2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 2016
Machine Translation may be defined as the task of transformation of source text from one language... more Machine Translation may be defined as the task of transformation of source text from one language to another. In the following paper, we have discussed the improvement in quality of Machine Translation (MT) using Source text Rewriting. We have performed English to Hindi translation on our MT system and also translation of rewritten English text to Hindi and then compared their performances and evaluated MT system based on 11 features sets as well as using automatic evaluation metrics such as BLEU, METEOR and F-Measure. We found that the performance of MT improved by using Text Rewriting approach.
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
2015 International Conference on Advances in Computer Engineering and Applications, 2015
Machine Translation is one of the research fields of Computational Linguistics. The objective of ... more Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.
Engineering, Technology & Applied Science Research, 2018
Machine translation (MT) has been a topic of great research during the last sixty years, but, imp... more Machine translation (MT) has been a topic of great research during the last sixty years, but, improving its quality is still considered an open problem. In the current paper, we will discuss improvements in MT quality by the use of the ensemble approach. We performed MT from English to Hindi using 6 MT different engines described in this paper. We found that the quality of MT is improved by using a combination of various approaches as compared to the simple baseline approach for performing MT from source to target text.
Machine translation (MT) is considered an important task that can be used to attain information f... more Machine translation (MT) is considered an important task that can be used to attain information from documents written in different languages. In the current paper, we discuss different approaches of MT, the problems faced in MT in Indian languages, the limitations of some of the current existing MT systems, and present a review of the work that has been done until now in MT in Indian language perspective.
Named Entity Recognition (NER) is the task in which proper nouns in a given document are discover... more Named Entity Recognition (NER) is the task in which proper nouns in a given document are discovered and then categorized into respective classes. The various classes of proper nouns may be name of location, name of person, Organization, River, Quantity, Time, Percentage etc. Today, there is a great need to perform NER in the Indian Languages, since not much work has been done in the field of Information retrieval in the Indian languages. In this paper, we have tried to explain NER, different approaches of NER and finally some results of NER in natural languages.
Abstract-Named Entity Recognition (NER) is the task of processing text to identify and classify n... more 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 a task to discover the Named Entities (NEs) in a document and t... more 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).
Named Entity Recognition (NER) is the task of processing text to identify and classify names, whi... more 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.
International Journal on Natural Language Computing, 2012
Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the b... more Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific.
International Journal on Computational Science & Applications, 2013
Named Entity Recognition (NER) is one of the tasks of processing documents to detect and categori... more Named Entity Recognition (NER) is one of the tasks of processing documents to detect and categorize proper nouns, which is known to be a useful component in various Natural Language Processing (NLP) applications, that enable the extraction of important information from texts very easily. In the following paper we have discussed about NER, various approaches of NER and the results obtained by performing NER in English using Hidden Markov Model (HMM).
International Journal on Natural Language Computing, 2013
Transliteration may be defined as the alteration of text from one language to another. In this pa... more Transliteration may be defined as the alteration of text from one language to another. In this paper, we have discussed how transliteration is useful in handling unknown words in Named Entity Recognition (NER). We have shown some of our results on unknown words handling in NER using transliteration.
International Journal on Natural Language Computing, 2013
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
International Journal of Information Sciences and Techniques (IJIST), 2012
Named entity recognition (NER) is one of the applications of Natural Language Processing and is r... more 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).
Quality improvement of Machine Translation (MT) is one of the open topics of research today. Thes... more Quality improvement of Machine Translation (MT) is one of the open topics of research today. These days, there are many machine translators available online. But, these machine translators lack in the quality of translation when the input sentence is complex and consists of many Named Entities which are not identified correctly. In this paper, we address this problem by means of investigating a new ensemble approach that can help in improving the quality of translation from English to Hindi and Hindi to English. We have developed 6 MT systems that can perform English to Hindi Translation and 6 MT Systems that can perform Hindi to English Translation. We have developed Ensemble MT Systems by combining Statistical Machine Translation (SMT) with Source Text Rewriting and Named Entity Translation. We have compared translation using baseline approach (SMT) with the ensemble approach and have shown that quality of translation improves when we use ensemble approach for English-Hindi and Hi...
International Conference on Computing, Communication & Automation, 2015
Text Simplification is the combination of Lexical Simplification and Syntactic Simplification. In... more Text Simplification is the combination of Lexical Simplification and Syntactic Simplification. In this paper, we have compared baseline approach of Text Simplification with classifier based approach of Text Simplification in improving the quality of English-Hindi Machine Translation. We have compared both approaches using Human Judgment and using Automatic measures BLEU, F-measure and Meteor.
International Journal of Information Sciences and Techniques, 2012
Named entity recognition (NER) is one of the applications of Natural Language Processing and is r... more 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).
International Journal on Natural Language Computing, 2013
Named Entity Recognition (NER) is considered as one of the key task in the field of Information R... more Named Entity Recognition (NER) is considered as one of the key task in the field of Information Retrieval. NER is the method of recognizing Named Entities (NEs) in a corpus and then organizing these NEs into diverse classes of NEs e.g. Name of Location, Person, Organization, Quantity, Time, Percentage etc. Today, there is a great need to develop a tool for NER, since the existing tools are of limited scope. In this paper, we would discuss the functionality and features of our tool of NER with some experimental results.
International Journal on Natural Language Computing, 2013
The process of identification of Named Entities (NEs) in a given document and then there classifi... more 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.
2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 2016
Machine Translation may be defined as the task of transformation of source text from one language... more Machine Translation may be defined as the task of transformation of source text from one language to another. In the following paper, we have discussed the improvement in quality of Machine Translation (MT) using Source text Rewriting. We have performed English to Hindi translation on our MT system and also translation of rewritten English text to Hindi and then compared their performances and evaluated MT system based on 11 features sets as well as using automatic evaluation metrics such as BLEU, METEOR and F-Measure. We found that the performance of MT improved by using Text Rewriting approach.
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
2015 International Conference on Advances in Computer Engineering and Applications, 2015
Machine Translation is one of the research fields of Computational Linguistics. The objective of ... more Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.
Engineering, Technology & Applied Science Research, 2018
Machine translation (MT) has been a topic of great research during the last sixty years, but, imp... more Machine translation (MT) has been a topic of great research during the last sixty years, but, improving its quality is still considered an open problem. In the current paper, we will discuss improvements in MT quality by the use of the ensemble approach. We performed MT from English to Hindi using 6 MT different engines described in this paper. We found that the quality of MT is improved by using a combination of various approaches as compared to the simple baseline approach for performing MT from source to target text.
Machine translation (MT) is considered an important task that can be used to attain information f... more Machine translation (MT) is considered an important task that can be used to attain information from documents written in different languages. In the current paper, we discuss different approaches of MT, the problems faced in MT in Indian languages, the limitations of some of the current existing MT systems, and present a review of the work that has been done until now in MT in Indian language perspective.
Named Entity Recognition (NER) is the task in which proper nouns in a given document are discover... more Named Entity Recognition (NER) is the task in which proper nouns in a given document are discovered and then categorized into respective classes. The various classes of proper nouns may be name of location, name of person, Organization, River, Quantity, Time, Percentage etc. Today, there is a great need to perform NER in the Indian Languages, since not much work has been done in the field of Information retrieval in the Indian languages. In this paper, we have tried to explain NER, different approaches of NER and finally some results of NER in natural languages.
Abstract-Named Entity Recognition (NER) is the task of processing text to identify and classify n... more 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 a task to discover the Named Entities (NEs) in a document and t... more 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).
Named Entity Recognition (NER) is the task of processing text to identify and classify names, whi... more 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.
International Journal on Natural Language Computing, 2012
Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the b... more Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific.
International Journal on Computational Science & Applications, 2013
Named Entity Recognition (NER) is one of the tasks of processing documents to detect and categori... more Named Entity Recognition (NER) is one of the tasks of processing documents to detect and categorize proper nouns, which is known to be a useful component in various Natural Language Processing (NLP) applications, that enable the extraction of important information from texts very easily. In the following paper we have discussed about NER, various approaches of NER and the results obtained by performing NER in English using Hidden Markov Model (HMM).
International Journal on Natural Language Computing, 2013
Transliteration may be defined as the alteration of text from one language to another. In this pa... more Transliteration may be defined as the alteration of text from one language to another. In this paper, we have discussed how transliteration is useful in handling unknown words in Named Entity Recognition (NER). We have shown some of our results on unknown words handling in NER using transliteration.
International Journal on Natural Language Computing, 2013
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
Transliteration may be defined as the process of mapping sounds in a text written in one language... more Transliteration may be defined as the process of mapping sounds in a text written in one language to another language. Current paper discusses about transliteration and its use in Named Entity Recognition. We have designed a code that executes Transliteration and assist in the process of Named Entity Recognition. We have presented some of the results of Named Entity Recognition (NER) using Transliteration.
International Journal of Information Sciences and Techniques (IJIST), 2012
Named entity recognition (NER) is one of the applications of Natural Language Processing and is r... more 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).
Quality improvement of Machine Translation (MT) is one of the open topics of research today. Thes... more Quality improvement of Machine Translation (MT) is one of the open topics of research today. These days, there are many machine translators available online. But, these machine translators lack in the quality of translation when the input sentence is complex and consists of many Named Entities which are not identified correctly. In this paper, we address this problem by means of investigating a new ensemble approach that can help in improving the quality of translation from English to Hindi and Hindi to English. We have developed 6 MT systems that can perform English to Hindi Translation and 6 MT Systems that can perform Hindi to English Translation. We have developed Ensemble MT Systems by combining Statistical Machine Translation (SMT) with Source Text Rewriting and Named Entity Translation. We have compared translation using baseline approach (SMT) with the ensemble approach and have shown that quality of translation improves when we use ensemble approach for English-Hindi and Hi...
International Conference on Computing, Communication & Automation, 2015
Text Simplification is the combination of Lexical Simplification and Syntactic Simplification. In... more Text Simplification is the combination of Lexical Simplification and Syntactic Simplification. In this paper, we have compared baseline approach of Text Simplification with classifier based approach of Text Simplification in improving the quality of English-Hindi Machine Translation. We have compared both approaches using Human Judgment and using Automatic measures BLEU, F-measure and Meteor.
International Journal of Information Sciences and Techniques, 2012
Named entity recognition (NER) is one of the applications of Natural Language Processing and is r... more 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).
International Journal on Natural Language Computing, 2013
Named Entity Recognition (NER) is considered as one of the key task in the field of Information R... more Named Entity Recognition (NER) is considered as one of the key task in the field of Information Retrieval. NER is the method of recognizing Named Entities (NEs) in a corpus and then organizing these NEs into diverse classes of NEs e.g. Name of Location, Person, Organization, Quantity, Time, Percentage etc. Today, there is a great need to develop a tool for NER, since the existing tools are of limited scope. In this paper, we would discuss the functionality and features of our tool of NER with some experimental results.
International Journal on Natural Language Computing, 2013
The process of identification of Named Entities (NEs) in a given document and then there classifi... more 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.
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Papers by Deepti Chopra