Short context messages (like tweets and SMS’s) are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. Most efforts done in this direction rely on machine learning techniques which are expensive in terms of data collection and training.
In this paper we present an unsupervised Semantic Web-driven approach to improve the extraction process by using clues from the disambiguation process. For extraction we used a simple Knowledge-Base matching technique combined with a clustering-based approach for disambiguation. Experimental results on a self-collected set of tweets (as an example of short context messages) show improvement in extraction results when using unsupervised feedback from the disambiguation process.
|Title of host publication||Workshop on Semantic Web and Information Extraction, SWAIE 2012|
|Place of Publication||Germany|
|Number of pages||10|
|Publication status||Published - Oct 2012|
|Event||Workshop on Semantic Web and Information Extraction, SWAIE 2012 - Galway, Ireland|
Duration: 8 Oct 2012 → 12 Oct 2012
|Name||CEUR Workshop Proceedings|
|Workshop||Workshop on Semantic Web and Information Extraction, SWAIE 2012|
|Period||8/10/12 → 12/10/12|
|Other||8-12 October 2012|
- Named Entity RecognitionNamed Entity LinkingNamed Entity ExtractionNamed Entity DisambiguationTwitterTweetsMicroblogs
- Named Entity Extraction Named Entity Disambiguation Twitter