Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/iswc2009/paper/research/174> ?p ?o. }
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- 174 creator amit-sheth.
- 174 creator christine-robson.
- 174 creator daniel-gruhl.
- 174 creator jan-pieper.
- 174 creator meena-nagarajan.
- 174 type InProceedings.
- 174 label "Context and Domain Knowledge Enhanced Entity Spotting in Informal Text".
- 174 sameAs 174.
- 174 abstract "This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album Music or Lilly Allens pop hit Smile. We evaluate improvements in annotation accuracy that can be obtained by restricting the set of possible entities using real-world constraints. We find that constrained domain entity extraction raises the annotation accuracy significantly, making an infeasible task practical. We then show that we can further improve annotation accuracy by over 50% by applying SVM based NLP systems trained on word-usages in this domain.".
- 174 hasAuthorList authorList.
- 174 hasTopic Semantic_Web.
- 174 isPartOf proceedings.
- 174 title "Context and Domain Knowledge Enhanced Entity Spotting in Informal Text".