1 research outputs found
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages
Scarcity of data and technological limitations for resource-poor languages in
developing countries like India poses a threat to the development of
sophisticated NLU systems for healthcare. To assess the current status of
various state-of-the-art language models in healthcare, this paper studies the
problem by initially proposing two different Healthcare datasets, Indian
Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real
world Indian hospital query data in English and multiple Indic languages
(Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with
the query intents as well as entities. Our aim is to detect query intents and
extract corresponding entities. We perform extensive experiments on a set of
models in various realistic settings and explore two scenarios based on the
access to English data only (less costly) and access to target language data
(more expensive). We analyze context specific practical relevancy through
empirical analysis. The results, expressed in terms of overall F1 score show
that our approach is practically useful to identify intents and entities