This document presents an algorithm for semantic-based similarity measure (SBSM) to improve text clustering. The algorithm assigns semantic weights to documents terms and phrases based on their use as arguments in proposition bank notation. It calculates similarity between a document and query based on matching weighted terms and phrases. Experimental results on a dataset show the SBSM using proposition bank notation improves performance over traditional measures like cosine and Jaccard similarity. The algorithm captures semantic information within documents for more accurate similarity assessment and clustering.
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.
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.
A CLUSTERING TECHNIQUE FOR EMAIL CONTENT MININGijcsit
In today’s world of internet, with whole lot of e-documents such, as html pages, digital libraries etc. occupying considerable amount of cyber space, organizing these documents has become a practical need. Clustering is an important technique that organizes large number of objects into smaller coherent groups.This helps in efficient and effective use of these documents for information retrieval and other NLP tasks.Email is one of the most frequently used e-document by individual or organization. Email categorization is one of the major tasks of email mining. Categorizing emails into different groups help easy retrieval and maintenance. Like other e-documents, emails can also be classified using clustering algorithms. In this
paper a similarity measure called Similarity Measure for Text Processing is suggested for email clustering.
The suggested similarity measure takes into account three situations: feature appears in both emails, feature appears in only one email and feature appears in none of the emails. The potency of suggested similarity measure is analyzed on Enron email data set to categorize emails. The outcome indicates that the efficiency acquired by the suggested similarity measure is better than that acquired by other measures.
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE ijdms
Nowadays, document clustering is considered as a da
ta intensive task due to the dramatic, fast increas
e in
the number of available documents. Nevertheless, th
e features that represent those documents are also
too
large. The most common method for representing docu
ments is the vector space model, which represents
document features as a bag of words and does not re
present semantic relations between words. In this
paper we introduce a distributed implementation for
the bisecting k-means using MapReduce programming
model. The aim behind our proposed implementation i
s to solve the problem of clustering intensive data
documents. In addition, we propose integrating the
WordNet ontology with bisecting k-means in order to
utilize the semantic relations between words to enh
ance document clustering results. Our presented
experimental results show that using lexical catego
ries for nouns only enhances internal evaluation
measures of document clustering; and decreases the
documents features from thousands to tens features.
Our experiments were conducted using Amazon ElasticMapReduce to deploy the Bisecting k-means
algorithm
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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.
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.
A CLUSTERING TECHNIQUE FOR EMAIL CONTENT MININGijcsit
In today’s world of internet, with whole lot of e-documents such, as html pages, digital libraries etc. occupying considerable amount of cyber space, organizing these documents has become a practical need. Clustering is an important technique that organizes large number of objects into smaller coherent groups.This helps in efficient and effective use of these documents for information retrieval and other NLP tasks.Email is one of the most frequently used e-document by individual or organization. Email categorization is one of the major tasks of email mining. Categorizing emails into different groups help easy retrieval and maintenance. Like other e-documents, emails can also be classified using clustering algorithms. In this
paper a similarity measure called Similarity Measure for Text Processing is suggested for email clustering.
The suggested similarity measure takes into account three situations: feature appears in both emails, feature appears in only one email and feature appears in none of the emails. The potency of suggested similarity measure is analyzed on Enron email data set to categorize emails. The outcome indicates that the efficiency acquired by the suggested similarity measure is better than that acquired by other measures.
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE ijdms
Nowadays, document clustering is considered as a da
ta intensive task due to the dramatic, fast increas
e in
the number of available documents. Nevertheless, th
e features that represent those documents are also
too
large. The most common method for representing docu
ments is the vector space model, which represents
document features as a bag of words and does not re
present semantic relations between words. In this
paper we introduce a distributed implementation for
the bisecting k-means using MapReduce programming
model. The aim behind our proposed implementation i
s to solve the problem of clustering intensive data
documents. In addition, we propose integrating the
WordNet ontology with bisecting k-means in order to
utilize the semantic relations between words to enh
ance document clustering results. Our presented
experimental results show that using lexical catego
ries for nouns only enhances internal evaluation
measures of document clustering; and decreases the
documents features from thousands to tens features.
Our experiments were conducted using Amazon ElasticMapReduce to deploy the Bisecting k-means
algorithm
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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).
With ever increasing number of documents on web and other repositories, the task of organizing and
categorizing these documents to the diverse need of the user by manual means is a complicated job, hence
a machine learning technique named clustering is very useful. Text documents are clustered by pair wise
similarity of documents with similarity measures like Cosine, Jaccard or Pearson. Best clustering results
are seen when overlapping of terms in documents is less, that is, when clusters are distinguishable. Hence
for this problem, to find document similarity we apply link and neighbor introduced in ROCK. Link
specifies number of shared neighbors of a pair of documents. Significantly similar documents are called as
neighbors. This work applies links and neighbors to Bisecting K-means clustering in identifying seed
documents in the dataset, as a heuristic measure in choosing a cluster to be partitioned and as a means to
find the number of partitions possible in the dataset. Our experiments on real-time datasets showed a
significant improvement in terms of accuracy with minimum time.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
TOPIC EXTRACTION OF CRAWLED DOCUMENTS COLLECTION USING CORRELATED TOPIC MODEL...ijnlc
The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational ExpectationMaximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.
A Novel Clustering Method for Similarity Measuring in Text DocumentsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONIJDKP
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
The Search of New Issues in the Detection of Near-duplicated Documentsijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
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.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
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).
With ever increasing number of documents on web and other repositories, the task of organizing and
categorizing these documents to the diverse need of the user by manual means is a complicated job, hence
a machine learning technique named clustering is very useful. Text documents are clustered by pair wise
similarity of documents with similarity measures like Cosine, Jaccard or Pearson. Best clustering results
are seen when overlapping of terms in documents is less, that is, when clusters are distinguishable. Hence
for this problem, to find document similarity we apply link and neighbor introduced in ROCK. Link
specifies number of shared neighbors of a pair of documents. Significantly similar documents are called as
neighbors. This work applies links and neighbors to Bisecting K-means clustering in identifying seed
documents in the dataset, as a heuristic measure in choosing a cluster to be partitioned and as a means to
find the number of partitions possible in the dataset. Our experiments on real-time datasets showed a
significant improvement in terms of accuracy with minimum time.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
TOPIC EXTRACTION OF CRAWLED DOCUMENTS COLLECTION USING CORRELATED TOPIC MODEL...ijnlc
The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational ExpectationMaximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.
A Novel Clustering Method for Similarity Measuring in Text DocumentsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONIJDKP
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
The Search of New Issues in the Detection of Near-duplicated Documentsijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
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.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for the ranking. However, existing computerized assessment systems do not provide all kinds of questions, namely open questions that require writing solutions. To overcome the challenges of the existing, the objective of this work is to achieve an intelligent evaluation system (IES) responding to the problems identified, and which adapts to the different types of questions, especially open-ended questions of which the answer requires sentence writing or programming.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
SEARCH OF INFORMATION BASED CONTENT IN SEMI-STRUCTURED DOCUMENTS USING INTERF...ijcsitcejournal
This paper proposes a semi-structured information retrieval model based on a new method for calculation
of similarity. We have developed CASISS (Calculation of Similarity of Semi-Structured documents)
method to quantify how two given texts are similar. This new method identifies elements of semi-structured
documents using elements descriptors. Each semi-structured document is pre-processed before the
extraction of a set of descriptors for each element, which characterize the contents of elements.It can be
used to increase the accuracy of the information retrieval process by taking into account not only the
presence of query terms in the given document but also the topology (position continuity) of these terms.
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical
relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations that is useful to retrieve sentences with identical content and performed the identification of those relations using SVMs. By exploiting the
rhetorical relations exist between sentences, we generate clusters of similar sentences from document sets. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of candidates summary. We evaluated our
method by measuring the cohesion and separation of the clusters and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in cluster-based text summarization.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
L0261075078
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 2 Issue 6 ǁ June. 2013 ǁ PP.75-78
www.ijesi.org 75 | Page
Algorithm for Semantic Based Similarity Measure
Sapna Chauhan1,
Pridhi Arora2
,Pawan Bhadana3
1
M.Tech Scholar of computer science & Engineering, BSAITM, Faridabad
2
Department of computer science & Engineering, BSAITM, Faridabad
3
Department of computer science & Engineering,BSAITM, Faridabad
ABSTRACT: In a document representation model the Semanti based Similarity Measure (SBSM), is
proposed. This model combines phrases analysis as well as words analysis with the use of propbank notation as
background knowledge to explore better ways of documents representation for clustering. The SBSM assigns
semantic weights to both document words and phrases. The new weights reflect the semantic relatedness
between documents terms and capture the semantic information in the documents. The SBSM finds similarity
between documents based on matching terms (phrases and words) and their semantic weights. Experimental
results show that the semantic based similarity Measure (SBSM) in conjunction with Propbank Notation has a
promising performance improvement for text clustering.
KEYWORDS: Click-through data, semantic similarity measure, marginalized kernel, event detection,
evolution pattern
I. INTRODUCTION
Information retrieval (IR) is the study of helping users to find information that matches their
information needs. Technically, IR studies the acquisition, organization, storage, retrieval, and distribution of
information. Historically, IR is about document retrieval, emphasizing document as the basic unit. Fig. 2.1 gives
a general architecture of an IR system. In Figure 2.1, the user with information need issues a query (user query)
to the retrieval system through the query operations module. The retrieval module uses the document index
to retrieve those documents that contain some query terms (such documents are likely to be relevant to the
query), compute relevance scores for them, and then rank the retrieved documents according to the scores..The
ranked documents are then presented to the user. The document collection is also called the text database,
which is indexed by the indexer for efficient retrieval
Fig. 2.1. A general IR system architecture
II. SIMILARITY MEASURE TECHNIQUES
There is various type of similarity measures such as:
1Cosine similarity measure
2 Jacard similarity measure
3 Euclidean Distance measure
4 Metric similarity measure
Cosine similarity: When documents are represented as term vectors, the similarity of two documents
corresponds to the correlation between the vectors. This is quantified as the cosine of the angle between vectors,
that is, the so-called cosine similarity. Cosine similarity is one of the most popular similarity measure applied to
text documents [14].
2. Algorithm For Semantic Based Similarity Measure
www.ijesi.org 76 | Page
Given two documents and their cosine similarity is.
SIMc =
Where and are m-dimensional vectors over the term set T = {t1,……tm}. Each dimension
represents a term with its weight in the document, which is non-negative. As a result, the cosine similarity is
non-negative and bounded between [0, 1].
An important property of the cosine similarity is its independence of document length. For example,
combining two identical copies of a document to get a new pseudo document d0, the cosine similarity
between, and d0 is 1, which means that these two documents are regarded to be identical. Meanwhile, given
another document l, and d0 will.
Have the same similarity value to l, that is, sim( , )= sim( , ) In other words, documents with
the same composition but different totals will be treated identically. Strictly speaking, this does not satisfy the
second condition of a metric, because after all the combination of two copies is a different object from the
original document. However, in practice, when the term vectors are normalized to a unit length such as 1, and in
this case the representation of d and d0 is the same.
Jacard similarity: The Jaccard coefficient, which is sometimes referred to as the Tanimoto coefficient,
measures similarity as the intersection divided by the union of the objects. For text document, the Jaccard
coefficient compares the sum weight of shared terms to the sum weight of terms that are present in either of the
two documents but are not the shared terms. The formal definition is [14].
SIMj =
The Jaccard coefficient is a similarity measure and ranges between 0 and 1. It is 1 When = and 0 when
and are disjoint, where 1 means the two objects are the same and 0 means they are completely different. The
corresponding distance measure is DJ = 1 – SIMj and we will use Dj instead in subsequent experiments.
Euclidean Distance: Euclidean distance is a standard metric for geometrical problems. It is the ordinary
distance between two points and can be easily measured with a ruler in two- or three-dimensional space.
Euclidean distance is widely used in clustering problems, including clustering text. It satisfies all the above four
conditions and therefore is a true metric. It is also the default distance measure used with the K-means
algorithm. Measuring distance between text documents, given two documents da and db represented by their
term vectors and respectively, the Euclidean distance of the two documents is defined as [14].
Where the term set is T = {t1, . . . , tm}. As mentioned previously, we use the tfidf value as term
weights, that is wt,a = tfidf(da, t).
Metric similarity: To qualify as a metric, a measure d must satisfy the following four conditions:
Let x and y be any two objects in a set and d(x, y) be the distance between x and y [14].
The distance between any two points must be nonnegative, that is, d(x, y) ≥ 0.
The distance between two objects must be zero if and only if the two objects are identical, that is, d(x, y) =
0 if and only if x = y.
Distance must be symmetric, that is, distance from x to y is the same as the distance from y to x, ie. d(x, y)
= d(y, x).
The measure must satisfy the triangle inequality, which is d(x, z) ≤ d(x, y) + d(y, z
III. RELATED WORK
Phrases convey local context information, which is essential in determining an accurate similarity
between documents. Toward this end, we devised a similarity measure based on matching phrases rather than
individual terms. This measure exploits the information extracted from the previous phrase matching algorithm
to better judge the similarity between the documents. This is related to the work of Isaacs and used a pair-wise
3. Algorithm For Semantic Based Similarity Measure
www.ijesi.org 77 | Page
probabilistic document similarity measure based on Information Theory. Although, they showed it could
improve on traditional similarity measures, but it is still fundamentally based on the vector space model
representation.The phrase similarity between two documents is calculated based on the list of matching phrases
between the two documents. From an information theoretic point of view, the similarity between two objects is
regarded as how much they share in common. The cosine and the Jaccard measures are indeed of such nature,
but they are essentially used as single-term based similarity measures.In Clustering of large collections of text
documents is a key process in providing a higher level of knowledge about the underlying inherent classification
of the documents. Web documents, in particular, are of great interest since managing, accessing, searching, and
browsing large repositories of web content requires efficient organization. Incremental clustering algorithms are
always preferred to traditional clustering techniques, since they can be applied in a dynamic environment such
as the Web. An incremental document clustering algorithm is introduced in this paper, which relies only on pair-
wise document similarity information. Clusters are represented using a Cluster Similarity Histogram, a concise
statistical representation of the distribution of similarities within each cluster, which provides a measure of
cohesiveness. The measure guides the incremental clustering process. Complexity analysis and experimental
results are discussed and show that the algorithm requires less computational time than standard methods while
achieving a comparable or better clustering quality
IV. PROPOSED WORK
There have been various attempts to label the sentence using semantic term labeler. Labeling the
thematic role in a sentence is known as thematic role analysis [29, 30]. In our approach we have used PropBank
[31] notation for labeling the each sentence of each document. Using the PropBank notation the sentence can be
labeled in verb argument structure in more than one way if a term used as a argument with different verbs in the
same sentence. Then it means the term has more significant semantic importance rather than others which has
been used less number of times. So the weight assigned to each term which can be a single word or phrase will
be based upon the count of how many times a term is used as an argument in the whole document in every verb
argument structure of sentences.
For example consider the following:
“We have noted, how some soft computing techniques, developed for optimization, have eventually
been used in data mining and others related fields.”
By using the PropBank notation the above sentence can be represented in three ways in verb argument structure.
- [ARG0 We] [verb noted] [ARG1 how some soft computing techniques, developed for optimization, have
eventually been used in data mining and others related fields]
-we have noted how [ARG1 some soft computing techniques][verb developed][ARGM_PNG for optimization]
have eventually been used in data mining and others related fields.
-We have noted how [ARG1 some soft computing techniques, developed for optimization] have [ARGM-TMP
eventually] been [verb used] [ARGM-LOC in data mining and other related fields].
After labeling the sentences some preprocessing is required which we have done using Porter Stemmer
Algorithm [32]. After performing the stemming we end up having some labeled terms. The same process we
have to do for query as well to get the labeled terms.
Now the algorithm given below is used to get the semantic similarity between the query and document. In the
algorithm below Di is a document, and Qi is query where i=1, 2, 3…..k; and k is a positive finite integer. LDi
and LQi are the list corresponding to document to document Di and query Qi to hold their labeled terms. A node
of the list contains labeled term as data, weight as the count of labeled term and link to next node.
Algorithm: Semantic based similarity measure
1. Di is a new document
2. LDi is empty list
3. for each sentence S in Di do
4. for each labeled term in S do
5. if(labeled term already in the list LDi)
6. Increase labeled-term count by 1;
7. else
8. {
9. Add a new node in the list
10. Node->data=labeled-term;
11. Labeled-term count =1
12. }
4. Algorithm For Semantic Based Similarity Measure
www.ijesi.org 78 | Page
13. End for
14. End for
15. SQ is a temporary variable.
16. For each labeled term in LQi do
17. If(labeled-term in LQi==labeled-term in LDi)
18. {
19. SQ= SQ + Labeled-term count in LDi * Labeled-term count in LQi;
20. }
21. End for
22. Semantic similarity=SQ/sum of count of all labeled terms in LDi;
If we use the above algorithm to compute the weight of each labeled term then we found the count for labeled
term “soft-computing”, “developed” and “optimization” are highest. This shows that these terms are having
more semantic significance rather than others labeled terms.
V. EXPERIMENTAL RESULT
The document collection we have used to test our algorithm is cisi dataset. The dataset has 1414
documents and 35 user queries. We have implemented the algorithm using MATLAB software. For finding
cosine and jaccard similarity we have used TMG:A MATLAB TOOLBOX. TMG is basically text to matrix
generator. We have used f-score as a fitness function. Overall fitness we have calculated in terms of f-score. We
have taken a population of random weights in which each individual represent the weights for each similarity
measure. We have run the algorithm upto 40 generations and got the optimized weight 0.932, 0.767, 0.621
respectfully. Fig. 5.1 below has shown the f-score over generations. Fig. 5.2 and Fig. 5.3 have shown the
precision on various level of recall for cosine and jaccard respectively. While Figure 5.4 has shown the precision
recall curve for our proposed semantic-based-combined-similarity- measure.
CONCLUSION
In our work we have combined various similarity measures to generate an effective matching function.
Effectiveness of the matching function depends upon all similarity measures based on weight given by genetic
algorithm. So to have an effective matching function both semantic and syntactic aspects should be taken into
consideration while choosing similarity measures. We observed that no significant improvement has been seen
in average fitness (f- score) value of overall generation after 40-50 iterations. The effect of crossover operator
beyond this stage becomes insignificant due to very small variation in individual for particular generation.
Applying fuzzy theory in our approach can control genetic algorithm and may lead to better results.
REFERENCES
[1.] Bing Liu, Web Data Mining, Springer, ISBN-10 3-540-37881-2
[2.] J. R. Quinlan. C4.5: Program for Machine Learning. Morgan Kaufmann, 1992
[3.] B. Liu, C. W. Chin, and H. T. Ng. Mining Topic-Specific Concepts and Definitions on the Web. In Proc. of the 12th Intl. World Wide Web Conf.
(WWW’03), pp. 251– 260, 2003
[4.] J. L. Klavans, and S. Muresan. DEFINDER: Rule-Based Methods for the Extraction of Medical Terminology and Their Associated Definitions from
On-line Text. In Proc. of American Medical Informatics Assoc., 2000
[5.] R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval. ACM Press / Addison-Wesley, 1999
[6.] G. Bordogna and G. Pasi. Modeling vagueness in information retrieval. Lectures on information retrieval, pages 207–241, 2001
[7.] J. N. K. Liu. An intelligent system integrated with fuzzy ontology for product recommendation and retrieval. In FS’07: Proceedings of the 8th
Conference on 8th WSEAS International Conference on Fuzzy Systems, pages 180–185, Stevens Point, Wisconsin, USA, 2007. World Scientific and
Engineering Academy and Society (WSEAS).
[8.] R. Pereira, I. Ricarte, and F. Gomide. Fuzzy relational ontological model in information search systems. In Elie Sanchez. (Org.). Fuzzy Logic and The
Semantic Web, pages 395–412, Amsterdan, 2006. Elsevier B. V
[9.] M. F. Porter. An Algorithm for Suffix Stripping. Program, 14(3), pp 130-137, 1980
[10.] Brin, S. and L. Page (1998). The anatomy of a large-scale hyper textual Web search engine. Computer Networks and ISDN Systems 30 (1-7), 107-117.