Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings

ACL 2019

Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings

Jan 19, 2021
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Abstract: In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation. In this work, we propose a DSM that learns multiple distributional representations of a word based on different topics. First, a separate DSM is trained for each topic and then each of the topic-based DSMs is aligned to a common vector space. Our unsupervised mapping approach is motivated by the hypothesis that words preserving their relative distances in different topic semantic sub-spaces constitute robust \textit{semantic anchors} that define the mappings between them. Aligned cross-topic representations achieve state-of-the-art results for the task of contextual word similarity. Furthermore, evaluation on NLP downstream tasks shows that multiple topic-based embeddings outperform single-prototype models. Authors: Eleftheria Briakou, Nikos Athanasiou, Alexandros Potamianos (University of Maryland, National Technical University of Athens, USC)

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