Pegah Alipoormolabashi
2021
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Shikhar Singh
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Nuan Wen
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Yu Hou
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Pegah Alipoormolabashi
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Te-lin Wu
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Xuezhe Ma
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Nanyun Peng
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
2020
Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds
Pegah Alipoormolabashi
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Sabine Schulte im Walde
Proceedings of the The Fourth Widening Natural Language Processing Workshop
Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.
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Co-authors
- Sabine Schulte im Walde 1
- Shikhar Singh 1
- Nuan Wen 1
- Yu Hou 1
- Te-Lin Wu 1
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