A deep neural network-based model for named entity recognition for Hindi language
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S.I. : APPLYING ARTIFICIAL INTELLIGENCE TO THE INTERNET OF THINGS
A deep neural network-based model for named entity recognition for Hindi language Richa Sharma1
· Sudha Morwal1 · Basant Agarwal2 · Ramesh Chandra3 · Mohammad S. Khan4
Received: 5 December 2019 / Accepted: 20 March 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends upon an efficient NLP model. To increase the capability of the smart IoT devices in comprehending the natural language, named entity recognition (NER) tools play an important role in these devices. In general, the NER is a two-step process that initially the proper nouns are identified from text and then classify them into predefined categories of entities such as person, location, measure, organization and time. NER is often performed as a subtask while processing natural languages which increases the accuracy level of a NLP task. In this paper, we propose deep neural network architecture for named entity recognition for the resource-scarce language Hindi, based on convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) neural network and conditional random field (CRF). In the proposed approach, initially, we use skip-gram word2vec model and GloVe model to represent words in semantic vectors which are further used in different deep neural network-based architectures. In the proposed approach, we use character- and word-level embedding to represent the text that includes information at fine-grained level. Due to the use of character-level embeddings, the proposed model is robust for the out-of-vocabulary words. Experimental results show that the combination of Bi-LSTM, CNN and CRF algorithms performs better as compared to the other baseline methods such as recurrent neural network, long short-term memory and Bi-LSTM individually. Keywords Neural networks · Machine learning · Sequence labeling · Deep learning · Convolutional neural network · Bi-LSTM
& Mohammad S. Khan [email protected] Richa Sharma [email protected] Sudha Morwal [email protected] Basant Agarwal [email protected] Ramesh Chandra [email protected] 1
Department of Computer Science and Engineering, Banasthali Vidyapith, Kota, India
2
Department of Computer Science and Engineering, Indian Institute of Information Technology Kota, Kota, India
3
Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, Norway
4
Department of Computing, East Tennessee State University, Johnson City, TN 37614-1266, USA
1 Introduction Humans communicate with smart IoT devices such as Alexa and Siri in natural language; thus, natural language processing is becoming an integral part of IoT devices [38–40]. To accomplish the straightforward human–computer inter
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