Word sense discrimination on tweets: A graph-based approach

Flavio Massimiliano Cecchini; Elisabetta Fersini; Enza Messina

doi:10.5220/0005640501380146

Word sense discrimination on tweets: A graph-based approach

Flavio Massimiliano Cecchini^*
, Elisabetta Fersini
, Enza Messina

^*Autore corrispondente per questo lavoro

Università Cattolica del Sacro Cuore

University of Milan - Bicocca

Risultato della ricerca: Contributo in libro › Contributo a conferenza

3 Citazioni (Scopus)

Abstract

In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.

Lingua originale	Inglese
Titolo della pubblicazione ospite	IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Pagine	138-146
Numero di pagine	9
Volume	1
DOI	https://doi.org/10.5220/0005640501380146
Stato di pubblicazione	Pubblicato - 2015
Evento	7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal Durata: 12 nov 2015 → 14 nov 2015

Convegno

Convegno	7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
Città	Lisbon, Portugal
Periodo	12/11/15 → 14/11/15

Keywords

Twitter
Word Sense Discrimination
graph-based methods
unsupervised methods

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10.5220/0005640501380146

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@inproceedings{e2fb35b582c14f84864fa40a7bbaab36,

title = "Word sense discrimination on tweets: A graph-based approach",

abstract = "In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.",

keywords = "Twitter, Word Sense Discrimination, graph-based methods, unsupervised methods, Twitter, Word Sense Discrimination, graph-based methods, unsupervised methods",

author = "Cecchini, \{Flavio Massimiliano\} and Elisabetta Fersini and Enza Messina",

year = "2015",

doi = "10.5220/0005640501380146",

language = "English",

isbn = "9789897581588",

volume = "1",

pages = "138--146",

booktitle = "IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",

note = "7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 ; Conference date: 12-11-2015 Through 14-11-2015",

}

Cecchini, FM, Fersini, E & Messina, E 2015, Word sense discrimination on tweets: A graph-based approach. in IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. vol. 1, pagg. 138-146, 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015, Lisbon, Portugal, 12/11/15. https://doi.org/10.5220/0005640501380146

TY - GEN

T1 - Word sense discrimination on tweets: A graph-based approach

AU - Cecchini, Flavio Massimiliano

AU - Fersini, Elisabetta

AU - Messina, Enza

PY - 2015

Y1 - 2015

N2 - In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.

AB - In this paper we are going to detail an unsupervised, graph-based approach for word sense discrimination on tweets. We deal with this problem by constructing a word graph of co-occurrences. By defining a distance on this graph, we obtain a word metric space, on which we can apply an aggregative algorithm for word clustering. As a result, we will get word clusters representing contexts that discriminate the possible senses of a term. We present some experimental results both on a data set consisting of tweets we collected and on the data set of task 14 at SemEval-2010.

KW - Twitter

KW - Word Sense Discrimination

KW - graph-based methods

KW - unsupervised methods

KW - Twitter

KW - Word Sense Discrimination

KW - graph-based methods

KW - unsupervised methods

UR - http://hdl.handle.net/10807/122106

U2 - 10.5220/0005640501380146

DO - 10.5220/0005640501380146

M3 - Conference contribution

SN - 9789897581588

VL - 1

SP - 138

EP - 146

BT - IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

T2 - 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015

Y2 - 12 November 2015 through 14 November 2015

ER -

Word sense discrimination on tweets: A graph-based approach

Abstract

Convegno

Keywords

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