@inproceedings{L16-1189,
 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).
},
 address = {Portorož, Slovenia},
 author = {Fabio Tamburini},
 booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
 month = {May},
 pages = {1190--1195},
 publisher = {European Language Resources Association (ELRA)},
 title = {Specialising Paragraph Vectors for Text Polarity Detection},
 url = {https://www.aclweb.org/anthology/L16-1189},
 year = {2016}
}