Embedding Senses via Dictionary Bootstrapping

Byungkon Kang, Kyung-Ah Sohn
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:331-340, 2017.

Abstract

This paper addresses the problem of embedding senses of a plain word according to its context. Natural language is inherently ambiguous, due to the presence of many multi-sensed words. Such ambiguity might have undesirable influence over many text-mining tools, including word embedding. Traditional word embedding techniques have focused on identifying which words tend to co-occur with one another in order to derive close embeddings for such words. However, the effectiveness of this approach is largely susceptible to the validity and neutrality of the training corpus. To address this problem, we propose to use the dictionary as the authoritative corpus for computing the word embeddings. The basic idea is to simultaneously embed the definition sentence while disambiguating words. Since dictionaries list a set of disambiguated senses, the proposed procedure yields sense embeddings which exhibit semantic characteristics comparable to plain word embeddings.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-kang17a, title = {Embedding Senses via Dictionary Bootstrapping}, author = {Kang, Byungkon and Sohn, Kyung-Ah}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {331--340}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/kang17a/kang17a.pdf}, url = {https://proceedings.mlr.press/r15/kang17a.html}, abstract = {This paper addresses the problem of embedding senses of a plain word according to its context. Natural language is inherently ambiguous, due to the presence of many multi-sensed words. Such ambiguity might have undesirable influence over many text-mining tools, including word embedding. Traditional word embedding techniques have focused on identifying which words tend to co-occur with one another in order to derive close embeddings for such words. However, the effectiveness of this approach is largely susceptible to the validity and neutrality of the training corpus. To address this problem, we propose to use the dictionary as the authoritative corpus for computing the word embeddings. The basic idea is to simultaneously embed the definition sentence while disambiguating words. Since dictionaries list a set of disambiguated senses, the proposed procedure yields sense embeddings which exhibit semantic characteristics comparable to plain word embeddings.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Embedding Senses via Dictionary Bootstrapping %A Byungkon Kang %A Kyung-Ah Sohn %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-kang17a %I PMLR %P 331--340 %U https://proceedings.mlr.press/r15/kang17a.html %V R15 %X This paper addresses the problem of embedding senses of a plain word according to its context. Natural language is inherently ambiguous, due to the presence of many multi-sensed words. Such ambiguity might have undesirable influence over many text-mining tools, including word embedding. Traditional word embedding techniques have focused on identifying which words tend to co-occur with one another in order to derive close embeddings for such words. However, the effectiveness of this approach is largely susceptible to the validity and neutrality of the training corpus. To address this problem, we propose to use the dictionary as the authoritative corpus for computing the word embeddings. The basic idea is to simultaneously embed the definition sentence while disambiguating words. Since dictionaries list a set of disambiguated senses, the proposed procedure yields sense embeddings which exhibit semantic characteristics comparable to plain word embeddings. %Z Reissued by PMLR on 04 October 2026.
APA
Kang, B. & Sohn, K.. (2017). Embedding Senses via Dictionary Bootstrapping. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:331-340 Available from https://proceedings.mlr.press/r15/kang17a.html. Reissued by PMLR on 04 October 2026.

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