Learning to distinguish hypernyms and co-hyponyms
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 116624_53103
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers
- First page
- 2249
- Volume
- 0
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2014
- URL
-
http://aclweb.org/anthology/C14-1212
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Received 3rd place in best paper awards at the International Conference on Computational Linguistics (COLING), one of the leading (peer-reviewed) conferences for Natural Language Processing (NLP), with H-index 41 and acceptance rate of 31% in 2014. With numerous citations from scholars worldwide, published at venues including NeurIPS [1] and ACL [2], it led the way in applying supervised machine learning to distinguishing different semantic relations between words based on their distributional representations. It also contributed a large benchmark dataset to the field, which has been used in over 10 subsequent studies. Field-weighted citation impact 4.63 (Scopus).
[1] https://proceedings.neurips.cc/paper/2017/file/59dfa2df42d9e3d41f5b02bfc32229dd-Paper.pdf
[2] https://www.aclweb.org/anthology/N15-1098.pdf"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -