Integrated cTAKES for concept mention detection and normalization

Hongfang D Liu, Kavishwar Wagholikar, Siddhartha Jonnalagadda, Sunghwan Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We participated Task 1 using an existing system MedTagger implemented in inte-grated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup results into a machine learning framework for sequential labeling. Dictionary lookup results of MedLex and semantic vectors representing distributed semantics were used as features. Overall, the precision, recall, and F-measure of our best run for concept mention are 0.8, 0.573, and 0.668 respectively for strict evaluation and 0.939, 0.766, and 0.844 for relaxed evaluation. The accuracy of our best run for concept men-tion normalization is 54.6% and 87.0% for strict and relaxed mapping, respectively.

Original languageEnglish (US)
Title of host publicationCEUR Workshop Proceedings
PublisherCEUR-WS
Volume1179
StatePublished - 2013
Event2013 Working Notes for CLEF Conference, CLEF 2013 - Valencia, Spain
Duration: Sep 23 2013Sep 26 2013

Other

Other2013 Working Notes for CLEF Conference, CLEF 2013
CountrySpain
CityValencia
Period9/23/139/26/13

Fingerprint

Glossaries
Semantics
Labeling
Learning systems

Keywords

  • Conditional random fields
  • Dictionary lookup
  • Distributed semantics
  • Named entity recognition
  • Normalization

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Liu, H. D., Wagholikar, K., Jonnalagadda, S., & Sohn, S. (2013). Integrated cTAKES for concept mention detection and normalization. In CEUR Workshop Proceedings (Vol. 1179). CEUR-WS.

Integrated cTAKES for concept mention detection and normalization. / Liu, Hongfang D; Wagholikar, Kavishwar; Jonnalagadda, Siddhartha; Sohn, Sunghwan.

CEUR Workshop Proceedings. Vol. 1179 CEUR-WS, 2013.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, HD, Wagholikar, K, Jonnalagadda, S & Sohn, S 2013, Integrated cTAKES for concept mention detection and normalization. in CEUR Workshop Proceedings. vol. 1179, CEUR-WS, 2013 Working Notes for CLEF Conference, CLEF 2013, Valencia, Spain, 9/23/13.
Liu HD, Wagholikar K, Jonnalagadda S, Sohn S. Integrated cTAKES for concept mention detection and normalization. In CEUR Workshop Proceedings. Vol. 1179. CEUR-WS. 2013
Liu, Hongfang D ; Wagholikar, Kavishwar ; Jonnalagadda, Siddhartha ; Sohn, Sunghwan. / Integrated cTAKES for concept mention detection and normalization. CEUR Workshop Proceedings. Vol. 1179 CEUR-WS, 2013.
@inproceedings{acebdcb129d940efb0c90bfb6c098829,
title = "Integrated cTAKES for concept mention detection and normalization",
abstract = "We participated Task 1 using an existing system MedTagger implemented in inte-grated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup results into a machine learning framework for sequential labeling. Dictionary lookup results of MedLex and semantic vectors representing distributed semantics were used as features. Overall, the precision, recall, and F-measure of our best run for concept mention are 0.8, 0.573, and 0.668 respectively for strict evaluation and 0.939, 0.766, and 0.844 for relaxed evaluation. The accuracy of our best run for concept men-tion normalization is 54.6{\%} and 87.0{\%} for strict and relaxed mapping, respectively.",
keywords = "Conditional random fields, Dictionary lookup, Distributed semantics, Named entity recognition, Normalization",
author = "Liu, {Hongfang D} and Kavishwar Wagholikar and Siddhartha Jonnalagadda and Sunghwan Sohn",
year = "2013",
language = "English (US)",
volume = "1179",
booktitle = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",

}

TY - GEN

T1 - Integrated cTAKES for concept mention detection and normalization

AU - Liu, Hongfang D

AU - Wagholikar, Kavishwar

AU - Jonnalagadda, Siddhartha

AU - Sohn, Sunghwan

PY - 2013

Y1 - 2013

N2 - We participated Task 1 using an existing system MedTagger implemented in inte-grated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup results into a machine learning framework for sequential labeling. Dictionary lookup results of MedLex and semantic vectors representing distributed semantics were used as features. Overall, the precision, recall, and F-measure of our best run for concept mention are 0.8, 0.573, and 0.668 respectively for strict evaluation and 0.939, 0.766, and 0.844 for relaxed evaluation. The accuracy of our best run for concept men-tion normalization is 54.6% and 87.0% for strict and relaxed mapping, respectively.

AB - We participated Task 1 using an existing system MedTagger implemented in inte-grated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup results into a machine learning framework for sequential labeling. Dictionary lookup results of MedLex and semantic vectors representing distributed semantics were used as features. Overall, the precision, recall, and F-measure of our best run for concept mention are 0.8, 0.573, and 0.668 respectively for strict evaluation and 0.939, 0.766, and 0.844 for relaxed evaluation. The accuracy of our best run for concept men-tion normalization is 54.6% and 87.0% for strict and relaxed mapping, respectively.

KW - Conditional random fields

KW - Dictionary lookup

KW - Distributed semantics

KW - Named entity recognition

KW - Normalization

UR - http://www.scopus.com/inward/record.url?scp=84922041543&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922041543&partnerID=8YFLogxK

M3 - Conference contribution

VL - 1179

BT - CEUR Workshop Proceedings

PB - CEUR-WS

ER -