In this paper, we have proposed several improved versions of rapid automatic keyword extraction (... more In this paper, we have proposed several improved versions of rapid automatic keyword extraction (RAKE) algorithm for extracting keywords from Hindi documents. As RAKE requires a stopword list to generate the set of candidate keywords, which is unavailable in Hindi, we have constructed the Hindi stopword list for this purpose. We have found some weakness in keyword scoring measures of RAKE and proposed several models such as N-RAKE, SD-RAKE, NSD-RAKE, and WOS-RAKE to improve upon the effectiveness of RAKE. We have found that our modifications yield better results in general than original RAKE.
International Journal on Semantic Web and Information Systems
This article describes how semantic web data sources follow linked data principles to facilitate ... more This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.
The explosive growth of the World Wide Web is making it difficult for a user to locate informatio... more The explosive growth of the World Wide Web is making it difficult for a user to locate information that is relevant to his/her interest. Though existing search engines work well to a certain extent but they still face problems like word mismatch which arises because the majority of information retrieval systems compare query and document terms on lexical level rather than on semantic level and short query: the average length of queries by the user is less than two words. Short queries and the incompatibility between the terms in user queries and documents strongly affect the retrieval of relevant document. Query expansion has long been suggested as a technique to increase the effectiveness of the information retrieval. Query expansion is the process of supplementing additional terms or phrases to the original query to improve the retrieval performance. The central problem of query expansion is the selection of the expansion terms based on which user's original query is expanded. Thesaurus helps to solve this problem. Thesaurus have frequently been incorporated in information retrieval system for identifying the synonymous expressions and linguistic entities that are semantically similar. Thesaurus has been widely used in many applications, including information retrieval and natural language processing. .
Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the perfo... more Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the performance of information retrieval systems. All the terms of PRF documents are not important for expanding the user query. Therefore selection of proper expansion term is very important for improving system performance. Individual query expansion terms selection methods have been widely investigated for improving its performance. Every individual expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, first the possibility of improving the overall performance using individual query expansion terms selection methods has been explored. Second, Borda count rank aggregation approach is used for combining multiple query expansion terms selection methods. Third, the semantic similarity approach is used to select semantically similar terms with the query after applying Borda count ranks combining approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over individual terms selection method and related state-of-the-art methods.
Automatic Query expansion is a well-known method to improve the performance of information retrie... more Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper, we consider methods to extract the candidate terms for automatic query expansion, based on co-occurrence information from psuedo relevant documents. The objective of the paper is: to present to user different ways of selecting and ranking co-occurring terms and to suggest use of information theoretic measures for ranking the co-occurring terms selected, in order to improve retrieval efficiency. Specifically in our work, we have used two information theoretic measures: Kullback-Leibler divergence (KLD) and a variant of KLD. These measures are based on relative entropy between top documents and entire collection. We have compared the retrieval improvement achieved by expanding the query with terms obtained with different methods belonging to both approaches (co occurrence based and information theoretic). Experiments have been performed on TREC-1 data set. Intensive experiments have been done to select suitable parameters used in automatic query expansion, such as number of top n selected documents and the number of terms selected for expansion. Results suggest that firstly considerable improvements can be achieved if co-occurring terms are selected properly by considering different options available for selecting them. Secondly, information theoretic measures applied over co-occurring terms can be helpful in improving retrieval efficiency.
The coming of the WWW and of the Internet as a telecommunications network has changed the concept... more The coming of the WWW and of the Internet as a telecommunications network has changed the concept of what is considered information: the person who is best informed is not the one with the most information but the one with the best means for obtaining and assimilating (consuming) exactly the information acquired. This situation is proving to be a great stimulus for research into and the development of applications in the field of technology for recovering and extracting information. In this context, automated document summary systems are a new step forward towards optimizing the treatment of documentation in digital formats and for tailoring it to the needs of users. The objective of this paper is to provide the status of work done in the field of automatic text summarization and develop an algorithm to generate automatic text summary. Further experiments are performed based on proposed algorithm.
Automatic Query expansion is a well-known method to improve the performance of information retrie... more Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper we have suggested information theoretic measures to improve efficiency of co-occurrence based automatic query expansion. We have used pseudo relevance feedback based local approach. The expansion terms were selected from the top N documents using co-occurrence based approach. They were then ranked using two different information theoretic approaches. First one is standard Kullback ...
International Journal of Computer Applications, 2015
In this paper we present a survey of various techniques available in text mining for keyword and ... more In this paper we present a survey of various techniques available in text mining for keyword and keyphrase extraction. Keywords and keyphrases are very useful in analyzing large amount of textual material quickly and efficiently search over the internet besides being useful for many other purposes. Keywords and keyphrases are set of representative words of a document that give high-level specification of the content for interested readers. They are used highly in the field of Computer Science especially in Information Retrieval and Natural Language Processing and can be used for index generation, query refinement, text summarization, author assistance, etc. We have also discussed some important feature selection metrics generally employed by researchers to rank candidate keywords and keyphrases according to their importance.
In this paper, we have proposed several improved versions of rapid automatic keyword extraction (... more In this paper, we have proposed several improved versions of rapid automatic keyword extraction (RAKE) algorithm for extracting keywords from Hindi documents. As RAKE requires a stopword list to generate the set of candidate keywords, which is unavailable in Hindi, we have constructed the Hindi stopword list for this purpose. We have found some weakness in keyword scoring measures of RAKE and proposed several models such as N-RAKE, SD-RAKE, NSD-RAKE, and WOS-RAKE to improve upon the effectiveness of RAKE. We have found that our modifications yield better results in general than original RAKE.
International Journal on Semantic Web and Information Systems
This article describes how semantic web data sources follow linked data principles to facilitate ... more This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.
The explosive growth of the World Wide Web is making it difficult for a user to locate informatio... more The explosive growth of the World Wide Web is making it difficult for a user to locate information that is relevant to his/her interest. Though existing search engines work well to a certain extent but they still face problems like word mismatch which arises because the majority of information retrieval systems compare query and document terms on lexical level rather than on semantic level and short query: the average length of queries by the user is less than two words. Short queries and the incompatibility between the terms in user queries and documents strongly affect the retrieval of relevant document. Query expansion has long been suggested as a technique to increase the effectiveness of the information retrieval. Query expansion is the process of supplementing additional terms or phrases to the original query to improve the retrieval performance. The central problem of query expansion is the selection of the expansion terms based on which user's original query is expanded. Thesaurus helps to solve this problem. Thesaurus have frequently been incorporated in information retrieval system for identifying the synonymous expressions and linguistic entities that are semantically similar. Thesaurus has been widely used in many applications, including information retrieval and natural language processing. .
Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the perfo... more Pseudo-Relevance Feedback (PRF) is a well-known method of query expansion for improving the performance of information retrieval systems. All the terms of PRF documents are not important for expanding the user query. Therefore selection of proper expansion term is very important for improving system performance. Individual query expansion terms selection methods have been widely investigated for improving its performance. Every individual expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, first the possibility of improving the overall performance using individual query expansion terms selection methods has been explored. Second, Borda count rank aggregation approach is used for combining multiple query expansion terms selection methods. Third, the semantic similarity approach is used to select semantically similar terms with the query after applying Borda count ranks combining approach. Our experimental results demonstrated that our proposed approaches achieved a significant improvement over individual terms selection method and related state-of-the-art methods.
Automatic Query expansion is a well-known method to improve the performance of information retrie... more Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper, we consider methods to extract the candidate terms for automatic query expansion, based on co-occurrence information from psuedo relevant documents. The objective of the paper is: to present to user different ways of selecting and ranking co-occurring terms and to suggest use of information theoretic measures for ranking the co-occurring terms selected, in order to improve retrieval efficiency. Specifically in our work, we have used two information theoretic measures: Kullback-Leibler divergence (KLD) and a variant of KLD. These measures are based on relative entropy between top documents and entire collection. We have compared the retrieval improvement achieved by expanding the query with terms obtained with different methods belonging to both approaches (co occurrence based and information theoretic). Experiments have been performed on TREC-1 data set. Intensive experiments have been done to select suitable parameters used in automatic query expansion, such as number of top n selected documents and the number of terms selected for expansion. Results suggest that firstly considerable improvements can be achieved if co-occurring terms are selected properly by considering different options available for selecting them. Secondly, information theoretic measures applied over co-occurring terms can be helpful in improving retrieval efficiency.
The coming of the WWW and of the Internet as a telecommunications network has changed the concept... more The coming of the WWW and of the Internet as a telecommunications network has changed the concept of what is considered information: the person who is best informed is not the one with the most information but the one with the best means for obtaining and assimilating (consuming) exactly the information acquired. This situation is proving to be a great stimulus for research into and the development of applications in the field of technology for recovering and extracting information. In this context, automated document summary systems are a new step forward towards optimizing the treatment of documentation in digital formats and for tailoring it to the needs of users. The objective of this paper is to provide the status of work done in the field of automatic text summarization and develop an algorithm to generate automatic text summary. Further experiments are performed based on proposed algorithm.
Automatic Query expansion is a well-known method to improve the performance of information retrie... more Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper we have suggested information theoretic measures to improve efficiency of co-occurrence based automatic query expansion. We have used pseudo relevance feedback based local approach. The expansion terms were selected from the top N documents using co-occurrence based approach. They were then ranked using two different information theoretic approaches. First one is standard Kullback ...
International Journal of Computer Applications, 2015
In this paper we present a survey of various techniques available in text mining for keyword and ... more In this paper we present a survey of various techniques available in text mining for keyword and keyphrase extraction. Keywords and keyphrases are very useful in analyzing large amount of textual material quickly and efficiently search over the internet besides being useful for many other purposes. Keywords and keyphrases are set of representative words of a document that give high-level specification of the content for interested readers. They are used highly in the field of Computer Science especially in Information Retrieval and Natural Language Processing and can be used for index generation, query refinement, text summarization, author assistance, etc. We have also discussed some important feature selection metrics generally employed by researchers to rank candidate keywords and keyphrases according to their importance.
Uploads
Papers by Aditi Sharan