A Review Paper on Part of Speech Tagger for Hindi (original) (raw)
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2012
Abstract-Named Entity Recognition (NER) is the task of processing text to identify and classify names, which is an important component in many Natural Language Processing (NLP) applications, enabling the extraction of useful information from documents. Basically NER is a two step process and used for many application like Machine Translation. Indian languages are free order, and highly inflectional and morphologically rich in nature. In this paper we describe the various approaches used for NER and summery on existing work done in different Indian Languages (ILs) using different approaches and also describe brief introduction about Hidden Markov Model And the Gazetteer method for NER. We also present some experimental result using Gazetteer method and HMM method that is a hybrid approach. Finally in the last the paper also describes the comparison between these two methods separately and then we combine these two methods so that performance of the system is increased.
Named Entity Recognition (NER) is the task of processing text to identify and classify names, which is an important component in many Natural Language Processing (NLP) applications, enabling the extraction of useful information from documents. Basically NER is a two step process and used for many application like Machine Translation. Indian languages are free order, and highly inflectional and morphologically rich in nature. In this paper we describe the various approaches used for NER and summery on existing work done in different Indian Languages (ILs) using different approaches and also describe brief introduction about Hidden Markov Model And the Gazetteer method for NER. We also present some experimental result using Gazetteer method and HMM method that is a hybrid approach. Finally in the last the paper also describes the comparison between these two methods separately and then we combine these two methods so that performance of the system is increased.
Named Entity Recognition in Punjabi Using Hidden Markov Model
Named Entity Recognition (NER) is a task to discover the Named Entities (NEs) in a document and then categorize these NEs into diverse Named Entity classes such as Name of Person, Location, River, Organization etc. Since, huge amount of work in NER has been done in English; so, we now need to concentrate ourselves in performing NER in the Indian languages (IL). As, Punjabi is not only the Indian language but also it is the official language of Punjab, So we have developed NER based system for Punjabi. This paper discusses about NER, approaches of NER and the results achieved by us by performing NER in Punjabi using Hidden Markov Model (HMM).
Hindi Named Entity Recognition By Aggregating Rule Based Heuristics and Hidden Markov Model
International Journal of Information Sciences and Techniques, 2012
Named entity recognition (NER) is one of the applications of Natural Language Processing and is regarded as the subtask of information retrieval. NER is the process to detect Named Entities (NEs) in a document and to categorize them into certain Named entity classes such as the name of organization, person, location, sport, river, city, country, quantity etc. In English, we have accomplished lot of work related to NER. But, at present, still we have not been able to achieve much of the success pertaining to NER in the Indian languages. The following paper discusses about NER, the various approaches of NER, Performance Metrics, the challenges in NER in the Indian languages and finally some of the results that have been achieved by performing NER in Hindi by aggregating approaches such as Rule based heuristics and Hidden Markov Model (HMM).
Identification and Classification of Named Entities in Indian Languages
The process of identification of Named Entities (NEs) in a given document and then there classification into different categories of NEs is referred to as Named Entity Recognition (NER). We need to do a great effort in order to perform NER in Indian languages and achieve the same or higher accuracy as that obtained by English and the European languages. In this paper, we have presented the results that we have achieved by performing NER in Hindi, Bengali and Telugu using Hidden Markov Model (HMM) and Performance Metrics.
HMM BASED POS TAGGER FOR HINDI
Part of Speech tagging in Indian Languages is still an open problem. We still lack a clear approach in implementing a POS tagger for Indian Languages. In this paper we describe our efforts to build a Hidden Markov Model based Part of Speech Tagger. We have used IL POS tag set for the development of this tagger. We have achieved the accuracy of 92%.