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21
Generic Sentence Fusion is an Ill-Defined Summarization Task
In: DTIC (2004)
BASE
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22
A Machine Learning Approach for Identification Thesis and Conclusion Statements in Student Essays [<Journal>]
Burstein, Jill [Verfasser]; Marcu, Daniel [Verfasser]
DNB Subject Category Language
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23
Current and new directions in discourse and dialogue
Ebert, Christian (Mitarb.); Healey, Patrick (Mitarb.); Traum, David R. (Mitarb.). - Dordrecht [u.a.] : Kluwer, 2003
BLLDB
UB Frankfurt Linguistik
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24
A machine learning approach for identification of thesis and conclusion statements in student essays
In: Computers and the humanities. - Dordrecht [u.a.] : Kluwer 37 (2003) 4, 455-467
BLLDB
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25
Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences
In: DTIC (2003)
BASE
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26
Statistical Phrase-Based Translation
In: DTIC (2003)
BASE
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27
A Noisy-Channel Approach to Question Answering
In: DTIC (2003)
BASE
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28
Translation with scarce bilingual resources
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 17 (2002) 1, 1-17
BLLDB
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29
Translation with Scarce Bilingual Resources
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 17 (2002) 1, 1-18
OLC Linguistik
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30
RST Discourse Treebank
Carlson, Lynn; Marcu, Daniel; Okurowski, Mary Ellen. - : Linguistic Data Consortium, 2002. : https://www.ldc.upenn.edu, 2002
BASE
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31
RST Discourse Treebank ...
Carlson, Lynn; Marcu, Daniel; Okurowski, Mary Ellen. - : Linguistic Data Consortium, 2002
BASE
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32
The Importance of Lexicalized Syntax Models for Natural Language Generation Tasks
In: DTIC (2002)
BASE
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33
A Noisy-Channel Model for Document Compression
In: DTIC (2002)
BASE
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34
An Unsupervised Approach to Recognizing Discourse Relations
In: DTIC (2002)
Abstract: We present an unsupervised approach to recognizing discourse relations of CONTRAST, EXPLANATION-EVIDENCE, CONDITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases. ; Sponsored in part by National Science Foundation. Presented at the Annual Meeting of the Association for Computational Linguistics (40th) held in Philadelphia, PA on 6-12 Jul 2002. Published in the Proceedings of the Annual Meeting of the Association for Computational Linguistics (40th), p368-375, 2002.
Keyword: *CLASSIFICATION; *COMPUTATIONAL LINGUISTICS; *DISCOURSE RELATIONS; *NAIVE BAYES CLASSIFIERS; *SEMANTICS; Cybernetics; Linguistics; NATURAL LANGUAGE; RECOGNITION; SYMPOSIA; TRAINING DATA
URL: http://www.dtic.mil/docs/citations/ADA462240
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA462240
BASE
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35
A Phrase-Based, Joint Probability for Statistical Machine Translation
In: DTIC (2002)
BASE
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36
Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory
In: DTIC (2001)
BASE
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37
Towards a Unified Approach to Memory- and Statistical-Based Machine Translation
In: DTIC (2001)
BASE
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38
Fast Decoding and Optimal Decoding for Machine Translation
In: DTIC (2001)
BASE
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39
The theory and practice of discourse parsing and summarization
Marcu, Daniel. - Cambridge, Massachusetts : MIT Press, 2000, [2000]©2000
MPI für Psycholinguistik
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40
Perlocutions: The Achilles' heel of speech act theory
In: Journal of pragmatics. - Amsterdam [u.a.] : Elsevier 32 (2000) 12, 1719-1742
OLC Linguistik
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