Variance Matters: Detecting Semantic Differences without Corpus/Word Alignment
Submission Type: Regular Long Paper
Submission Track: Linguistic Theories, Cognitive Modeling, and Psycholinguistics
Submission Track 2: Interpretability, Interactivity, and Analysis of Models for NLP
Keywords: Semantic difference, semantic shift, word vectors, variance, concentration parameter
TL;DR: Detecting Semantic Differences without Corpus/Word Alignment
Abstract: In this paper, we propose methods for discovering semantic differences in words appearing in two corpora. The key idea is to measure the coverage of meanings of a word in a corpus through the norm of its mean word vector, which is equivalent to examining a kind of variance of the word vector distribution. The proposed methods do not require alignments between words and/or corpora for comparison that previous methods do. All they require are to compute variance (or norms of mean word vectors) for each word type. Nevertheless, they rival the best-performing system in the SemEval-2020 Task 1. In addition, they are (i) robust for the skew in corpus sizes; (ii) capable of detecting semantic differences in infrequent words; and (iii) effective in pinpointing word instances that have a meaning missing in one of the two corpora under comparison. We show these advantages for historical corpora and also for native/non-native English corpora.
Submission Number: 47
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