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.
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Sharma, R., Palshikar, G., Pawar, S. (2018). An Unsupervised Approach for Cause-Effect Relation Extraction from Biomedical Text. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_43
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DOI: https://doi.org/10.1007/978-3-319-91947-8_43
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