IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Fast and Accurate Spelling Correction Using Trie and Damerau-levenshtein Dist...TELKOMNIKA JOURNAL
This research was intended to create a fast and accurate spelling correction system with the
ability to handle both kind of spelling errors, non-word and real word errors. Existing spelling correction
system was analyzed and was then applied some modifications to improve its accuracy and speed. The
proposed spelling correction system is then built based on the method and intuition used by existing
system along with the modifications made in previous step. The result is a various spelling correction
system using different methods. Best result is achieved by the system that uses bigram with Trie and
Damerau-Levenshtein distance with the word level accuracy of 84.62% and an average processing speed
of 18.89 ms per sentence.
A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERINGIJDKP
Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text
mining in order to perform document clustering. We took an example of document clustering where the
documents had to be clustered into two categories. The method involved cleaning up the text and stemming
of words. Then, we chose ‘m’ features which differ significantly in their word frequencies (WF), normalized
by document length, between documents belonging to these two clusters. The documents to be clustered
were represented as a collection of ‘m’ normalized WF values. Fuzzy c-means (FCM) algorithm was used
to cluster these documents into two clusters. After the FCM execution finished, the documents in the two
clusters were analysed for the values of their respective ‘m’ features. It was known that documents
belonging to a document type ‘X’ tend to have higher WF values for some particular features. If the
documents belonging to a cluster had higher WF values for those same features, then that cluster was said
to represent ‘X’. By fuzzy logic, we not only get the cluster name, but also the degree to which a document
belongs to a cluster
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Fast and Accurate Spelling Correction Using Trie and Damerau-levenshtein Dist...TELKOMNIKA JOURNAL
This research was intended to create a fast and accurate spelling correction system with the
ability to handle both kind of spelling errors, non-word and real word errors. Existing spelling correction
system was analyzed and was then applied some modifications to improve its accuracy and speed. The
proposed spelling correction system is then built based on the method and intuition used by existing
system along with the modifications made in previous step. The result is a various spelling correction
system using different methods. Best result is achieved by the system that uses bigram with Trie and
Damerau-Levenshtein distance with the word level accuracy of 84.62% and an average processing speed
of 18.89 ms per sentence.
A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERINGIJDKP
Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text
mining in order to perform document clustering. We took an example of document clustering where the
documents had to be clustered into two categories. The method involved cleaning up the text and stemming
of words. Then, we chose ‘m’ features which differ significantly in their word frequencies (WF), normalized
by document length, between documents belonging to these two clusters. The documents to be clustered
were represented as a collection of ‘m’ normalized WF values. Fuzzy c-means (FCM) algorithm was used
to cluster these documents into two clusters. After the FCM execution finished, the documents in the two
clusters were analysed for the values of their respective ‘m’ features. It was known that documents
belonging to a document type ‘X’ tend to have higher WF values for some particular features. If the
documents belonging to a cluster had higher WF values for those same features, then that cluster was said
to represent ‘X’. By fuzzy logic, we not only get the cluster name, but also the degree to which a document
belongs to a cluster
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...IJCSEA Journal
We have attempted in this paper to reduce the number of checked condition through saving frequency of the
tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep
algorithm. The essential idea of non-overlapping iterative neighbor search in a document lies in focusing
the search not on the full space of solutions but on a smaller subspace considering non-overlapping
intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword.
We repeatedly pick a range up and flip the unsatisfied keywords, so the relevant ranges are detected. The
proposed method tries to improve the plane sweep algorithm by efficiently calculating the minimal group of
words and enumerating intervals in a document which contain the minimum frequency keyword. It
decreases the number of comparison and creates the best state of optimized search algorithm especially in
a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the
technical approach.
A SURVEY ON SIMILARITY MEASURES IN TEXT MINING mlaij
The Volume of text resources have been increasing in digital libraries and internet. Organizing these text documents has become a practical need. For organizing great number of objects into small or minimum number of coherent groups automatically, Clustering technique is used. These documents are widely used for information retrieval and Natural Language processing tasks. Different Clustering algorithms require a metric for quantifying how dissimilar two given documents are. This difference is often measured by similarity measure such as Euclidean distance, Cosine similarity etc. The similarity measure process in text
mining can be used to identify the suitable clustering algorithm for a specific problem. This survey discusses the existing works on text similarity by partitioning them into three significant approaches; String-based, Knowledge based and Corpus-based similarities.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Discovering Novel Information with sentence Level clustering From Multi-docu...irjes
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
COMPARISON OF HIERARCHICAL AGGLOMERATIVE ALGORITHMS FOR CLUSTERING MEDICAL DO...ijseajournal
Extensive amount of data stored in medical documents require developing methods that help users to find
what they are looking for effectively by organizing large amounts of information into a small number of
meaningful clusters. The produced clusters contain groups of objects which are more similar to each other
than to the members of any other group. Thus, the aim of high-quality document clustering algorithms is to
determine a set of clusters in which the inter-cluster similarity is minimized and intra-cluster similarity is
maximized. The most important feature in many clustering algorithms is treating the clustering problem as
an optimization process, that is, maximizing or minimizing a particular clustering criterion function
defined over the whole clustering solution. The only real difference between agglomerative algorithms is
how they choose which clusters to merge. The main purpose of this paper is to compare different
agglomerative algorithms based on the evaluation of the clusters quality produced by different hierarchical
agglomerative clustering algorithms using different criterion functions for the problem of clustering
medical documents. Our experimental results showed that the agglomerative algorithm that uses I1 as its
criterion function for choosing which clusters to merge produced better clusters quality than the other
criterion functions in term of entropy and purity as external measures.
Supervised WSD Using Master- Slave Voting Techniqueiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...IJCSEA Journal
We have attempted in this paper to reduce the number of checked condition through saving frequency of the
tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep
algorithm. The essential idea of non-overlapping iterative neighbor search in a document lies in focusing
the search not on the full space of solutions but on a smaller subspace considering non-overlapping
intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword.
We repeatedly pick a range up and flip the unsatisfied keywords, so the relevant ranges are detected. The
proposed method tries to improve the plane sweep algorithm by efficiently calculating the minimal group of
words and enumerating intervals in a document which contain the minimum frequency keyword. It
decreases the number of comparison and creates the best state of optimized search algorithm especially in
a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the
technical approach.
A SURVEY ON SIMILARITY MEASURES IN TEXT MINING mlaij
The Volume of text resources have been increasing in digital libraries and internet. Organizing these text documents has become a practical need. For organizing great number of objects into small or minimum number of coherent groups automatically, Clustering technique is used. These documents are widely used for information retrieval and Natural Language processing tasks. Different Clustering algorithms require a metric for quantifying how dissimilar two given documents are. This difference is often measured by similarity measure such as Euclidean distance, Cosine similarity etc. The similarity measure process in text
mining can be used to identify the suitable clustering algorithm for a specific problem. This survey discusses the existing works on text similarity by partitioning them into three significant approaches; String-based, Knowledge based and Corpus-based similarities.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Discovering Novel Information with sentence Level clustering From Multi-docu...irjes
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
COMPARISON OF HIERARCHICAL AGGLOMERATIVE ALGORITHMS FOR CLUSTERING MEDICAL DO...ijseajournal
Extensive amount of data stored in medical documents require developing methods that help users to find
what they are looking for effectively by organizing large amounts of information into a small number of
meaningful clusters. The produced clusters contain groups of objects which are more similar to each other
than to the members of any other group. Thus, the aim of high-quality document clustering algorithms is to
determine a set of clusters in which the inter-cluster similarity is minimized and intra-cluster similarity is
maximized. The most important feature in many clustering algorithms is treating the clustering problem as
an optimization process, that is, maximizing or minimizing a particular clustering criterion function
defined over the whole clustering solution. The only real difference between agglomerative algorithms is
how they choose which clusters to merge. The main purpose of this paper is to compare different
agglomerative algorithms based on the evaluation of the clusters quality produced by different hierarchical
agglomerative clustering algorithms using different criterion functions for the problem of clustering
medical documents. Our experimental results showed that the agglomerative algorithm that uses I1 as its
criterion function for choosing which clusters to merge produced better clusters quality than the other
criterion functions in term of entropy and purity as external measures.
Supervised WSD Using Master- Slave Voting Techniqueiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Application of Pattern matching for Motif IdentificationCSCJournals
Pattern matching is one of the central and most widely studied problem in theoretical computer science. Solutions to the problem play an important role in many areas of science and information processing. Its performance has great impact on many applications including database query, text processing and DNA sequence analysis. In general Pattern matching algorithms are based on the shift value, the direction of the sliding window and the order in which comparisons are made. The performance of the algorithms can be enhanced to a great extent by a larger shift value and less number of comparison to get the shift value. In this paper we proposed an algorithm, for finding motif in DNA sequence. The algorithm is based on preprocessing of the pattern string(motif) by considering four consecutive nucleotides of the DNA that immediately follow the aligned pattern window in an event of mismatch between pattern(motif) and DNA sequence .Theoretically, we found the proposed algorithms work efficiently for motif identification.
Proposed Method for String Transformation using Probablistic ApproachEditor IJMTER
For this system the string is given as an input to the system generates the k most likely output strings corresponding to the input string. This system proposes both accurate and efficient feature by using a novel and probabilistic approach to string transformation, which is. The approach is includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method will apply to correction of spelling errors in queries as well are formulation of queries in web search.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
Visualizing stemming techniques on online news articles text analyticsjournalBEEI
Stemming is the process to convert words into their root words by the stemming algorithm. It is one of the main processes in text analytics where the text data needs to go through stemming process before proceeding to further analysis. Text analytics is a very common practice nowadays that is practiced toanalyze contents of text data from various sources such as the mass media and media social. In this study, two different stemming techniques; Porter and Lancaster are evaluated. The differences in the outputs that are resulted from the different stemming techniques are discussed based on the stemming error and the resulted visualization. The finding from this study shows that Porter stemming performs better than Lancaster stemming, by 43%, based on the stemming error produced. Visualization can still be accommodated by the stemmed text data but some understanding of the background on the text data is needed by the tool users to ensure that correct interpretation can be made on the visualization outputs.
Extractive Document Summarization - An Unsupervised ApproachFindwise
In this paper we present and evaluate a system for automatic extractive document summarization. We employ three different unsupervised algorithms for sentence ranking - TextRank, K-means clustering and previously unexplored in this field, one- class SVM. By adding language and domain specific boosting we achieve state-of-the-art performance for English measured in ROUGE Ngram(1,1) score on the DUC 2002 dataset - 0,4797. In addition, the system can be used for both single and multi document summarization. We also present results for Swedish, based on a new corpus - featured Wikipedia articles.
SIMILAR THESAURUS BASED ON ARABIC DOCUMENT: AN OVERVIEW AND COMPARISONIJCSEA Journal
The massive grow of the modern information retrieval system (IRS), especially in natural languages
becomes more difficult. The search in Arabic languages, as natural language, is not good enough yet. This
paper will try to build similar thesaurus based on Arabic language in two mechanisms, the first one is full
word mechanisms and the other is stemmed mechanisms, and then to compare between them.
The comparison made by this study proves that the similar thesaurus using stemmed mechanisms get more
better results than using traditional in the same mechanisms and similar thesaurus improved more the
recall and precision than traditional information retrieval system at recall and precision levels.
A COMPARATIVE STUDY OF ROOT-BASED AND STEM-BASED APPROACHES FOR MEASURING THE...acijjournal
Representation of semantic information contained in the words is needed for any Arabic Text Mining
applications. More precisely, the purpose is to better take into account the semantic dependencies
between words expressed by the co-occurrence frequencies of these words. There have been many
proposals to compute similarities between words based on their distributions in contexts. In this paper,
we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Rootbased (Stemming), and Stem-based (Light Stemming) approaches for measuring the similarity between
Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide
variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity,
Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one
hand, the variety of the corpus produces more accurate results; on the other hand, the Stem-based
approach outperformed the Root-based one because this latter affects the words meanings.
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...cscpconf
Document Clustering algorithms goal is to create clusters that are coherent internally, but clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable to eliminate it and keeping just the useful information. Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining applications can use it to improve her results. The Keyphrases are defined as phrases that capture the main topics discussed in document; they offer a brief and precise summary of document content. Therefore, it can be a good solution to get rid of the existent noise from documents. In this paper, we propose a new method to solve the problem cited above especially for Arabic language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach, we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting Keyphrases improves the clustering results.
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...csandit
Document Clustering algorithms goal is to create clusters that are coherent internally, but
clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable
to eliminate it and keeping just the useful information.
Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining
applications can use it to improve her results. The Keyphrases are defined as phrases that
capture the main topics discussed in document; they offer a brief and precise summary of
document content. Therefore, it can be a good solution to get rid of the existent noise from
documents.
In this paper, we propose a new method to solve the problem cited above especially for Arabic
language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach,
we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage
techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting
Keyphrases improves the clustering results.
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An Examination of Effectuation Dimension as Financing Practice of Small and M...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Does Goods and Services Tax (GST) Leads to Indian Economic Development?iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Childhood Factors that influence success in later lifeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emotional Intelligence and Work Performance Relationship: A Study on Sales Pe...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer’s Acceptance of Internet Banking in Dubaiiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study of Employee Satisfaction relating to Job Security & Working Hours amo...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumer Perspectives on Brand Preference: A Choice Based Model Approachiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Broadcast Management in Nigeria: The systems approach as an imperativeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Retailer’s Perception on Soya Products with Special Reference to T...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study Factors Influence on Organisation Citizenship Behaviour in Corporate ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumers’ Behaviour on Sony Xperia: A Case Study on Bangladeshiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Design of a Balanced Scorecard on Nonprofit Organizations (Study on Yayasan P...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Public Sector Reforms and Outsourcing Services in Nigeria: An Empirical Evalu...iosrjce
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
A survey of Stemming Algorithms for Information Retrieval
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. VI (May – Jun. 2015), PP 76-80
www.iosrjournals.org
DOI: 10.9790/0661-17367680 www.iosrjournals.org 76 | Page
A survey of Stemming Algorithms for Information Retrieval
Brajendra Singh Rajput1
, Dr. Nilay Khare2
1,2
(Computer Science &Engineering, Maulana Azad National Institute of Technology, India)
Abstract:Now a day’s text documents is advancing over internet, e-mails and web pages. As the use of internet
is exponentially growing, the need of massive data storage is increasing. Normally many of the documents
contain morphological variables, so stemming which is a preprocessing technique gives a mapping of different
morphological variants of words into their base word called the stem. Stemming process is used in information
retrieval as a way to improve retrieval performance based on the assumption that terms with the same stem
usually have similar meaning. To do stemming operation on large data, we require normally more computation
time and power, to cope up with the need to search for a particular word in the data. In this paper, various
stemming algorithms are analyzed with the benefits and limitation of the recent stemming technique.
Keywords - Information Retrieval, NLP, stemming technique, Decision based method, Statistical method.
I. Introduction
In Information Retrieval systems the main thing is to improve recall while keeping a good precision. A
recall increasing method which can be useful for even the simplest Boolean retrieval systems is stemming.
Information finder who is looking for texts say dogs is probably interested in the texts which consist of the term
dog [6]. The capacity of the search database has increased in the last few years, so in order to meet the challenge
of real time search NLP algorithms speed up required. Natural language texts typically consist of many different
syntactic variants for example corrected, correct, correcting, correction, correctly, correctness, correctively,
correctional, corrective, correctable (adjective), corrector (noun) all are derived word of root word correct [1].
The conventional approach used to extract data for some user query is to search the documents present in the
corpus word by word for the given query. This approach is very time taking and it may leave some of the
equivalent documents of equal nature. Thus to avoid these situations, Stemming has been extensively used in
various Information Retrieval Systems to increase the extracting accuracy [4].
All documents which contain word with same stem as the query term are relevant, Stemming cut down the size
of the feature set. In text mining, stemming can be viewed as clustering in pattern recognition, feature
reducibility. In rule based reasoning, the main purpose is to choose maximum representative feature, dimensions
base on similarity measurement [13].
The derived words present, presented, presentation and presenting are converted to root word present,
through which not only retrieval performance improve but also storage can be optimized in some specific
applications.
II. Approaches Of Conflations
In order to perform stemming operation, we have to conflate a word to its different variants.
Conflations approaches which are used in stemming algorithms are shown in figure 1. The conflation of words
or Stemming can be executed in two ways, either manually using the kinds of regular expression or automatic.
Automatic technique can be divided into four types namely affix removal, successor, table lookup and n gram.
Affix removal can be further divided into two ways one is longest match and another is a simple removal [8].
2. A survey on Stemming Algorithms for Information Retrieval
DOI: 10.9790/0661-17367680 www.iosrjournals.org 77 | Page
Fig1: Conflation Approach.
2.1 Affix Removal
The affix removal algorithms eliminate prefix or suffix from word in order to reduce word into
common base. Most of stemmer used this type of approach for conflation. These algorithms depend on two
principles one is iteration, which removes strings in each order class one at a time, starting at the end of a word
and going towards its beginning. Not more than one match is allowed in a single order class. The suffix is added
to a word in any random order, that is, there exist order classes of suffix. The longest match is second type in
which within any given class of endings, if more than one ending gives a match then longest match should be
eliminated [1].
2.2 Successor Variety
In successor variety method [12], frequencies of letter sequences in a body of text as the basis of
stemming. The successor variety of a string is the number of different characters that follows it in word in some
body of text. Consider text pattern which consists of the following terms for example, match, mean, mood,
miasm, mobile .For estimating the successor variety (SV) for “machine" suppose, the following approach is
used. The earliest letter of machine is 'm' which is accompany by a, i, o, e so successor variety of m is 4,for the
next SV of machine we have to check that “ma” in machine is followed by which terms in the text body, so
next SV of machine is 1 because t come next in match for machine. When this process is applied on a large
body of text the successor variety of the substring of term will reduces as more character are added until a
segment boundary is reached. So this idea is used to get the stem.
2.3 Table Lookup Method
Table lookup method is done by looking at the table where the term stems and their Corresponding
stored. Term from queries and indexes could be stemmed by then a lookup table [6].If we use B-tree or hash
table lookup then such would be fast, but there is a problem of storage overhead for such table.
2.4 N-Gram Method
Another method of conflating the terms called shared diagram method given in 1974 by Adamson and
Boreham [9]. The diagram is a pair of consecutive letters. Besides diagram, we can also use trigrams and Hence
it is called n-gram method [10] .With this approach, pair of words are associated on the basis of unique diagram
they hold both. For calculating this relationship, we use determines Dice's coefficient [8]. For example, the term
Correction and Corrective can be broken into di-grams as follows.
WORD DI GRAMS TRI GRAMS
Correction *C,CO,OR,RR,RE,EC,CT,TI,IO,ON,N* **C,*CO,COR,ORR,RRE,REC,ECT,CTI,TIO,ION,ON*,N**
Corrective *C,CO,OR,RR,RE,EC,CT,TI,IV,VE,E* **C,*CO,COR,ORR,RRE,REC,ECT,CTI,TIV,IVE,VE*,E**
A 11 12
B 11 12
C 8 8
Dice-Coeff. 0.727 0.667
Table 1 N – Grams (* denotes padding space)
3. A survey on Stemming Algorithms for Information Retrieval
DOI: 10.9790/0661-17367680 www.iosrjournals.org 78 | Page
Thus “Correction " has eleven digrams and twelve trigrams of which all are unique and " Corrective "
also has eleven digram and twelve trigrams of which all are unique. The two words share eight unique digrams
and trigrams.
Once the unique digrams and trigrams for the pair have been identified and counted, the similarity
measure based on them can be calculated. The similarity measure is used Dice's coefficient, which is given as:
S = (2C)/ (A + B)
Where A is the number of unique N-gram in the First Word, B is the number of unique N-gram in the
second word and C is the number of N-grams shared by A and B. For example, above Dice's coefficient would
be equal (2 * 8) / (11 + 11) = 0.727 for Di gram and (2*8)/(12+12) = 0.667 for Tri grams. Such similarity
measures are determined for all pairs of term in the database. Such similarity is computed for all the word pairs,
they clustered as the groups. The value of the Dice coefficient gives you the hint that the stem for these pairs of
words lies in the first 8 unique n-grams.
III. Classification Of Stemmer
Basically Stemming algorithms can be classified into two types, Rule based and Statistical. Each type
has its own ways to find for stem. Rule based stemmer encodes language specific rules, whereas statistical
information from a large corpus of a given language to learn the morphology.
Fig2: Stemmer Classification.
3.1 Rule Based Stemmer
In a rule based approach language specific rules are planned and based on these regulations stemming
is performed. In this approach various provision are specified for converting a word to its derivation stem, a list
of all legitimate stem are given and there are some special rules which are used to handle the exceptional cases.
4. A survey on Stemming Algorithms for Information Retrieval
DOI: 10.9790/0661-17367680 www.iosrjournals.org 79 | Page
3.1.1 Porter Stemmer:
In standard Porter stemmer there are five steps and sixty conditions. There are many modifications of
standard algorithms and its used for English document processing. General rule of removing suffix is given as:
(Condition)S1 S2
Whenever condition is fulfilled suffix S1 is replaced by suffix S2. The order of consonants(C), vowel
(V) and consonants (C) is counted as measure function (m) in porter stemmer. When the measuring function is
greater than one, then only certain condition are applied [5].
3.1.2 Lovins Stemmer
In Lovins stemmer there are 29 conditions, 35 transformation rules and it perform a lookup on a table
of 294 endings. Here stemming comprises of two phases [7].In the first phase, the stemming algorithm retrieves
the stem of a word by removing its longest possible ending by matching these ending with the list of suffixes
stored in computer and in the second phase spelling exception are handled. For example the word “absorption”
is derived from the stem ”absorpt” and “absorbing” is derived from the stem ”absorb”. The problem with the
spelling exception arises in the above case when we try to match the two words “absorpt” and “absorb”. Such
exceptions are handled very carefully by introducing recording and partial matching techniques in the stemmer
as post stemming procedures.
Rule dependent stemmer is fast in nature means calculation time used to find a stem is less. The
retrieval result for English by using a rule dependent stemmer is reasonable, but the problem associated with
rule based is one need to have extensive language expertise to make them.
3.2 Statistical Stemmer
Statistical Stemmer is good alternative to rule based stemmer and does not involve language expertise.
They use statistical information from a large corpus of a given language to learn morphology. Statistical
language processing has been successfully used to improve the performance of information retrieval systems in
the absence of extensive linguistic resources for some language.
3.2.1 Yet Another Suffix Stripper (YASS)
Yet another suffix stripper is one of statistical based language independent stemmer and its
performance can be compared with both rule base stemmer in term of average precision. In this method a set of
string distance measure is used. The string distance measure is used to check the similarity between the two
words by calculating string the distance between two strings. The distance function maps a pair of string a and b
to a real number r, where a smaller value of r indicates greater similarity between a and b. The main reason for
estimating this distance is to find the longest matching prefix [4].
3.2.2 Graph Based Stemmer (GRAS)
GRAS is a graph based language independent stemmer for information retrieval. Extracting
effectiveness, simplification and low computation cost are the features of GRAS. In GRAS [10], first we look
for long common prefix amongst the word pair available in the document set. Suppose two word pair W1=P*S1
and W2=P*S2 where P is the longest common prefix between W1 and W2.The suffix pair S1 & S2 should be
valid suffix if other word pairs also have a common initial part followed by these suffixes such that W’1 = P’ *S1
& W’2 = P’* S2 Then, S1 & S2 is the pair of candidate suffix if large number of word pairs is of this form.
Then look for pairs that are morphological related if they share a non-empty common prefix. The suffix pair is a
legal candidate suffix pair. Using a Graph we model word relationships where nodes represent the words and
edges are used to attach the related words. Normally in GRAS Pivot is a node which is associated by edges to an
other nodes. In the last step, a word which is connected to a pivot is put in the same class as the pivot if it shares
common neighbors with the pivot.
IV. Stemming Error
There are fundamentally two kinds of fault in stemming algorithms one is over stemming and another
is under stemming [3]. Over stemming occurs when two words which have dissimilar root word are changed to
the identical base term, which is also identified as a false positive. In under stemming two words which have
similar root are not stemmed to the same base term, which is also called as false negative. Paice [11] has
demonstrated that light stemmer decreases the over stemming but increases the under stemming errors. On the
other side heavy stemmer reduces the under stemming error while increasing the over stemming errors.
5. A survey on Stemming Algorithms for Information Retrieval
DOI: 10.9790/0661-17367680 www.iosrjournals.org 80 | Page
V. Conclusion
We studied a variety of stemming methods and got to know that stemming appreciably increases the
retrieval results for both rule dependent and statistical approach. It is also useful in reducing the size of index
files and feature set or attribute as the number of words to be indexed are reduced to common forms called
stems. The performance of statistical stemmers is far superior to some well-known rule-based stemmers but time
consuming. Rule dependent stemmer like porter stemmer is good choice for English document processing but its
language dependent.
References
[1]. Sandeep R. Sirsat, Dr. Vinay Chavan and Dr. Hemant S. Mahalle, Strength and Accuracy Analysis of Affix Removal Stemming
Algorithms, International Journal of Computer Science and Information Technologies, Vol. 4 (2) , 2013, 265 - 269.
[2]. S.P.Ruba Rani, B.Ramesh, M.Anusha and Dr. J.G.R.Sathiaseelan, Evaluation of Stemming Techniques for Text Classification
,International Journal of Computer Science and Mobile Computing, Vol.4 Issue.3, March- 2015, pg. 165-171
[3]. Ms. Anjali Ganesh Jivani, A Comparative Study of Stemming Algorithms, International Journal Comp. Tech. Appl.(IJCTA) 2011,
Vol 2 (6), 1930-1938 ISSN:2229-6093
[4]. Deepika Sharma, Stemming Algorithms: A Comparative Study and their Analysis, International Journal of Applied Information
Systems (IJAIS) Foundation of Computer Science, FCS, New York, USA September 2012 ISSN : 2249-0868 Volume 4– No.3
[5]. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3):130–137
[6]. Wessel Kraaij and Renee ´ Pohlmann,Porter’s stemming algorithm for Dutch,UPLIFT (Utrecht Project: Linguistic Information for
Free Text retrieval) is sponsored by the NBBI,Philips Research, the Foundation for Language Technology.
[7]. Lovins, J. B. (1968). Development of a stemming algorithm. Mechanical Translation and Computational Linquistics, 11:22–31
[8]. WB Frakes, 1992,“Stemming Algorithm “, in “Information Retrieval Data Structures and Algorithm”,Chapter 8, page 132-139.
[9]. G. Adamson and J. Boreham 1974. "The Use of an Association Measure Based on Character Structure to Identify Semantically
Related Pairs of Words and Document Titles," Information Storage and Retrieval, 10,253-60.
[10]. JH Paik, Mandar Mitra, Swapan K. Parui, Kalervo Jarvelin, “GRAS ,An effective and efficient stemming algorithm for information
retrieval”, ACM Transaction on Information System Volume 29 Issue 4, December 2011, Chapter 19, page 20-24
[11]. Paice Chris D.,Another stemmer, ACM SIGIR Forum, Volume 24, No. 3. 1990, 56-61.
[12]. M. Hafer and S. Weiss 1974. "Word Segmentation by Letter Successor Varieties," Information Storage and Retrieval, 10, 371-85
[13]. Narayan L. Bhamidipati and Sankar K. Pal, Stemming via distribution based word segregation for classification and retrival,IEEE
Transaction on system,man,and cybernetics – partB cybernetics. Vol 37 ,no2 april 2007.