Papers › Definition Modeling: Learning to define word embeddings in natural language

Definition Modeling: Learning to define word embeddings in natural language

1 Dec 2016arXiv:1612.00394archive 2025-07-28

Thanapon Noraset, Chen Liang, Larry Birnbaum, Doug Downey

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a character-level convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.

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intersect_words northanapon/dict-definition/definition/words/word_sampler.py official repository ran · our draft was wrong GPL-3.0 (copyleft) · pointer only · 2b67634f5d886f0d · report
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remove_words_with_chars northanapon/dict-definition/definition/words/word_sampler.py official repository ran · our draft was wrong GPL-3.0 (copyleft) · pointer only · e4f26c3ab03a1a58 · report

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Word EmbeddingsWord Similarity

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Convolution

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