Abstract
This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are promoted to a proposed model with the integration of CNN and conditional random field (CRF) layers. After a comparative analysis of results, it is verified that the performance of the proposed model (i.e., Bi-LSTM-CNN-CRF) is impressive. The proposed system achieves 61% precision, 56% recall, and 58% F-measure.
Highlights
Language is a necessary entity for communication
This paper presents a hybrid model which is a combination of BiLSTM-convolutional neural network (CNN)-conditional random field (CRF) for implementation of Hindi Named Entity Recognition (NER)
Experiments are run on recurrent neural network (RNN)-CRF, long short-term memory (LSTM)-CRF and bi-directional long short-term memory (Bi-LSTM)-CRF models
Summary
Language is a necessary entity for communication. To easiness the way of human-computer interaction, it is preferred for machines to comprehend natural languages. Several machine learning based methods have been used to implement NER including hidden markov model (Chopra, Joshi, & Mathur, 2016; Morwal, Jahan, & Chopra, 2012), support vector machine (Ekbal & Bandyopadhyay, 2010), conditional random field (Ekbal & Bandyopadhyay, 2009) and combination of these methods These methods rely on an intensive knowledge of grammar and handcrafted features. Research motive of this experiment is to observe the efficiency of deep learning based techniques on Hindi NER without using language specific features and linguistic resources.
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