Sentence to sentence similarity : a review |
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Author: | Bounab, Yazid1; Seppänen, Jaakko1; Savusalo, Markus1; |
Organizations: |
1University of Oulu, Oulu, Finland |
Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.6 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202001202596 |
Language: | English |
Published: |
FRUCT,
2019
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Publish Date: | 2020-01-20 |
Description: |
AbstractThis paper suggests a novel sentence-to-sentence similarity measure. The proposal makes use of both word embedding and named-entity based semantic similarity. This is motivated by the increasing short text phrases that contain named-entity tags and the importance to detect various levels of hidden semantic similarity even in case of high noise ratio. The proposal is evaluated using a set of publicly available datasets as well as an in-house built dataset, while comparison with some state of art algorithms is performed. see all
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Series: |
Proceedings of Conference of Open Innovations Association FRUCT |
ISSN: | 2305-7254 |
ISSN-E: | 2343-0737 |
ISSN-L: | 2305-7254 |
ISBN: | 978-952-69244-0-3 |
Pages: | 439 - 443 |
Host publication: |
Proceedings of the FRUCT’25, Helsinki, Finland, 5-8 November 2019 |
Host publication editor: |
Balandin, S. Niemi, V. Tuytina, T. |
Conference: |
Conference of Open Innovations Association FRUCT |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Funding: |
This work is partly supported by CBC Karelia IoT Business Creation (2018-2020) and EU YoungRes (#823701) projects. |
Copyright information: |
© The Authors 2019. |