Keshava, V.Avvara, P.Sowmya, Kamath S.Geetha, V.2020-03-302020-03-302018Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017, 2018, Vol.2018-January, , pp.127-130https://idr.nitk.ac.in/jspui/handle/123456789/7542Domain-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.Book chapter