Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach

Lisa Gentile, Anna, et al. "Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach." Serdica Journal of Computing 4.2 (2010): 217-242. <http://eudml.org/doc/11385>.

@article{LisaGentile2010,
abstract = {One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.},
author = {Lisa Gentile, Anna, Zhang, Ziqi, Xia, Lei, Iria, José},
journal = {Serdica Journal of Computing},
keywords = {Wikipedia; Named Entity Disambiguation; Semantic Relatedness; Graph},
language = {eng},
number = {2},
pages = {217-242},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach},
url = {http://eudml.org/doc/11385},
volume = {4},
year = {2010},
}

TY - JOUR
AU - Lisa Gentile, Anna
AU - Zhang, Ziqi
AU - Xia, Lei
AU - Iria, José
TI - Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach
JO - Serdica Journal of Computing
PY - 2010
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 4
IS - 2
SP - 217
EP - 242
AB - One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.
LA - eng
KW - Wikipedia; Named Entity Disambiguation; Semantic Relatedness; Graph
UR - http://eudml.org/doc/11385
ER -