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.

中文翻译：

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基于深度神经网络的印地语命名实体识别模型

摘要

这项工作的目的是从给定的文本中开发有效的命名实体识别，从而提高使用自然语言处理（NLP）的系统的性能。基于物联网的设备（例如Alexa和Cortana）的性能在很大程度上取决于有效的NLP模型。为了增强智能物联网设备理解自然语言的能力，命名实体识别（NER）工具在这些设备中扮演着重要角色。通常，NER是一个两步过程，首先从文本中识别专有名词，然后将其分类为实体的预定义类别，例如人，位置，度量，组织和时间。NER通常在处理自然语言时作为子任务执行，从而提高了NLP任务的准确性。在本文中，我们基于卷积神经网络（CNN），双向长短期记忆（Bi-LSTM）神经网络和条件随机场（CRF），提出了一种深度神经网络架构，用于资源稀缺语言印地语的命名实体识别。在所提出的方法中，最初，我们使用跳跃语法word2vec模型和GloVe模型来表示语义向量中的词，这些词进一步在不同的基于深度神经网络的体系结构中使用。在提出的方法中，我们使用字符级和单词级嵌​​入来表示包含细粒度信息的文本。由于使用了字符级嵌入，因此所提出的模型对于词汇不足的单词具有鲁棒性。实验结果表明，Bi-LSTM的组合，