Semantic relatedness approach for named entity disambiguation
|Semantic relatedness approach for named entity disambiguation|
|Author(s)||Gentile A.L., Zhang Z., Xia L., Iria J.|
|Published in||Communications in Computer and Information Science|
|Keyword(s)||Unknown (Extra: Bag of words, Data sets, Graph-based models, Named entities, NAtural language processing, Natural languages, Real-world objects, Semantic content, Semantic relatedness, State of the art, Wikipedia, Computational linguistics, Digital libraries, Natural language processing systems)|
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|Browse properties · List of conference papers|
Semantic relatedness approach for named entity disambiguation is a 2010 conference paper written in English by Gentile A.L., Zhang Z., Xia L., Iria J. and published in Communications in Computer and Information Science.
Natural Language is a mean to express and discuss about concepts, objects, events, i.e., it carries semantic contents. One of the ultimate aims of Natural Language Processing techniques is to identify the meaning of the text, providing effective ways to make a proper linkage between textual references and their referents, that is, real world objects. This work addresses the problem of giving a sense to proper names in a text, that is, automatically associating words representing Named Entities with their referents. The proposed methodology for Named Entity Disambiguation is based on Semantic Relatedness Scores obtained with a graph based model over Wikipedia. We show that, without building a Bag of Words representation of the text, but only considering named entities within the text, the proposed paradigm achieves results competitive with the state of the art on two different datasets.
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