MayoClinicNLP–CORE: Semantic representations for textual similarity

Stephen Wu; Dongqing Zhu; Ben Carterette; Hongfang D Liu

MayoClinicNLP–CORE: Semantic representations for textual similarity

Stephen Wu, Dongqing Zhu, Ben Carterette, Hongfang D Liu

Quantitative Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence-level. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source-specific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.

Original language	English (US)
Title of host publication	*SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics
Publisher	Association for Computational Linguistics (ACL)
Pages	148-154
Number of pages	7
Volume	1
ISBN (Electronic)	9781937284480
State	Published - Jan 1 2013
Event	2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013 - Atlanta, United States Duration: Jun 13 2013 → Jun 14 2013

Other

Other	2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013
Country/Territory	United States
City	Atlanta
Period	6/13/13 → 6/14/13

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Theoretical Computer Science

Cite this

@inproceedings{52ed00683a8a43b3a5554ae216d2e0ee,

title = "MayoClinicNLP–CORE: Semantic representations for textual similarity",

abstract = "The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence-level. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source-specific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.",

author = "Stephen Wu and Dongqing Zhu and Ben Carterette and Liu, {Hongfang D}",

year = "2013",

month = jan,

day = "1",

language = "English (US)",

volume = "1",

pages = "148--154",

booktitle = "*SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics",

publisher = "Association for Computational Linguistics (ACL)",

note = "2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013 ; Conference date: 13-06-2013 Through 14-06-2013",

}

TY - GEN

T1 - MayoClinicNLP–CORE

T2 - 2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013

AU - Wu, Stephen

AU - Zhu, Dongqing

AU - Carterette, Ben

AU - Liu, Hongfang D

PY - 2013/1/1

Y1 - 2013/1/1

N2 - The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence-level. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source-specific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.

AB - The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence-level. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source-specific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.

UR - http://www.scopus.com/inward/record.url?scp=84958230417&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958230417&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84958230417

VL - 1

SP - 148

EP - 154

BT - *SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics

PB - Association for Computational Linguistics (ACL)

Y2 - 13 June 2013 through 14 June 2013

ER -

MayoClinicNLP–CORE: Semantic representations for textual similarity

Abstract

Other

ASJC Scopus subject areas

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