Abstract
This paper presents a method to employ unstructured texts of different domains for extracting entities from the closely related domains. To investigate the domain closeness, we trained our model in one domain and test in rest of the domains. The second challenge lies in the fact that if we retrain our model using a pair of closely related domains, we achieve better results while testing on texts from mixed genre in contrast to those individual genres. Working on the idea of domain adaptation, we carried out experiments with single-domain and mixed-domain training data and observed the precision, recall, and f-score of our entity extraction system. The performance of our system was optimal in a semi-supervised dataset with closely related or sister domains.
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Acknowledgements
The work is supported by Visvesvaraya Young Faculty Ph.D. Research Scheme, under Ministry of Electronics and Information Technology (MeitY), Media Lab Asia, Government of India.
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Maitra, P., Das, D. (2020). Employing Cross-genre Unstructured Texts to Extract Entities in Adapting Sister Domains. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_38
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DOI: https://doi.org/10.1007/978-981-13-7403-6_38
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