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- item-5 hasContent shawn-medero.
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- 754 creator alan-lee.
- 754 creator aravind-joshi.
- 754 creator bonnie-webber.
- 754 creator eleni-miltsakaki.
- 754 creator livio-robaldo.
- 754 creator nikhil-dinesh.
- 754 creator rashmi-prasad.
- 754 type InProceedings.
- 754 label "The Penn Discourse TreeBank 2.0.".
- 754 sameAs 754.
- 754 abstract "We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus.".
- 754 hasAuthorList authorList.
- 754 hasTopic Linguistics.
- 754 isPartOf proceedings.
- 754 keyword "Corpus (creation, annotation, etc.)".
- 754 keyword "Discourse".
- 754 keyword "Text mining".
- 754 title "The Penn Discourse TreeBank 2.0.".
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- item-1 hasContent rashmi-prasad.
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- item-2 hasContent nikhil-dinesh.
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- item-7 hasContent bonnie-webber.
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- 755 creator christopher-cieri.
- 755 creator denise-dipersio.
- 755 creator marian-reed.
- 755 type InProceedings.
- 755 label "The Linguistic Data Consortium Member Survey: Purpose, Execution and Results".
- 755 sameAs 755.
- 755 abstract "The Linguistic Data Consortium (LDC) seeks to provide its members with quality linguistic resources and services. In order to pursue these ideals and to remain current, LDC monitors the needs and sentiments of its communities. One mechanism LDC uses to generate feedback on consortium and resource issues is the LDC Member Survey. The survey allows LDC Members and nonmembers to provide LDC with valuable insight into their own unique circumstances, their current and future data needs and their views on LDC s role in meeting them. When the 2006 Survey was found to be a useful tool for communicating with the Consortium membership, a 2007 Survey was organized and administered. As a result of the surveys, LDC has confirmed that it has made a positive impact on the community and has identified ways to improve the quality of service and the diversity of monthly offerings. Many respondents recommended ways to improve LDC s functions, ordering mechanism and webpage. Some of these comments have inspired changes to LDC s operation and strategy.".
- 755 hasAuthorList authorList.
- 755 hasTopic Linguistics.
- 755 isPartOf proceedings.
- 755 keyword "LR national/international projects, organizational/policy issues".
- 755 keyword "Other".
- 755 keyword "Usability, user satisfaction".
- 755 title "The Linguistic Data Consortium Member Survey: Purpose, Execution and Results".
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- item-1 hasContent marian-reed.
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- 758 creator angus-roberts.
- 758 creator mark-hepple.
- 758 creator robert-gaizasukas.
- 758 creator yikun-guo.
- 758 type InProceedings.
- 758 label "Combining Terminology Resources and Statistical Methods for Entity Recognition: an Evaluation".
- 758 sameAs 758.
- 758 abstract "Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from the text back to a resource can make additional knowledge available to applications. Use of such resources is especially pertinent in the biomedical domain, where large numbers of these resources are available, and where they are widely used in informatics applications. Terminology resources can be most readily used by simple lexical lookup of terms in the text. A major drawback with such lexical lookup, however, is poor precision caused by ambiguity between domain terms and general language words. We combine lexical lookup with simple filtering of ambiguous terms, to improve precision. We compare this lexical lookup with a statistical method of entity recognition, and to a method which combines the two approaches. We show that the combined method boosts precision with little loss of recall, and that linkage from recognised entities back to the domain knowledge resources can be maintained.".
- 758 hasAuthorList authorList.
- 758 hasTopic Linguistics.
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- 758 keyword "Acquisition, Machine Learning".
- 758 keyword "Information Extraction, Information Retrieval".