Specialising Paragraph Vectors for Text Polarity Detection

Fabio Tamburini

Specialising Paragraph Vectors for Text Polarity Detection

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Abstract

This paper presents some experiments for specialising Paragraph Vectors, a new technique for creating text fragment (phrase, sentence, paragraph, text, ...) embedding vectors, for text polarity detection. The first extension regards the injection of polarity information extracted from a polarity lexicon into embeddings and the second extension aimed at inserting word order information into Paragraph Vectors. These two extensions, when training a logistic-regression classifier on the combined embeddings, were able to produce a relevant gain in performance when compared to the standard Paragraph Vector methods proposed by Le and Mikolov (2014).

Anthology ID:: L16-1189
Volume:: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:: May
Year:: 2016
Address:: Portorož, Slovenia
Editors:: Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
Note:
Pages:: 1190–1195
Language:
URL:: https://aclanthology.org/L16-1189/
DOI:
Bibkey:
Cite (ACL):: Fabio Tamburini. 2016. Specialising Paragraph Vectors for Text Polarity Detection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1190–1195, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):: Specialising Paragraph Vectors for Text Polarity Detection (Tamburini, LREC 2016)
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PDF:: https://aclanthology.org/L16-1189.pdf

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@inproceedings{tamburini-2016-specialising,
    title = "Specialising Paragraph Vectors for Text Polarity Detection",
    author = "Tamburini, Fabio",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Grobelnik, Marko  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, Helene  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L16-1189/",
    pages = "1190--1195",
    abstract = "This paper presents some experiments for specialising Paragraph Vectors, a new technique for creating text fragment (phrase, sentence, paragraph, text, ...) embedding vectors, for text polarity detection. The first extension regards the injection of polarity information extracted from a polarity lexicon into embeddings and the second extension aimed at inserting word order information into Paragraph Vectors. These two extensions, when training a logistic-regression classifier on the combined embeddings, were able to produce a relevant gain in performance when compared to the standard Paragraph Vector methods proposed by Le and Mikolov (2014)."
}

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%0 Conference Proceedings
%T Specialising Paragraph Vectors for Text Polarity Detection
%A Tamburini, Fabio
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F tamburini-2016-specialising
%X This paper presents some experiments for specialising Paragraph Vectors, a new technique for creating text fragment (phrase, sentence, paragraph, text, ...) embedding vectors, for text polarity detection. The first extension regards the injection of polarity information extracted from a polarity lexicon into embeddings and the second extension aimed at inserting word order information into Paragraph Vectors. These two extensions, when training a logistic-regression classifier on the combined embeddings, were able to produce a relevant gain in performance when compared to the standard Paragraph Vector methods proposed by Le and Mikolov (2014).
%U https://aclanthology.org/L16-1189/
%P 1190-1195

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Markdown (Informal)

[Specialising Paragraph Vectors for Text Polarity Detection](https://aclanthology.org/L16-1189/) (Tamburini, LREC 2016)

Specialising Paragraph Vectors for Text Polarity Detection (Tamburini, LREC 2016)

ACL

Fabio Tamburini. 2016. Specialising Paragraph Vectors for Text Polarity Detection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1190–1195, Portorož, Slovenia. European Language Resources Association (ELRA).