@inproceedings{wawer-mykowiecka-2017-supervised,
title = "Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms",
author = "Wawer, Aleksander and
Mykowiecka, Agnieszka",
editor = "Camacho-Collados, Jose and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1915",
doi = "10.18653/v1/W17-1915",
pages = "120--125",
abstract = "This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet).",
}
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%0 Conference Proceedings
%T Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
%A Wawer, Aleksander
%A Mykowiecka, Agnieszka
%Y Camacho-Collados, Jose
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F wawer-mykowiecka-2017-supervised
%X This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet).
%R 10.18653/v1/W17-1915
%U https://aclanthology.org/W17-1915
%U https://doi.org/10.18653/v1/W17-1915
%P 120-125
Markdown (Informal)
[Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms](https://aclanthology.org/W17-1915) (Wawer & Mykowiecka, SENSE 2017)
ACL