Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature

Jimmy Laishram, Kishorjit Nongmeikapam, Sudip Naskar

Abstract
The recognition task of Multi-Word Named Entities (MNEs) in itself is a challenging task when the language is inflectional and agglutinative. Having breakthrough NLP researches with deep neural network and language modelling techniques, the applicability of such techniques/algorithms for Indian language like Manipuri remains unanswered. In this paper an attempt to recognize Manipuri MNE is performed using a Long Short Term Memory (LSTM) recurrent neural network model in conjunction with Part Of Speech (POS) embeddings. To further improve the classification accuracy, word cluster information using K-means clustering approach is added as a feature embedding. The cluster information is generated using a Skip-gram based words vector that contains the semantic and syntactic information of each word. The model so proposed does not use extensive language morphological features to elevate its accuracy. Finally the model’s performance is compared with the other machine learning based Manipuri MNE models.
Anthology ID:
2020.icon-main.57
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2020
Address:
Indian Institute of Technology Patna, Patna, India
Editors:
Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
420–429
Language:
URL:
https://aclanthology.org/2020.icon-main.57
DOI:
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Cite (ACL):
Jimmy Laishram, Kishorjit Nongmeikapam, and Sudip Naskar. 2020. Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 420–429, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature (Laishram et al., ICON 2020)
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