Named Entity Recognition (NER) is a tool based on principles of Artificial Intelligence (AI) and Natural Language Processing (NLP) for automatically tagging Named Entities from unstructured text. Named Entities, which are generally proper nouns, can be the name of a person, organization, location etc. Named Entity Recognition is a very crucial tool of Natural Language Processing. Some of the application areas of NER include- Information Extraction and Retrieval, Machine Translation, Text Summarization etc. This paper analyses different approaches used in the NER of Indian languages, with emphasis on Hindi. The study compares the different approaches for NER viz. Machine Learning (ML), Rule-based and Hybrid. This study is orchestrated to provide gap analysis in available NER systems for Indian Languages, especially Hindi. It is because existing NER systems are evaluated for accuracies based on predefined datasets, which are not providing universal results on any dataset. Also, there is no standard dataset available for fair comparison of accuracies of Indian Languages. Furthermore, we concentrated our scope to Hindi as it is the official language of India and representative to other Indo-Aryan languages having similar structure, making it a generalized solution. Moreover, the scope of Named Entity tags are limited. Whereas, there is a scope of further classification of Named Entities. As future work, we would like to develop an efficient system, in terms of accuracy, and catering to more Named Entity tags than available presently in Hindi NER tools.

Named Entity Recognition: A Survey for Indian Languages

Named Entity Recognition (NER) is an application of Natural Language Processing (NLP). NER is a activity of Information Extraction. NER is a task used for automated text processing for various industries, key concept for academics, artificial intelligence, robotics, Bioinformatics and many more. NER is always essential when dealing with chief NLP activity such as machine translation, question-answering, document summarization etc. Most NER work has been done for other European languages. Among Indian constitutional languages, NER work has been done for few languages. Not enough work is possible due to some challenges such as lack of resources, ambiguity in language, morphologically rich and many more. In this paper, we found many challenges available in NER for Indian languages and compared by measuring standard evaluation metrics values of accuracy, precision, recall and F-measure.

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A Survey on Various Approach used in Named Entity Recognition for Indian Languages

21st century is far more advanced than the 20th century because of its new innovations along with the relevant technological mappings. Technology makes our day to day work easy. However, this has been led our simple life to be very complex. We have become really busy, money minded and most importantly we don't have time to spend with our families or thinking about ourselves. As Millennials form our childhood what we have experienced is the stress to be the best. The competition which has been generated around and among us cannot be handled; so, people have been depressed and this would let them even committing suicide. Therefore, a Learning Assistant for advanced level students, which has been named as “Shilpa”, would be a practical remedy and a companion to overcome such difficulties. Shilpa has been stepped forward to monitor students' stress levels and to help them understand their weak areas considering the curriculum of a particular subject. Once identifying a particularly weak area, Shilpa navigates the user to the summarized version of a weakly identified content of a lesson in which the user doesn't have to go through the entire course curriculum to improve his/her weak areas. The implemented system has been tested considering all the novel components, and an overall value of 0.81 has been experimented as per the precision. It can be concluded that this novel approach has achieved an overall 81% accuracy over the existing state-of-the-art baselines.

Shilpa: A Novel Neural Based Approach for Measuring Human Stress Level

BPS-Statistics Indonesia, as an official data producer, puts data quality as a top priority. Public acceptance and trust in data reflect data reliability which is one of the data quality indicators. The existing survey that collects user acceptance, trust, and perspective to data produced by BPS can only reach data users in a limited number. Perspectives from a large number of data users cannot be captured. This research aims to build an application that collects and provides users’ perspectives and sentiment to official statistics sourced from online news. One feature in this application is Named Entity Recognition, which extracts public perspectives in entities such as names, organizations, statistical indicators, quotes or opinions, etc. This application objectively measures the sentiment of news discussing or citing statistical indicators. This application also facilitates BPS to do social network analysis to understand the relationships between fellow data users for each statistical indicator. The implicit goal is to effectively provide insights into how frequently society uses and refers to statistical indicators produced by BPS in any domain and their perspective on data. All models and features provided in this application have been evaluated based on standard performance metrics.

Development of Application Providing Public’s Perspectives on Official Statistical Indicators

Named Entity Recognition (NER) is a method to search for a particular Named Entity (NE)[1] from a file or an image, recognize it and classify it into specified Entity Classes like Name, Location, Organization, Numbers and Others Categories. It is the most useful element of the technique known as Natural Language Processing (NLP) which makes text extraction very easy [2]. In this paper, we focus on using Hidden Markov Model (HMM) based techniques to recognize the Named Entity (NE) for Gujarati language. The main aim of using HMM is that it provides better performance and can be easily implemented for any languages. A remarkable amount of work has been carried out for many languages like English, Greek, Chinese etc. But, still a wide scope is open for Indian Origin Languages like Hindi, Gujarati, Devanagari etc. As Gujarati is not only the Indian Language, but a language that is most spoken in Gujarat. Thus, in this paper, we emphasis on proposing a NER based scheme for Gujarati Language using HMM.

Named entity recognition and classification for Gujarati language

Optical character recognition has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. At present, there is great demand of such softwares which can identify the characters from scanned documents or images. Optical Character Recognition deals with the problem of recognizing optically processed characters. Optical recognition is performed off-line after the writing or printing has been completed, as opposed to on-line recognition where the computer recognizes the characters as they are drawn. This paper presents the system which identifies characters from the images. The objective of this system prototype are to develop a prototype for the Optical Character Recognition (OCR) system and to implement the Template Matching algorithm in developing the system prototype.

Komil Vora

Papers

Named entity recognition and classification for Gujarati language

Design and Implementation of OCR to identify English Characters and Numbers