
Computational linguistics. - Cambridge, Mass. : MIT Press
23 (1997) 2, 241-268
OLC Linguistik
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9
Adaptive multilingual sentence boundary disambiguation
Palmer, David D.
;
Hearst, Marti A.
In:
Computational linguistics. - Cambridge, Mass. : MIT Press
23 (1997) 2, 241-267
BLLDB
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10
SATZ - An Adaptive Sentence Segmentation System ...
Palmer, David D.
. - : arXiv, 1995
BASE
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11
Adaptive Sentence Boundary Disambiguation ...
Palmer, David D.
;
Hearst, Marti A.
. - : arXiv, 1994
Abstract:
Labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks, including part-of-speech tagging and sentence alignment. End-of-sentence punctuation marks are ambiguous; to disambiguate them most systems use brittle, special-purpose regular expression grammars and exception rules. As an alternative, we have developed an efficient, trainable algorithm that uses a lexicon with part-of-speech probabilities and a feed-forward neural network. After training for less than one minute, the method correctly labels over 98.5\% of sentence boundaries in a corpus of over 27,000 sentence-boundary marks. We show the method to be efficient and easily adaptable to different text genres, including single-case texts. ... : This is a Latex version of the previously submitted ps file (formatted as a uuencoded gz-compressed .tar file created by csh script). The software from the work described in this paper is available by contacting dpalmer@cs.berkeley.edu ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/cmp-lg/9411022
https://dx.doi.org/10.48550/arxiv.cmp-lg/9411022
BASE
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