Matches in ScholarlyData for { ?s ?p ?o. }
- 303 keyword "Acquisition, Machine Learning".
- 303 keyword "Corpus (creation, annotation, etc.)".
- 303 keyword "Named Entity recognition".
- 303 title "Is this NE tagger getting old?".
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- item-1 type ListItem.
- item-1 hasContent cristina-mota.
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- item-2 type ListItem.
- item-2 hasContent ralph-grishman.
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- 304 creator miroslav-janicek.
- 304 creator ondrej-bojar.
- 304 creator pavel-ceska.
- 304 creator peter-bena.
- 304 creator zdenek-abokrtsky.
- 304 type InProceedings.
- 304 label "CzEng 0.7: Parallel Corpus with Community-Supplied Translations".
- 304 sameAs 304.
- 304 abstract "This paper describes CzEng 0.7, a new release of Czech-English parallel corpus freely available for research and educational purposes. We provide basic statistics of the corpus and focus on data produced by a community of volunteers. Anonymous contributors manually correct the output of a machine translation (MT) system, generating on average 2000 sentences a month, 70% of which are indeed correct translations. We compare the utility of community-supplied and of professionally translated training data for a baseline English-to-Czech MT system.".
- 304 hasAuthorList authorList.
- 304 hasTopic Linguistics.
- 304 isPartOf proceedings.
- 304 keyword "Corpus (creation, annotation, etc.)".
- 304 keyword "Machine Translation, SpeechToSpeech Translation".
- 304 keyword "Validation of LRs".
- 304 title "CzEng 0.7: Parallel Corpus with Community-Supplied Translations".
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- authorList hasItem item-1.
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- item-1 type ListItem.
- item-1 hasContent ondrej-bojar.
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- item-2 type ListItem.
- item-2 hasContent miroslav-janicek.
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- item-3 type ListItem.
- item-3 hasContent zdenek-abokrtsky.
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- item-4 type ListItem.
- item-4 hasContent pavel-ceska.
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- item-5 hasContent peter-bena.
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- 306 creator peter-anick.
- 306 creator shaji-sebastian.
- 306 creator vijay-murthi.
- 306 type InProceedings.
- 306 label "Similar Term Discovery using Web Search".
- 306 sameAs 306.
- 306 abstract "We present an approach to the discovery of semantically similar terms that utilizes a web search engine as both a source for generating related terms and a tool for estimating the semantic similarity of terms. The system works by associating with each document in the search engine s index a weighted term vector comprising those phrases that best describe the document s subject matter. Related terms for a given seed phrase are generated by running the seed as a search query and mining the result vector produced by averaging the weights of terms associated with the top documents of the query result set. The degree of similarity between the seed term and each related term is then computed as the cosine of the angle between their respective result vectors. We test the effectiveness of this approach for building a term recommender system designed to help online advertisers discover additional phrases to describe their product offering. A comparison of its output with that of several alternative methods finds it to be competitive with the best known alternative.".
- 306 hasAuthorList authorList.
- 306 hasTopic Linguistics.
- 306 isPartOf proceedings.
- 306 keyword "Information Extraction, Information Retrieval".
- 306 keyword "Text mining".
- 306 keyword "Tools, systems, applications".
- 306 title "Similar Term Discovery using Web Search".
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- item-1 type ListItem.
- item-1 hasContent peter-anick.
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- item-2 type ListItem.
- item-2 hasContent vijay-murthi.
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- item-3 type ListItem.
- item-3 hasContent shaji-sebastian.
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- 308 creator jonathan-clark.
- 308 creator lori-levin.
- 308 creator robert-frederking.
- 308 type InProceedings.
- 308 label "Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation".
- 308 sameAs 308.
- 308 abstract "Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next.".
- 308 hasAuthorList authorList.
- 308 hasTopic Linguistics.
- 308 isPartOf proceedings.
- 308 keyword "Corpus (creation, annotation, etc.)".
- 308 keyword "Machine Translation, SpeechToSpeech Translation".
- 308 keyword "Typological databases".