Semantic Textual Similarity with Siamese Neural Networks

Tharindu Ranasinghe; Constantin Orasan; Ruslan Mitkov

doi:10.26615/978-954-452-056-4_116

Semantic Textual Similarity with Siamese Neural Networks

Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Computer Science

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods

Original language	English
Title of host publication	Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
DOIs	https://doi.org/10.26615/978-954-452-056-4_116
Publication status	Published - Sep 2019
Event	Recent Advances in Natural Language Processing - Varna, Bulgaria Duration: 2 Sep 2019 → 4 Sep 2019 https://www.ranlp.org/archive/ranlp2019/start.php

Conference

Conference	Recent Advances in Natural Language Processing
Abbreviated title	RANLP 2019
Country/Territory	Bulgaria
City	Varna
Period	2/09/19 → 4/09/19
Internet address	https://www.ranlp.org/archive/ranlp2019/start.php

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10.26615/978-954-452-056-4_116

Cite this

@inproceedings{1d28333f654c4b19abd8443541b2b4cd,

title = "Semantic Textual Similarity with Siamese Neural Networks",

abstract = "Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods",

author = "Tharindu Ranasinghe and Constantin Orasan and Ruslan Mitkov",

year = "2019",

month = sep,

doi = "10.26615/978-954-452-056-4_116",

language = "English",

booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",

note = "Recent Advances in Natural Language Processing, RANLP 2019 ; Conference date: 02-09-2019 Through 04-09-2019",

url = "https://www.ranlp.org/archive/ranlp2019/start.php",

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TY - GEN

T1 - Semantic Textual Similarity with Siamese Neural Networks

AU - Ranasinghe, Tharindu

AU - Orasan, Constantin

AU - Mitkov, Ruslan

PY - 2019/9

Y1 - 2019/9

N2 - Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods

AB - Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods

UR - https://www.aclweb.org/anthology/R19-1116

U2 - 10.26615/978-954-452-056-4_116

DO - 10.26615/978-954-452-056-4_116

M3 - Conference publication

BT - Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

T2 - Recent Advances in Natural Language Processing

Y2 - 2 September 2019 through 4 September 2019

ER -

Semantic Textual Similarity with Siamese Neural Networks

Abstract

Conference

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