Keshava, V.Pravalika, P.Kamath S․, S.S.Geetha, V.2026-02-062017Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017, 2017, Vol.2018-January, , p. 127-130https://doi.org/10.1109/IALP.2017.8300562https://idr.nitk.ac.in/handle/123456789/31818Domain-specific taxonomies constitute a valuable resource as they offer extensive support in information retrieval related activities like browsing, searching, recommendations and personalization. Such taxonomies can bridge the gap between the lack of domain-specific querying knowledge in potential users and the actual content. In case of multilingual content, taxonomies can play a pivotal role in boosting search performance for content across language barriers. In this paper, a domain-agnostic framework for building an evolving, domain-specific taxonomy for the Hindi, given a set of well-organized data points is proposed. The approach is intended for designing a hierarchical taxonomy enriched with synonyms and other morphological variants using WordNet and Word2vec models respectively. The hierarchical structure acts as a base which binds the taxonomy to a given domain. Such enrichment can improve taxonomy coverage within the given domain. The focus is also on building a taxonomy that can self-evolve over time, with high precision and recall, with minimal manual effort. © 2017 IEEE.Asian language processingnatural language processingsemantic processingtaxonomies