Distributed specificity for automatic terminology extraction

Author:

Amjadian Ehsan12,Inkpen Diana2,Paribakht T. Sima2,Faez Farahnaz3

Affiliation:

1. Carleton University, Canada

2. University of Ottawa, Canada

3. Western University, Canada

Abstract

Abstract The present article explores two novel methods that integrate distributed representations with terminology extraction. Both methods assess the specificity of a word (unigram) to the target corpus by leveraging its distributed representation in the target domain as well as in the general domain. The first approach adopts this distributed specificity as a filter, and the second directly applies it to the corpus. The filter can be mounted on any other Automatic Terminology Extraction (ATE) method, allows merging any number of other ATE methods, and achieves remarkable results with minimal training. The direct approach does not perform as high as the filtering approach, but it reemphasizes that using distributed specificity as the words’ representation, very little data is required to train an ATE classifier. This encourages more minimally supervised ATE algorithms in the future.

Publisher

John Benjamins Publishing Company

Subject

Library and Information Sciences,Communication,Language and Linguistics

Reference37 articles.

1. Enriching Word Vectors with Subword Information

2. Terminology Extraction Approaches for Product Aspect Detection in Customer Reviews;Broß,2013

3. Automatic term detection

4. Automatic term detection

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