Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning
Luis Espinosa-Anke, Jose Camacho-Collados, Sara Rodr{\'\i}guez-Fern{\'a}ndez, Horacio Saggion, Leo Wanner
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WordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending it manually is costly. One important type of relation which could potentially add enormous value to WordNet is the inclusion of collocational information, which is paramount in tasks such as Machine Translation, Natural Language Generation and Second Language Learning. In this paper, we present ColWordNet (CWN), an extended WordNet version with fine-grained collocational information, automatically introduced thanks to a method exploiting linear relations between analogous sense-level embeddings spaces. We perform both intrinsic and extrinsic evaluations, and release CWN for the use and scrutiny of the community.