Document Type
Conference Proceeding
Publication Date
10-2009
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 Allen’s 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.
Repository Citation
Gruhl, D.,
Nagarajan, M.,
Pieper, J.,
Robson, C.,
& Sheth, A. P.
(2009). Context and Domain Knowledge Enhanced Entity Spotting in Informal Text. Lecture Notes in Computer Science, 5823, 260-276.
https://corescholar.libraries.wright.edu/knoesis/549
DOI
10.1007/978-3-642-04930-9_17
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Attached is the unpublished version of the proceeding. The final, publisher's version can be found at http://dx.doi.org/10.1007/978-3-642-04930-9_17.
Presented at the 8th International Semantic Web Conference, Chantilly, VA, October 25-29, 2009.