TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed
in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of
sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit
expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and
also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add
some additional features for improving the classification method. The quality of the sentiment classification
is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy
rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as
precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and
Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence
interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 %
accurate results and error rate is very less compared to existing sentiment classification techniques.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
Generation of Question and Answer from Unstructured Document using Gaussian M...IJACEE IJACEE
Question Answering (QA) system is one of the ever growing applications in Natural Language Processing. The purpose of Automatic Question and Answer Generation system is to generate all possible questions and its relevant answers from a given unstructured document. Complex sentences are simplified to make question generation easier. The accuracy of the generated questions is measured by identifying the subtopics from the text using Gaussian Mixture Neural Topic Model (GMNTM).The similarity between generated questions and text are calculated using Extended String Subsequence Kernel (ESSK). The syntactic correctness of the questions is measured by Syntactic Tree Kernel which computes the similarity scores between each sentence in the given context and generated questions. Based on the similarity score, questions are ranked. The answers for the generated questions are extracted using Pattern Matching Approach. This system is expected to produce better accuracy when compared with the system using Latent Dirichlet Allocation (LDA) for subtopic identification.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGEijaia
Humans are always in a quest to extract information related to some topic or entity. Question answering system helps user to find the precise answer of the question articulated in natural language. Question answering system provides explicit, concise and accurate answer to user questions rather than providing set of relevant documents or web pages as answers as most of the information retrieval system does. The paper proposes question answering system for Marathi natural language by using concept of ontology as a formal representation of knowledge base for extracting answers. Ontology is used to express domain specific knowledge about semantic relations and restrictions in the given domains. The ontologies are developed with the help of domain experts and the query is analyzed both syntactically and semantically. The results obtained here are accurate enough to satisfy the query raised by the user. The level of accuracy is enhanced since the query is analyzed semantically.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
The traditional search engine have shortcoming that they retrieve irrelevant information. Query expansion
with relevant words increases the performance of search engines, but finding and using the relevant words
is an open problem. This paper presents a Hindi search engine in which we describe three models for
query enhancement. They are based on lexical variance, user context and combination of both techniques.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed
in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of
sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit
expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and
also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add
some additional features for improving the classification method. The quality of the sentiment classification
is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy
rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as
precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and
Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence
interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 %
accurate results and error rate is very less compared to existing sentiment classification techniques.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
Generation of Question and Answer from Unstructured Document using Gaussian M...IJACEE IJACEE
Question Answering (QA) system is one of the ever growing applications in Natural Language Processing. The purpose of Automatic Question and Answer Generation system is to generate all possible questions and its relevant answers from a given unstructured document. Complex sentences are simplified to make question generation easier. The accuracy of the generated questions is measured by identifying the subtopics from the text using Gaussian Mixture Neural Topic Model (GMNTM).The similarity between generated questions and text are calculated using Extended String Subsequence Kernel (ESSK). The syntactic correctness of the questions is measured by Syntactic Tree Kernel which computes the similarity scores between each sentence in the given context and generated questions. Based on the similarity score, questions are ranked. The answers for the generated questions are extracted using Pattern Matching Approach. This system is expected to produce better accuracy when compared with the system using Latent Dirichlet Allocation (LDA) for subtopic identification.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGEijaia
Humans are always in a quest to extract information related to some topic or entity. Question answering system helps user to find the precise answer of the question articulated in natural language. Question answering system provides explicit, concise and accurate answer to user questions rather than providing set of relevant documents or web pages as answers as most of the information retrieval system does. The paper proposes question answering system for Marathi natural language by using concept of ontology as a formal representation of knowledge base for extracting answers. Ontology is used to express domain specific knowledge about semantic relations and restrictions in the given domains. The ontologies are developed with the help of domain experts and the query is analyzed both syntactically and semantically. The results obtained here are accurate enough to satisfy the query raised by the user. The level of accuracy is enhanced since the query is analyzed semantically.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
The traditional search engine have shortcoming that they retrieve irrelevant information. Query expansion
with relevant words increases the performance of search engines, but finding and using the relevant words
is an open problem. This paper presents a Hindi search engine in which we describe three models for
query enhancement. They are based on lexical variance, user context and combination of both techniques.
Chapter 11: User support
from
Dix, Finlay, Abowd and Beale (2004).
Human-Computer Interaction, third edition.
Prentice Hall. ISBN 0-13-239864-8.
http://www.hcibook.com/e3/
Applying AI to software engineering problems: Do not forget the human!University of Córdoba
The application of artificial intelligence (AI) to software engineering (SE)-problem-solving has been around since the 80s when expert systems were first used. However, it is during the last 10 years that there has been a peak in the use of these techniques, first based on search and optimisation algorithms such as metaheuristics, and later based on machine learning algorithms. The aim is to help the software engineer to automate and optimise tasks of the software development process, and to use valuable information hidden in multiple data sources such as software repositories to execute insightful actions that generate improvements in the performance of the overall process. Today, the use of AI is trendy, and often overused as it could generate artificial results since it does not consider the subjective nature of the software development process requiring the experience and know-how of the engineer. With this Invited Talk, we will discuss different proposals to incorporate the human into the decision-making process in the application of AI for SE (AI4SE), from interactive algorithms to the generation of interpretable models or explanations.
Requirements Engineering for the HumanitiesShawn Day
This workshop explores how requirements engineering can be employed by digital and non-digital humanities scholars (and others) to conceptualise and communicate a research project.
requirementsEngineeringAs the field of digital humanities has evolved, one of the biggest challenges has been getting the marrying technical expertise with humanities scholarly practice to successfully deliver sustainable and sound digital projects. At its core this is a communications exercise. However, to communicate effectively demands an ability to effectively translate, define and find clarity in your own mind.
User Interface Design- Module 2 Uid ProcessbrindaN
User Interface Design- Module 2 Uid Process
Subject Code:15CS832 USER INTERFACE DESIGN
VTU UNIVERSITY
Referred Text Book: The Essential Guide to User Interface Design (Second Edition) Author: Wilbert O. Galitz
This document layouts out an introduction to Microsoft's DigiSeniors Curriculum and gives information to prospective instructors/trainers for how to leverage it in their classrooms.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
4. Introduction
What is difference between workspaces?What is your preference?
What all things can cause interruptions to programmer’s life in office as well as home?
Do you think taking a break and having an interruptions are different? How?
5. THE COGNITIVE NEUROSCIENCE OF
MEMORY
Memory types used heavily during programming:
Prospective
Attentive
Associative
Episodic
Conceptual
6. Reborn from Memory Failures !!
Discussion
“Mental Modelling of Program Context facilitates quick recovery from
memory failures” ?
What is your generic style of coding model ?
7. PROSPECTIVE MEMORY
“Remembering to Remember”
Storage of Intended action and it’s retrieval cue
Holds reminders for future action
Prone to:
Monitor Failure
Engage Failure
Solution : Smart Reminders
- Reminder Condition
- Notification Mechanism
- Reminder Message
8. DISCUSSION
How long does it take for you to get back in the groove with former task?
What could be the factors causing monitor failures
Passive Notification vs obstructive Notifications vs Constrictive Notifications
“Unless intentions are periodically refreshed by attentional checks in the interim,
there is a concern that they will become overlaid by other cognitive demands”
9. ATTENTIVE MEMORY
Highly volatile and prone to frequent failure
Prone toConcentration failure & Limit failure
Example: Refactoring of code
Rely on Compile Error
Information needs:
Need focused attention
Attending to various program location
Tools:Touch Point
Related Device: Bookmarks,TaskContext
10. Discussion
What you guys do to avoid impact of distraction when you are working on tasks like
refactoring of code?
Do you think any other information user needs when it comes to dealing with attentive
memory?
11. Associative Memory
Records traces and features of experiences
Example: Navigate through unknown codebase
Prone to Retention failure, Association failure
Rely on tabs, scrollbars to keep context
Information Needs:
Need diverse and distinguishable features
Need support for indexing into associative memory via multiple
modalities
Tools : Association Links
Related Device: NavTracs, Code Bubbles, Code Canvass
12. Discussion
What are the contextual elements that you need for association while
navigating through unknown codebase? Example
Video
Can you find any other information needs from this video?
14. EPISODIC MEMORY
Recollection of past events
Source Failure
Recording a stream of events, contextual details –
code snapshots, search terms & Results,
address of code samples and stack traces
Recollection Failure
Obstacle Narrative Structure – Setting, Conflict, Investigation and Resolution
Tutorial Narrative Structure – Setting (Procedure and code Snippet) and Conclusion
Devices : Information Quests and Code Replays
15. DISCUSSION
“Presenting information about a past programming session in an episodic
manner improves recall of a past programming task” – Does this statement
holds good?
“A programming task that can span several days, a code replay can
overwhelm a programmer with an excessively long and unstructured
sequence of code changes.” ?
16. CONCEPTUAL MEMORY
FormingConcept
Prone to:
Activation Failure
Formation Failure
Conceptualize: Sketching, diagramming
and note-taking
Information Needs:
Refresh and review before resuming with task
Tool: Memlet
Related Devices:Concern Mapper & Code Folding
17. Discussion
Does visualization (Sketches & Diagrams) refine your power of
conceptualization?
Is this a valid statement - “Explicit mapping between layers of
abstraction plays a role in Conceptualization” ?
How Novices and Experts deal with different levels of Conceptual
Memory Failures ?
18. Ongoing Research
Development of “GroupBar”
Categorization of interruption then analyzing its effect
Research should also include context of user
workspace and type of interruption
Complexity of task
Length of interruption
19. DISCUSSION & CONCLUSION
Semantics for language are easier to remember than syntactic
“Worklets” to handle different types of failure
Do you think is it possible to deal with all different failures through one tool or
different plugins?Which is better way?
If we use all plugins and extensions in one tool, do you think that will be best tool for
developer?
20. References
• [1] Czerwinski Eric Mary, SusanWilhite Horvitz, “A Diary Study ofTask Switching and
Interruptions”, CHI 2004
• [2] M.-A.D. Storey, F.D. Fracchia , H.A. Muller, Cognitive design elements to support the
construction of a mental model during software exploration”, onThe Journal of Systems and
Software 44”
• [3] Cherubini Mauro,Venolia Gina, DeLine Rob, J. Ko Andrew “Let’s Go to the Whiteboard:
How andWhy Software Developers Use Drawings”, CHI 2007 Proceedings
• [4] A. J. Ko, M. J. Coblenz, and H. H. Aung, “An exploratory study of how developers seek,
relate, and collect relevant information during software maintenance tasks,” IEEETrans.
Softw. Eng.,
• [5] Gloria Mark,Victor M. Gonzalez, Justin Harris, Donald Bren, “NoTask Left Behind?
Examining the Nature of Fragmented Work”,
• [6] ShamsiT. Iqbal, Xianjun Sam Zheng, Brian P. Bailey, “Task-Evoked Pupillary Response to
MentalWorkload in Human-Computer Interaction ”