|
|
Sentence to sentence similarity : a review |
|---|---|
| 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
|
| 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
|
| 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. |