Finding Questions in Medical Forum Posts Using Sequence Labeling Approach

Adrianus Saga Ekakristi; Rahmad Mahendra; Mirna Adriani

doi:10.1007/978-3-031-23793-5_6

Finding Questions in Medical Forum Posts Using Sequence Labeling Approach

Adrianus Saga Ekakristi, Rahmad Mahendra, Mirna Adriani

Department of Computer Science

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

Abstract

Complex medical question answering system in medical domain receives a question in form of long text that need to be decomposed before further processing. This research propose sequence labeling approach to decompose that complex question. Two main tasks in segmenting complex question sentence are detecting sentence boundary with its type, and recognizing word that could be ignored in sentence. The proposed sequence labeling method achieves F1 score of 0.83 in detecting beginning sentence boundary and 0.93 when determining sentence type. When recognizing the word sequence that could be ignored in sentence, the sequence labeling method achieves F1 score of 0.90.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers
Editors	Alexander Gelbukh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	62-73
Number of pages	12
ISBN (Print)	9783031237928
DOIs	https://doi.org/10.1007/978-3-031-23793-5_6
Publication status	Published - 2023
Event	19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018 - Hanoi, Viet Nam Duration: 18 Mar 2018 → 24 Mar 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13396 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018
Country/Territory	Viet Nam
City	Hanoi
Period	18/03/18 → 24/03/18

Keywords

Chunking
Medical question answering
Question decomposition
Sequence labeling

Access to Document

10.1007/978-3-031-23793-5_6

Cite this

Ekakristi, A. S., Mahendra, R., & Adriani, M. (2023). Finding Questions in Medical Forum Posts Using Sequence Labeling Approach. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers (pp. 62-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13396 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23793-5_6

Ekakristi, Adrianus Saga ; Mahendra, Rahmad ; Adriani, Mirna. / Finding Questions in Medical Forum Posts Using Sequence Labeling Approach. Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. editor / Alexander Gelbukh. Springer Science and Business Media Deutschland GmbH, 2023. pp. 62-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Finding Questions in Medical Forum Posts Using Sequence Labeling Approach",

abstract = "Complex medical question answering system in medical domain receives a question in form of long text that need to be decomposed before further processing. This research propose sequence labeling approach to decompose that complex question. Two main tasks in segmenting complex question sentence are detecting sentence boundary with its type, and recognizing word that could be ignored in sentence. The proposed sequence labeling method achieves F1 score of 0.83 in detecting beginning sentence boundary and 0.93 when determining sentence type. When recognizing the word sequence that could be ignored in sentence, the sequence labeling method achieves F1 score of 0.90.",

keywords = "Chunking, Medical question answering, Question decomposition, Sequence labeling",

author = "Ekakristi, {Adrianus Saga} and Rahmad Mahendra and Mirna Adriani",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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Ekakristi, AS, Mahendra, R & Adriani, M 2023, Finding Questions in Medical Forum Posts Using Sequence Labeling Approach. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13396 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 62-73, 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Viet Nam, 18/03/18. https://doi.org/10.1007/978-3-031-23793-5_6

Finding Questions in Medical Forum Posts Using Sequence Labeling Approach. / Ekakristi, Adrianus Saga; Mahendra, Rahmad; Adriani, Mirna.
Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. ed. / Alexander Gelbukh. Springer Science and Business Media Deutschland GmbH, 2023. p. 62-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13396 LNCS).

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

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N2 - Complex medical question answering system in medical domain receives a question in form of long text that need to be decomposed before further processing. This research propose sequence labeling approach to decompose that complex question. Two main tasks in segmenting complex question sentence are detecting sentence boundary with its type, and recognizing word that could be ignored in sentence. The proposed sequence labeling method achieves F1 score of 0.83 in detecting beginning sentence boundary and 0.93 when determining sentence type. When recognizing the word sequence that could be ignored in sentence, the sequence labeling method achieves F1 score of 0.90.

AB - Complex medical question answering system in medical domain receives a question in form of long text that need to be decomposed before further processing. This research propose sequence labeling approach to decompose that complex question. Two main tasks in segmenting complex question sentence are detecting sentence boundary with its type, and recognizing word that could be ignored in sentence. The proposed sequence labeling method achieves F1 score of 0.83 in detecting beginning sentence boundary and 0.93 when determining sentence type. When recognizing the word sequence that could be ignored in sentence, the sequence labeling method achieves F1 score of 0.90.

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KW - Question decomposition

KW - Sequence labeling

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BT - Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers

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Ekakristi AS, Mahendra R, Adriani M. Finding Questions in Medical Forum Posts Using Sequence Labeling Approach. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing - 19th International Conference, CICLing 2018, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 62-73. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-23793-5_6