Paper Info
Title
Supporting Natural Language Processing with Background Knowledge: Coreference Resolution Case
Abstract
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 the web under the form of logical theories, with statistical methods for NLP. The major problems that we have to solve to implement our methodology concern (i) the selection of the correct and minimal knowledge among the large amount available in the web, (ii) the representation of uncertain knowledge, and (iii) the resolution and the encoding of the rules that combine knowledge retrieved from Semantic Web sources with semantics in the text. In order to evaluate the appropriateness of our approach, we present an application of the methodology to the problem of intra-document coreference resolution, and we show by means of some experiments on the standard dataset, how the injection of knowledge leads to the improvement of this task performance.
Year
DOI
Venue
2010
10.1007/978-3-642-17746-0_6
International Semantic Web Conference
Keywords
Field
DocType
coreference resolution case,natural language processing,uncertain knowledge,minimal knowledge,large amount,knowledge extraction,intra-document coreference resolution,methodology concern,textual data,reference methodology,web semantics,coreference resolution,machine learning,linked data,semantic web,relation extraction
Data mining,Semantic Web Stack,Computer science,Semantic Web,Artificial intelligence,Natural language processing,Social Semantic Web,Semantic Web Rule Language,Semantic computing,Semantic compression,Semantic search,Information retrieval,Semantic analytics,Database
Conference
Volume
ISSN
ISBN
6496
0302-9743
3-642-17745-X
Citations 
PageRank 
References 
12
0.82
26
Authors
4
Name
Order
Citations
PageRank
Volha Bryl118014.46
Claudio Giuliano248833.00
Luciano Serafini32230204.36
Kateryna Tymoshenko418011.39