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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Searching Semantic Resources for Complex Selectional Restrictions to Support Verb Sense Disambiguation Taylor, Merwyn; Carlson, Lynn; Poisson, Stephanie... In: DTIC (2010) BASE Show details 2 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 Show details 3 RST Discourse Treebank Carlson, Lynn; Marcu, Daniel; Okurowski, Mary Ellen. - : Linguistic Data Consortium, 2002. : https://www.ldc.upenn.edu, 2002 BASE Show details 4 RST Discourse Treebank ... Carlson, Lynn; Marcu, Daniel; Okurowski, Mary Ellen. - : Linguistic Data Consortium, 2002 BASE Show details 5 Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory Carlson, Lynn; Marcu, Daniel; Okurowski, Mary E. In: DTIC (2001) BASE Show details 6 Representing Text Meaning for Multilingual Knowledge-Based Machine Translation. Carlson, Lynn; Cooper, Elizabeth; Dolan, Ronald... In: DTIC AND NTIS (1994) BASE Show details 7 Corpora and Data Preparation Carlson, Lynn; Onyshkevych, Boyan; Okurowski, Mary E. In: DTIC (1993) BASE Show details 8 Tasks, Domains, and Languages Onyshkevych, Boyan; Okurowski, Mary E.; Carlson, Lynn In: DTIC (1993) Abstract: The Fifth Message Understanding Conference (MUC-5) involved the same tasks, domains and languages as the information extraction portion of the ARPA TIPSTER program. These tasks center on automatically filling object-oriented data structures, called templates, with information extracted from free text in news stories (for discussion of templates and objects, see "Template Design for Information Extraction" in this volume). For each task, a generic type of information that is specified for extraction corresponds to each of the slots in the templates. With text as input, the MUC-5 systems first detect whether the text contains relevant information. If available, the systems extract specific instances of The generic types from the text and output that information by filling the template slots with the appropriately formatted data representations. These slots are then scored by using an automatic scoring program with analyst-produced templates as the keys. Human analysts also prepared development set templates for each domain, which served as training models for system developers (for discussion of the data preparation effort, see "Corpora and Data Preparation" in this volume). Keyword: *INFORMATION RETRIEVAL; *TEMPLATES; *TEXT PROCESSING; ANALYSTS; AUTOMATIC PROGRAMMING; DATA BASES; DATA MANAGEMENT; EXTRACTION; Information Science; Linguistics; MODELS; PERSONNEL; PREPARATION; SCORING; SYSTEMS ENGINEERING; TRAINING URL: http://www.dtic.mil/docs/citations/ADA459848 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA459848 BASE Hide details 9 Corpora and Data Preparation for Information Extraction Carlson, Lynn; Onyshkevych, Boyan; Okurowski, Mary E In: DTIC (1993) BASE Show details 10 Tasks, Domains, and Languages for Information Extraction Onyshkevych, Boyan; Okurowski, Mary E; Carlson, Lynn In: DTIC (1993) BASE Show details

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