Supporting Natural Language Processing with Background Knowledge: Coreference Resolution Case

Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important directions of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and other tasks related to knowledge extraction, is by exploiting semantics. While in the past, the unavailability of rich and complete semantic descriptions constituted a serious limitation of their applicability, nowadays, the Semantic Web made available a large amount of logically encoded information (e.g. ontologies, RDF(S)-data, linked data, etc.), which constitutes a valuable source of semantics. However, web semantics cannot be easily plugged into machine learning systems. Therefore the objective of this paper is to define a reference methodology for combining semantic information available in th...

• Type: Journal
• Year: 2010
• Where: SEMWEB
• Authors: Volha Bryl, Claudio Giuliano, Luciano Serafini, Kateryna Tymoshenko
• Keywords: Coreference Resolution, Internet Technology, Semantic Web, Semantics, SEMWEB 2010