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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15693
Title: An unsupervised approach for cause-effect relation extraction from biomedical text
Authors: Sharma R.
Palshikar G.
Pawar S.
Meziane F.
Silberztein M.
Atigui F.
Kornyshova E.
Metais E.
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract: Identification of Cause-effect (CE) relation mentions, along with the arguments, are crucial for creating a scientific knowledge-base. Linguistically complex constructs are used to express CE relations in text, mainly using generic causative (causal) verbs (cause, lead, result etc). We observe that some generic verbs have a domain-specific causative sense (inhibit, express) and some domains have altogether new causative verbs (down-regulate). Not every mention of a generic causative verb (e.g., lead) indicates a CE relation mention. We propose a linguistically-oriented unsupervised iterative co-discovery approach to identify domain-specific causative verbs, starting from a small set of seed causative verbs and an unlabeled corpus. We use known causative verbs to extract CE arguments, and use known CE arguments to discover causative verbs (hence co-discovery). Since causes and effects are typically agents, events, actions, or conditions, we use WordNet hypernym categories to identify suitable CE arguments. PMI is used to measure linguistic associations between a causative verb and its argument. Once we have a list of domain-specific causative verbs, we use it to extract CE relation mentions from a given corpus in an unsupervised manner, filtering out non-causative use of a causative verb using WordNet hypernym check of its arguments. Our approach extracts 256 domain-specific causative verbs from 10, 000 PubMed abstracts of Leukemia papers, and outperforms several baselines for extracting intra-sentence CE relation mentions. © 2018, Springer International Publishing AG, part of Springer Nature.
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (2018), 419- 427
URI: https://doi.org/10.1007/978-3-319-91947-8_43
http://repository.iitr.ac.in/handle/123456789/15693
Issue Date: 2018
Publisher: Springer Verlag
Keywords: Biomedical domain
Causative verbs
Cause-effect relation
Hypernyms
Leukemia
PMI
Relation extraction
ISBN: 9783319919461
ISSN: 3029743
Author Scopus IDs: 55582575200
55890466600
55654994400
Author Affiliations: Sharma, R., TCS Research, Tata Consultancy Services, Pune, India
Palshikar, G., TCS Research, Tata Consultancy Services, Pune, India
Pawar, S., TCS Research, Tata Consultancy Services, Pune, India
Corresponding Author: Sharma, R.; TCS Research, Tata Consultancy ServicesIndia; email: raksha.sharma1@tcs.com
Appears in Collections:Conference Publications [CS]

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