Abstract: Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational knowledge that is implicitly encoded in word embeddings can be extracted in a reliable way. In this paper, we propose two probabilistic models to address this issue. The first model is based on the common relations-as-translations view, but is cast in a probabilistic setting. Our second model is based on the much weaker assumption that there is a linear relationship between the vector representations of related words. Compared to existing approaches, our models lead to more accurate predictions, and they are more explicit about what can and cannot be extracted from the word embedding.
About COLING: COLING is among the top-tier conferences in NLP. The 27th International Conference on Computational Linguistics (COLING 2018) will take place in Santa Fe, New-Mexico, USA. COLING 2018 will be held at the Santa Fe Community Convention Center from August 20th through 26th 2018.
Authors: Zied Bouraoui, Shoaib Jameel, and Steven Schockaert
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