UWB at SemEval-2016 Task 1: Semantic textual similarity using lexical, syntactic, and semantic information

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43929278" target="_blank" >RIV/49777513:23520/16:43929278 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

UWB at SemEval-2016 Task 1: Semantic textual similarity using lexical, syntactic, and semantic information

Original language description

We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods benefit from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked first among 26 systems.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Workshop SemEval 2016

ISBN

978-1-941643-95-2

ISSN

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e-ISSN

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Number of pages

7

Pages from-to

588-594

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

San Diego

Event date

Jun 16, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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