Extending WordNet with fine-grained collocational information via supervised distributional learning

Comunicació presentada a COLING 2016: the 26th International Conference on Computational Linguistics; 2016 Dec. 11-16; Osaka (Japan)WordNet is probably the best known lexical resource in Natural Language Processing. While it/nis widely regarded as a high quality repository of concepts and semantic relations, updating and/nextending it manually is costly. One important type of relation which could potentially add enormous/nvalue toWordNet is the inclusion of collocational information, which is paramount in tasks/nsuch as Machine Translation, Natural Language Generation and Second Language Learning. In/nthis paper, we present ColWordNet (CWN), an extended WordNet version with fine-grained collocational/ninformation, automatically introduced thanks to a method exploiting linear relations/nbetween analogous sense-level embeddings spaces. We perform both intrinsic and extrinsic evaluations,/nand release CWN for the use and scrutiny of the community.This work was partially funded by the following projects: MULTISENSOR (FP7-ICT-610411),/nKRISTINA (H2020-645012-RIA), HARENES (FFI201130219-CO2-02), through a predoctoral grant/n(BES-2012-057036), the Mar´ıa de Maeztu Units of Excellence Program (MDM-2015-0502) and TUNER/n(TIN2015-65308-C5-5-R, MINECO/FEDER, UE). Jose Camacho-Collados is supported by a Google/nDoctoral Fellowship in Natural Language Processing