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ASOBEK at SemEval-2016 Task 1: Sentence representation with character N-gram embeddings for semantic textual similarity

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posted on 2023-06-09, 01:51 authored by Asli Eyecioglu, Bill Keller
A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While re- cent research focuses on word-based represen- tation for phrases, sentences and even paragraphs, this study considers an alternative approach based on character n-grams. We generate embeddings for character n-grams using a continuous-bag-of-n-grams neural network model. Three different sentence rep- resentations based on n-gram embeddings are considered. Results are reported for experi- ments with bigram, trigram and 4-gram em- beddings on the STS Core dataset for SemEval-2016 Task 1.

History

Publication status

  • Published

File Version

  • Published version

Journal

Proceedings of SemEval-2016, San Diego, California, June 16-17, 2016

ISSN

9781941643952

Publisher

Association for Computational Linguistics (ACL)

Page range

1320-1324

Book title

SemEval-2016: The 10th International Workshop on Semantic Evaluation: proceedings of the Workshop: June 16-17, 2016, San Diego, California, USA

Place of publication

Stroudsburg, PA

ISBN

9781941643952

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2016-06-23

First Compliant Deposit (FCD) Date

2016-06-23

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