Exploiting Bi-LSTMs for Named Entity Recognition in Indian Culinary Science

Abstract

This paper discusses the use of Bidirectional LSTMs for recognition of Named Entities over the Indian Recipe Blogs. Recipe posts from popular blogs including Hebbar's Kitchen are harvested and trained for recognizing NEs. Both the word embeddings and character embeddings are utilized as feature vectors for training the Bi-LSTM. CRF model is used for joint decoding of the labels. The system shows a development data F1 score of 92.87% and test data F1 score of 94.66%. The dataset used and meta-results obtained are released freely for research use.