A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning to resolve bridging references
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
BART: a modular toolkit for coreference resolution
HLT-Demonstrations '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session
Collective semantic role labelling with Markov logic
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Scaling textual inference to the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Kernel methods for minimally supervised wsd
Computational Linguistics
Simple coreference resolution with rich syntactic and semantic features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Probabilistic inductive logic programming
Supporting natural language processing with background knowledge: coreference resolution case
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Coreference resolution with world knowledge
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Resolving complex cases of definite pronouns: the winograd schema challenge
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
End-to-end coreference resolution for clinical narratives
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 direction 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 constitute 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 semantics 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 ACE 2005 dataset, how the injection of knowledge is correlated to the improvement of the performance of our approach on this tasks.