Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
SHEF: semantic tagging and summarization techniques applied to cross-document coreference
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Ontology-based information extraction for business intelligence
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Unsupervised web name disambiguation using semantic similarity and single-pass clustering
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Context similarity measure using Fuzzy Formal Concept Analysis
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Cross-document personal name resolution is the process of identifying whether or not a common personal name mentioned in different documents refers to the same individual. Most previous approaches usually rely on lexical matching such as the occurrence of common words surrounding the entity name to measure the similarity between documents, and then clusters the documents according to their referents. In spite of certain successes, measuring similarity based on lexical comparison sometimes ignores important linguistic phenomena at the semantic level such as synonym or paraphrase. This paper presents a semantics-based approach to the resolution of personal name crossover documents that can make the most of both lexical evidences and semantic clues. In our method, the similarity values between documents are determined by estimating the semantic relatedness between words. Further, the semantic labels attached to sentences allow us to highlight the common personal facts that are potentially available among documents. An evaluation on three web datasets demonstrates that our method achieves the better performance than the previous work.