The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. In particular, microblogging platforms enables the collection of continuously and instantly updated information. The organization and extraction of valuable knowledge from these contents are fundamental for ensuring profitability and efficiency to companies and institutions. This paper presents an unsupervised model for the task of Named Entity Linking in microblogging environments. The aim is to link the named entity mentions in a text with their corresponding knowledge-base entries exploiting a novel heterogeneous representation space characterized by more meaningful similarity measures between words and named entities, obtained by Word Embeddings. The proposed model has been evaluated on different benchmark datasets proposed for Named Entity Linking challenges for English and Italian language. It obtains very promising performance given the highly challenging environment of user-generated content over microblogging platforms.

Nozza, D., Sas, C., Fersini, E., Messina, E. (2019). Word Embeddings for Unsupervised Named Entity Linking. In Knowledge Science, Engineering and Management : 12th International Conference, KSEM 2019, Athens, Greece, August 28–30, 2019, Proceedings, Part II (pp.115-132). Springer [10.1007/978-3-030-29563-9_13].

Word Embeddings for Unsupervised Named Entity Linking

Nozza, D;Fersini, E;Messina, E
2019

Abstract

The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. In particular, microblogging platforms enables the collection of continuously and instantly updated information. The organization and extraction of valuable knowledge from these contents are fundamental for ensuring profitability and efficiency to companies and institutions. This paper presents an unsupervised model for the task of Named Entity Linking in microblogging environments. The aim is to link the named entity mentions in a text with their corresponding knowledge-base entries exploiting a novel heterogeneous representation space characterized by more meaningful similarity measures between words and named entities, obtained by Word Embeddings. The proposed model has been evaluated on different benchmark datasets proposed for Named Entity Linking challenges for English and Italian language. It obtains very promising performance given the highly challenging environment of user-generated content over microblogging platforms.
paper
Named Entity Linking; Social media; Word Embeddings;
Named Entity Linking; Social media; Word Embeddings
English
12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019
2019
Douligeris, C; Kanagiannis, D; Apostolou, D
Knowledge Science, Engineering and Management : 12th International Conference, KSEM 2019, Athens, Greece, August 28–30, 2019, Proceedings, Part II
978-3-030-29562-2
2019
11776
115
132
https://link.springer.com/chapter/10.1007/978-3-030-29563-9_13
none
Nozza, D., Sas, C., Fersini, E., Messina, E. (2019). Word Embeddings for Unsupervised Named Entity Linking. In Knowledge Science, Engineering and Management : 12th International Conference, KSEM 2019, Athens, Greece, August 28–30, 2019, Proceedings, Part II (pp.115-132). Springer [10.1007/978-3-030-29563-9_13].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/256745
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