Espinosa-Anke, L, Camacho-Collados, J, Rodríguez-Fernández, S, Saggion, H, Wanner, L. Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning. 26th International Conference on Computational Linguistics (COLING 2016)
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
|
Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
|
Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
|
UbicaLab |
Wireless Networking |
Educational Technologies |
Espinosa-Anke, L, Camacho-Collados, J, Rodríguez-Fernández, S, Saggion, H, Wanner, L. Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning. 26th International Conference on Computational Linguistics (COLING 2016)
Espinosa-Anke, L, Camacho-Collados, J, Rodríguez-Fernández, S, Saggion, H, Wanner, L. Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning. Coling 2016.
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.
Additional material: