Unsupervised Embedding-based Detection of Lexical Semantic Changes
- URL: http://arxiv.org/abs/2005.07979v1
- Date: Sat, 16 May 2020 13:05:47 GMT
- Title: Unsupervised Embedding-based Detection of Lexical Semantic Changes
- Authors: Ehsaneddin Asgari and Christoph Ringlstetter and Hinrich Sch\"utze
- Abstract summary: This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1.
EmmLexChange is defined as the divergence between the embedding based profiles of word w in the source and the target domains.
We show that using a resampling framework for the selection of reference words, we can reliably detect lexical-semantic changes in English, German, Swedish, and Latin.
- Score: 1.7403133838762452
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper describes EmbLexChange, a system introduced by the "Life-Language"
team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic
changes. EmbLexChange is defined as the divergence between the embedding based
profiles of word w (calculated with respect to a set of reference words) in the
source and the target domains (source and target domains can be simply two time
frames t1 and t2). The underlying assumption is that the lexical-semantic
change of word w would affect its co-occurring words and subsequently alters
the neighborhoods in the embedding spaces. We show that using a resampling
framework for the selection of reference words, we can reliably detect
lexical-semantic changes in English, German, Swedish, and Latin. EmbLexChange
achieved second place in the binary detection of semantic changes in the
SemEval-2020.
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