@inproceedings{wilson-etal-2020-embedding,
title = "Embedding Structured Dictionary Entries",
author = "Wilson, Steven and
Magdy, Walid and
McGillivray, Barbara and
Tyson, Gareth",
editor = "Rogers, Anna and
Sedoc, Jo{\~a}o and
Rumshisky, Anna",
booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.insights-1.18/",
doi = "10.18653/v1/2020.insights-1.18",
pages = "117--125",
abstract = "Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries."
}
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<abstract>Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.</abstract>
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%0 Conference Proceedings
%T Embedding Structured Dictionary Entries
%A Wilson, Steven
%A Magdy, Walid
%A McGillivray, Barbara
%A Tyson, Gareth
%Y Rogers, Anna
%Y Sedoc, João
%Y Rumshisky, Anna
%S Proceedings of the First Workshop on Insights from Negative Results in NLP
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wilson-etal-2020-embedding
%X Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.
%R 10.18653/v1/2020.insights-1.18
%U https://aclanthology.org/2020.insights-1.18/
%U https://doi.org/10.18653/v1/2020.insights-1.18
%P 117-125
Markdown (Informal)
[Embedding Structured Dictionary Entries](https://aclanthology.org/2020.insights-1.18/) (Wilson et al., insights 2020)
ACL
- Steven Wilson, Walid Magdy, Barbara McGillivray, and Gareth Tyson. 2020. Embedding Structured Dictionary Entries. In Proceedings of the First Workshop on Insights from Negative Results in NLP, pages 117–125, Online. Association for Computational Linguistics.