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        <title>Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning</title>
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        <namePart type="given">Luis</namePart>
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        <namePart type="given">Jose</namePart>
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    <name type="personal">
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        <namePart type="family">Rodríguez-Fernández</namePart>
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    <name type="personal">
        <namePart type="given">Horacio</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>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.</abstract>
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        <url>https://aclanthology.org/C16-1323</url>
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%0 Conference Proceedings
%T Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning
%A Espinosa-Anke, Luis
%A Camacho-Collados, Jose
%A Rodríguez-Fernández, Sara
%A Saggion, Horacio
%A Wanner, Leo
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F espinosa-anke-etal-2016-extending
%X 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.
%U https://aclanthology.org/C16-1323
%P 3422-3432

Markdown (Informal)

[Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning](https://aclanthology.org/C16-1323) (Espinosa-Anke et al., COLING 2016)

• Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning (Espinosa-Anke et al., COLING 2016)

ACL

• Luis Espinosa-Anke, Jose Camacho-Collados, Sara Rodríguez-Fernández, Horacio Saggion, and Leo Wanner. 2016. Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3422–3432, Osaka, Japan. The COLING 2016 Organizing Committee.