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Named Entity Recognition System for Kashmiri Language Iamir Bashir Malik, Iikhushboo Bansal Istudent, M.Tech, Iiassistant Professor I,Iidept

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Named Entity Recognition System for Kashmiri Language Iamir Bashir Malik, Iikhushboo Bansal Istudent, M.Tech, Iiassistant Professor I,Iidept ISSN : 2347 - 8446 (Online) International Journal of Advanced Research in ISSN : 2347 - 9817 (Print) Vol. 3, Issue 2 (Apr. - Jun. 2015) Computer Science & Technology (IJARCST 2015) Named Entity Recognition System for Kashmiri Language IAmir Bashir Malik, IIKhushboo Bansal IStudent, M.Tech, IIAssistant Professor I,IIDept. of CSE, Desh Bhagat University, Mandi Gobindgarh, Punjab, India Abstract Named Entity Recognition (NER) is a task which helps in finding out Persons name, Location names, Organization names, Place, Date, Time etc. and classifies them into predefined different categories. Named Entity Recognition plays a major role in various Natural Language Processing (NLP) fields like Information Extraction, Machine Translations and Question Answering. Unfortunately Kashmiri language which is a scarce resourced language has not been taken into account. This paper describes the problems of NER in the context of Kashmiri Language and provides relevant solutions. Keywords Named Entity, Named Entity Recognition, Natural language process, Kashmiri language text. I. Introduction is as follows. The term Named Entity (NE) was evolved during the sixth (1) “Micromax”represent anorganization and “ Dec19, 2014” Message Understanding Conference (MUC -6, 1995).Named represent dateand “smartphone” represent entity and “had Entity Recognition (NER) is also knows as entity identification is a launched its on” represent others. subtask of information extraction (IE). NER extracts and classifies The named entities may be of any type such as given below in the true Named Entities in text. NER system is widely used in a table. different tasks of Natural Language Processing (NLP) and in many commercial applications on internet like Search Engine Table 1: Different named entities .Named Entity Recognition (NER) is a process of searching the S.NO NE Tag DEFINITION (example) text to detect entities in a text and to classify them into predefined 01 ORG Name of organization (Micromax) classes such as the names of persons, organizations, locations, date, time, Designations, Measures, , abbreviations and brand 02 PER Name of person (Amir) etc. Construction of a Named Entity 03 COUNTRY Name of Country (India) Recognition (NER) system becomes challenging if proper 04 OTHER Not a named entity resources are not available. Gazetteer lists are often used for the development of NER systems In many resource-poor languages like kashmiri gazetteer lists of proper size are not available, II. Literature Survey but sometimes relevant lists are available in English. 1. Amarappa and Sathyanarayana, 2012, came up with a paper In Indian languages kashmiri is a most popular language in on „Named Entity Recognition and Classification (NERC) in northern part of India. Kashmiri languagethe current number of Kannada language’, that built a SEMI-Automatic Statistical its speakers will be around four million. Kashmiri is also spoken Machine Learning NLP models based on noun taggers using by Kashmiris settled in other parts of India, and other countries. HMM. Kashmiri language belongs to the Dardic sub-group of the Indo- The challenges and issues faced for Kannada language are listed Aryan group of languages. by them are 1. No capitalization NER based approaches are shown in fig1 given below. 2. High phonetic characteristic of Brahmi script. 3. Non-availability of large gazetteer lists MICROSOFT APPLE NOKIA 4. Lack of standardization and spelling 5. Number of frequently used words (common nouns). ORGANIZATION Their proposed NER system for Kannada receives the HUMAN MUSLIM unannotated text file containing the Kannada document, FOOD ENTITY NER COMMUNITY HINDU recognizes the NE‟s and generates an annotated text document CURRENCY SIKH file. Further the output of NERC system is subjected to a suitable cryptographic algorithm to secure the structured corpus. They NUMERIC OTHERS came up with 13 noun taggers for NER like person name (NNP), location name (NNL), organization name (NNO),etc. Hidden DATE TIME PERCENT Markov Model (HMM) is a supervised learning technique and a statistical model with generalized learning method. It is used Fig. 1 : A Named Entity recognition split into more specific Named to develop a NER with symbolic, statistical, connectionist and Entities hybrid natures. NE‟s and NE Tags are defined with examples in this paper. For example consider the English sentence like: 2. Kaur and Vishal Gupta, 2012, built a „NER for Punjabi‟ Micromaxhad launched its first smartphone on Dec 19, 2014. using rule based and list look up approaches. As mentioned earlier, After performing the named entities on these sentences the result Punjabi is also a language with high clung and inflections, which www.ijarcst.com 209 © All Rights Reserved, IJARCST 2013 International Journal of Advanced Research in ISSN : 2347 - 8446 (Online) Computer Science & Technology (IJARCST 2015) Vol. 3, Issue 2 (Apr. - Jun. 2015) ISSN : 2347 - 9817 (Print) leads to linguistic problems. The rule based approach trained for Punjabi Language”. International Journal of Computer the system to identify NEs by writing rules manually for all Science and Information Technology&Security (IJCSITS), NE features. The most common words are removed from ISSN: 2249-9555 Vol. 2, No.3, June 2012. the database, and then a list look up approach is used with [06] Yungwei ding hsinhsi Chen and ShihchungTsaI, “Named the Gazetteer's lists to classify the identified NEs. Their system entity extraction for information retrieval”. Proc. of HLT- resulted with 85.88% f-measure. NAACL. 3. PrakashHiremath, Shambhavi B. R, 2014, Named Entity [07] http://en.wikipedia.org/wiki/Urdu Accessed on March Recognition (NER) is subtask of information extraction that 2012 seeks to locate and classify the elements in some text into [08] www.bbc.co.uk/urdu/ Accessed on March-May 2012 pre-defined categories. NER finds its application in Natural [09] Pallavi, Dr. Anitha S Pillai. “Named Entity Recognition Language Processing tasks like machine translation, question- for Indian Languages: A Survey”. International Journal of answering systems and automatic summarization. The approaches Engineering and Advanced Technology (IJEAT) ISSN: 2277 to NER are rule based, statistics based or a combination of both. In 128X, Volume 3, November 2013 this paper, we present a survey of these various approaches for [10] Surya Bahadur Bam, TejBahadurShahi,” Named Entity identification of Names Entities (NE) in Indian Languages. Recognition for Nepali Text Using Support Vector Machines”. 4. UmrinderPal Singh, Vishal Goyal, 2014, built a „NER for Intelligent Information Management Published March 2014 Urdu‟ using rule based approaches. This paper describes the in S ci R es. problems of NER in the context of Urdu Language and provides [11] NavneetKaurAulakh, Er.YadwinderKaur. “Review Paper relevant solutions. The system is developed to tag thirteen different on Name Entity Recognition of Machine Translation”. Named Entities (NE), twelve NE proposed by IJCNLP-08 and International Journal of Advanced Research in Computer Izaafats. Science and Software Engineering ISSN: 2277 128X Volume 4, April 2014 III. Issues in Kashmiri NER System [12] PrakashHiremath, Shambhavi B. R. “Approaches to Named • Non-availability of resources Entity Recognition in Indian Languages”. International • Language Resources are must for any approach whether it Journal of Engineering and Advanced Technology (IJEAT) is Rule Based or Statistical. There is no large gazetteer and ISSN: 2249 – 8958, Volume-3, August 2014. annotated data available for Kashmiri language. Kashmiri language is written from right to left. • One major issue with Kashmiri language is that it requires language experts. • The training and testing for Kashmiri language is difficult task for the person who is not language expert of Kashmiri. • No Kashmiri language conversion in Google translator. • 05. No inbuilt knowledge base. IV. Conclusion and Future Work In this work, the method for extracting named entities from data of various domains has been presented which is a system useful in the identification and classification of names. The work for Kashmiri NER is very complex due to the nature of Kashmiri language which is in free order and lacks of research work in Kashmiri text. References [01] Joel , N. (2008) Learning NER from Wikipedia. [1] Pramod Kumar Gupta and SunitaArora(2009) “An Approach for Named Entity Recognition System for Hindi”: An Experimental Study In Proceedings of ASCNT CDAC, Noida, India, pp. 103 – 108. [02] DarvinderKaur, Vishal Gupta, ―A survey of Named Entity Recognition in English and other Indian Languages, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November 2010. [03] Riaz K. Rule-based named entity recognition in Urdu. In Proceedings of the Named Entities Workshop. Pages 126- 135.2010 [04] Vishal Gupta, Gurpreet Singh Lehal, “Named Entity Recognition for Punjabi Language Text Summarization”. International Journal of ComputerApplications (0975 – 8887) Volume 33– No.3, November 2011. [05] KamaldeepKaur, Vishal Gupta.“Name Entity Recognition © 2013, IJARCST All Rights Reserved 210 www.ijarcst.com.
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