Learning Neural Word Salience Scores

Krasen Samardzhiev, Andrew Gargett, Danushka Bollegala


Abstract
Measuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. We propose Neural Word Salience (NWS) scores, unlike heuristics, are learnt from a corpus. Specifically, we learn word salience scores such that, using pre-trained word embeddings as the input, can accurately predict the words that appear in a sentence, given the words that appear in the sentences preceding or succeeding that sentence. Experimental results on sentence similarity prediction show that the learnt word salience scores perform comparably or better than some of the state-of-the-art approaches for representing sentences on benchmark datasets for sentence similarity, while using only a fraction of the training and prediction times required by prior methods. Moreover, our NWS scores positively correlate with psycholinguistic measures such as concreteness, and imageability implying a close connection to the salience as perceived by humans.
Anthology ID:
S18-2004
Volume:
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
*SEM | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–42
Language:
URL:
https://aclanthology.org/S18-2004
DOI:
10.18653/v1/S18-2004
Bibkey:
Cite (ACL):
Krasen Samardzhiev, Andrew Gargett, and Danushka Bollegala. 2018. Learning Neural Word Salience Scores. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 33–42, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Learning Neural Word Salience Scores (Samardzhiev et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-2004.pdf