2015. Revisiting Word Embedding for Contrasting Meaning. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th ...

This paper presents the embedding models that achieve an F-score of 92% on the widely-used, publicly available dataset, the GRE “most contrasting word” ...

We present in this paper the embedding models that achieve an F-score of 92% on the widely-used, publicly available dataset, the GRE “most contrasting word” ...

Implemented in one code library.

It is widely accepted that traditional word embedding models, which rely on distributional semantics hypothesis, are relatively limited for contrast meaning ...

Revisiting Word Embedding for Contrasting Meaning ; Automated requirement contradiction detection through formal logic and LLMs. Gärtner A.E., Göhlich D. · Q2.

Recent word-embedding models based on distributional semantics hypothesis are known to be weak for modeling lexical contrast. We present in this paper the ...

Implementations of the Marginal Contrast Embedding (MCE) model presented in the paper "Revisiting word embedding for contrasting meaning" by Zhigang Chen, ...

[word] In linguistics a word is the smallest element that may be uttered in isolation with semantic or pragmatic content (with literal or practical meaning).

Duration: 14:08
Posted: Mar 2, 2016