Entity recognition in information extraction
|Entity recognition in information extraction|
|Author(s)||Hanafiah N., Quix C.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Database systems, Cosine similarity, Domain independents, Entity recognition, Information extraction systems, Retrieval applications, String similarity, Unstructured texts, Wikipedia articles, Information retrieval systems)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Entity recognition in information extraction is a 2014 conference paper written in English by Hanafiah N., Quix C. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Detecting and resolving entities is an important step in information retrieval applications. Humans are able to recognize entities by context, but information extraction systems (IES) need to apply sophisticated algorithms to recognize an entity. The development and implementation of an entity recognition algorithm is described in this paper. The implemented system is integrated with an IES that derives triples from unstructured text. By doing so, the triples are more valuable in query answering because they refer to identified entities. By extracting the information from Wikipedia encyclopedia, a dictionary of entities and their contexts is built. The entity recognition computes a score for context similarity which is based on cosine similarity with a tf-idf weighting scheme and the string similarity. The implemented system shows a good accuracy on Wikipedia articles, is domain independent, and recognizes entities of arbitrary types.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.