Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings
Špela Vintar, Larisa Grčić Simeunović, Matej Martinc, Senja Pollak, Uroš Stepišnik
Abstract
We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.- Anthology ID:
- 2020.bucc-1.5
- Volume:
- Proceedings of the 13th Workshop on Building and Using Comparable Corpora
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Reinhard Rapp, Pierre Zweigenbaum, Serge Sharoff
- Venue:
- BUCC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 29–34
- Language:
- English
- URL:
- https://aclanthology.org/2020.bucc-1.5
- DOI:
- Cite (ACL):
- Špela Vintar, Larisa Grčić Simeunović, Matej Martinc, Senja Pollak, and Uroš Stepišnik. 2020. Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings. In Proceedings of the 13th Workshop on Building and Using Comparable Corpora, pages 29–34, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings (Vintar et al., BUCC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.bucc-1.5.pdf