This document discusses a framework for acquiring vague knowledge from socially generated content in an enterprise setting. It involves setting up a microblogging platform for employees to discuss topics related to the enterprise. Vague knowledge assertions are extracted from posts and used to determine fuzzy degrees and membership functions for concepts, relations, and datatypes in a fuzzy ontology representing the enterprise's knowledge. The strength of each assertion is calculated based on social characteristics of the discussions. Future work involves applying the framework in a real enterprise to evaluate its ability to acquire vague knowledge and accuracy of the learned fuzzy ontology.
The emergence in recent years of initiatives like the Linked Open Data (LOD) has led to a significant increase in the amount of structured semantic data on the Web. In this paper we argue that the shareability and wider reuse of such data can very often be hampered by the existence of vagueness within it, as this makes the data’s meaning less explicit. Moreover, as a way to reduce this problem,
we propose a vagueness metaontology that may represent in an explicit way the nature and characteristics of vague elements within semantic data.
Troubleshooting and Optimizing Named Entity Resolution Systems in the IndustryPanos Alexopoulos
Named Entity Resolution (NER) is an information extraction task that involves detecting mentions of named entities within texts and mapping them to
their corresponding entities in a given knowledge resource. Systems and frameworks for performing NER have been developed both by the academia and the industry with different features and capabilities. Nevertheless, what all approaches have in common is that their satisfactory performance in a given scenario does not constitute a trustworthy predictor of their performance in a different one, the reason being the scenario’s different characteristics (target entities, input texts, domain knowledge etc.). With that in mind, we describe a metric-based Diagnostic Framework that can be used to identify the causes behind the low performance of NER systems in industrial settings and take appropriate actions to increase it.
The phenomenon of vagueness, manifested by terms and concepts like Tall, Red, Modern, etc., is quite common in human knowledge and it is related to our inability to precisely determine the extensions of such terms due to their blurred applicability boundaries. In the context of Ontologies and Semantic Web, vagueness is primarily treated by means of Fuzzy Ontologies, namely extensions of classical ontologies that apply truth degrees to vague ontological elements in an effort to quantify their vagueness and reason with it. Nevertheless, while a number of fuzzy conceptual formalisms and fuzzy ontology language extensions for representing vagueness in ontologies have been proposed by the community, the methodological issues entailed within the development process of such ontologies have been rather neglected. In this talk we position vagueness within the overall lifecycle of semantic information management and we present IKARUS-Onto, a methodology for engineering fuzzy ontologies that covers all typical ontology development stages, from specification to validation.
Towards Purposeful Reuse of Semantic Datasets Through Goal-Driven SummarizationPanos Alexopoulos
The emergence in the last years of initiatives like the Linked Open Data (LOD) has led to a significant increase of the amount of structured semantic data on the Web. Nevertheless, the wider reuse of such public semantic data is inhibited by the difficulty for users to decide whether a given dataset is actually suitable for their needs. This is because semantic datasets typically cover diverse domains, do not follow a unified way of organizing the knowledge and may differ in a number of dimensions. With that in mind, in this paper, we report our work in progress on a goal-driven dataset summarization approach that may facilitate better understanding and reuse-oriented evaluation of available semantic data.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
"Knowing about the user’s feedback can come to a greater aid in knowing the user as well as improving the organization. Here an example of student’s data is taken for study purpose. Analyzing the student feedback will help to help to address student related problems and help to make teaching more student oriented. Prashali S. Shinde | Asmita R. Kanase | Rutuja S. Pawar | Yamini U. Waingankar ""Sentiment Analysis of Feedback Data"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23090.pdf
Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/23090/sentiment-analysis-of-feedback-data/prashali--s-shinde"
The emergence in recent years of initiatives like the Linked Open Data (LOD) has led to a significant increase in the amount of structured semantic data on the Web. In this paper we argue that the shareability and wider reuse of such data can very often be hampered by the existence of vagueness within it, as this makes the data’s meaning less explicit. Moreover, as a way to reduce this problem,
we propose a vagueness metaontology that may represent in an explicit way the nature and characteristics of vague elements within semantic data.
Troubleshooting and Optimizing Named Entity Resolution Systems in the IndustryPanos Alexopoulos
Named Entity Resolution (NER) is an information extraction task that involves detecting mentions of named entities within texts and mapping them to
their corresponding entities in a given knowledge resource. Systems and frameworks for performing NER have been developed both by the academia and the industry with different features and capabilities. Nevertheless, what all approaches have in common is that their satisfactory performance in a given scenario does not constitute a trustworthy predictor of their performance in a different one, the reason being the scenario’s different characteristics (target entities, input texts, domain knowledge etc.). With that in mind, we describe a metric-based Diagnostic Framework that can be used to identify the causes behind the low performance of NER systems in industrial settings and take appropriate actions to increase it.
The phenomenon of vagueness, manifested by terms and concepts like Tall, Red, Modern, etc., is quite common in human knowledge and it is related to our inability to precisely determine the extensions of such terms due to their blurred applicability boundaries. In the context of Ontologies and Semantic Web, vagueness is primarily treated by means of Fuzzy Ontologies, namely extensions of classical ontologies that apply truth degrees to vague ontological elements in an effort to quantify their vagueness and reason with it. Nevertheless, while a number of fuzzy conceptual formalisms and fuzzy ontology language extensions for representing vagueness in ontologies have been proposed by the community, the methodological issues entailed within the development process of such ontologies have been rather neglected. In this talk we position vagueness within the overall lifecycle of semantic information management and we present IKARUS-Onto, a methodology for engineering fuzzy ontologies that covers all typical ontology development stages, from specification to validation.
Towards Purposeful Reuse of Semantic Datasets Through Goal-Driven SummarizationPanos Alexopoulos
The emergence in the last years of initiatives like the Linked Open Data (LOD) has led to a significant increase of the amount of structured semantic data on the Web. Nevertheless, the wider reuse of such public semantic data is inhibited by the difficulty for users to decide whether a given dataset is actually suitable for their needs. This is because semantic datasets typically cover diverse domains, do not follow a unified way of organizing the knowledge and may differ in a number of dimensions. With that in mind, in this paper, we report our work in progress on a goal-driven dataset summarization approach that may facilitate better understanding and reuse-oriented evaluation of available semantic data.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
"Knowing about the user’s feedback can come to a greater aid in knowing the user as well as improving the organization. Here an example of student’s data is taken for study purpose. Analyzing the student feedback will help to help to address student related problems and help to make teaching more student oriented. Prashali S. Shinde | Asmita R. Kanase | Rutuja S. Pawar | Yamini U. Waingankar ""Sentiment Analysis of Feedback Data"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23090.pdf
Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/23090/sentiment-analysis-of-feedback-data/prashali--s-shinde"
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Comparative Study on Lexicon-based sentiment analysers over Negative sentimentAI Publications
Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.
The big data phenomenon has confirmed the achievement of data access transformation. Sentiment analysis (SA) is one of the most exploited area and used for profit-making purpose through business intelligence applications. This paper reviews the trends in SA and relates the growth in the area with the big data era.
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.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
CrowdTruth for medical relation extraction - WAI talkAnca Dumitrache
I will present the CrowdTruth (http://crowdtruth.org/) approach to performing relation extraction from medical data. CrowdTruth exploits inter-annotator disagreement as a useful signal, allowing us to evaluate data quality, such as ambiguity and vagueness at the sentence level, worker quality, and the quality of the target semantics. I will introduce a workflow for generating gold standard annotations for medical relation extraction through a series of crowdsourcing tasks. Then I will present an evaluation of the crowd data by comparing it with the current gold standard in medical relation extraction. The evaluation is performed by training a relation extraction classifier with both datasets, and comparing the results for F1 measure in a cross-validation experiment.
Supervised Sentiment Classification using DTDP algorithmIJSRD
Sentiment analysis is the process widely used in all fields and it uses the statistical machine learning approach for text modeling. The primarily used approach is Bag-of-words (BOW). Though, this technique has some limitations in polarity shift problem. Thus, here we propose a new method called Dual sentiment analysis (DSA) which resolves the polarity shift problem. Proposed method involves two approaches such as dual training and dual prediction (DPDT). First, we propose a data expansion technique by creating a reversed review for training data. Second, dual training and dual prediction algorithm is developed for doing analysis on sentiment data. The dual training algorithm is used for learning a sentiment classifier and the dual prediction algorithm is developed for classifying the review by considering two sides of one review.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
https://www.youtube.com/watch?v=nvlHJgRE3pU
Won ITAC Graduation Projects Competition, ITAC ID: GP2015.R10.75
A web application that analyze big volumes of product reviews, social networks posts and tweets related to a given product. Then, present these results of this big data analytical job in a user friendly, understandable, and easily interpreted manner that can be used by different customers for different purposes.
Technologies used:
1- Hadoop
2- Hadoop Streaming
3- R Statistical
4- PHP
5- Google Charts API
please just write the bulk of the paper with in text citations and.docxrandymartin91030
please just write the bulk of the paper with in text citations and a work cited page as well don’t worry about title page and header and footer I will edit that upon completion.
To access articles in the Library for this class and others, please refer to the instructions on the Syllabus and in Case 1.
For the session long project, choose one area within the health issue below as your research topic. You will focus on the same topic for your SLP throughout the session.
Traumatic brain injury
Before you begin, read the instructions and expectations carefully -- this is not a typical report-style assignment.
Narrow down the topic to a certain part of the population (i.e. an age group, gender, a certain race or ethnicity, or a particular geographic area). It will help to do some research before choosing your focus, so you can see what literature will be available to use throughout the session. Look at the SLP in Modules 2 - 5 so you can plan ahead as approporiate.
Use credible professional sources such as ProQuest or EBSCO articles, or Websites from a university, government, or nonprofit organization to search for information about the issue. Consumer sources such as e-magazines, newspapers, and .com sites are not appropriate.
1. Introduce the topic and write a brief background about the scope of the problem. What is the health effect? How many people does it affect? Is there a treatment or a cure? What kind of research is being conducted about the problem? This part of the paper should be approximately 1 page.
2. Now, based on what you learned about the topic, think about what the gaps in knowledge seem to be. They are often stated in the "conclusions" of research articles. Using that information, do the following:
State a properly phrased health-related research question that you would like to answer if you were a researcher. Review the information in the link provided on the Background Information page so you are clear as to what a research question is. This should not be a paragraph or an explanation, just a research question.
3. Now, formulate a specific hypothesis to investigate that research question. Again, this should not be a paragraph or an explanation, just a properly stated hypothesis. Review the information in the links provided on the Background Information page so you are clear as to what a hypothesis is.
ASSIGNMENT EXPECTATIONS: Please read before completing assignments.
· Copy the actual assignment from this page onto the cover page of your paper (do this for all papers in all courses).
· Assignment should be 2 pages in length (double-spaced).
· Please use major sections corresponding to the major points of the assignment, and where appropriate use sub-sections (with headings).
· Remember to write in a Scientific manner (try to avoid using the first person except when describing a relevant personal experience).
· Quoted material should not exceed 10% of the total paper (since the focus of these assignments is on independent t.
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Comparative Study on Lexicon-based sentiment analysers over Negative sentimentAI Publications
Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.
The big data phenomenon has confirmed the achievement of data access transformation. Sentiment analysis (SA) is one of the most exploited area and used for profit-making purpose through business intelligence applications. This paper reviews the trends in SA and relates the growth in the area with the big data era.
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.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
CrowdTruth for medical relation extraction - WAI talkAnca Dumitrache
I will present the CrowdTruth (http://crowdtruth.org/) approach to performing relation extraction from medical data. CrowdTruth exploits inter-annotator disagreement as a useful signal, allowing us to evaluate data quality, such as ambiguity and vagueness at the sentence level, worker quality, and the quality of the target semantics. I will introduce a workflow for generating gold standard annotations for medical relation extraction through a series of crowdsourcing tasks. Then I will present an evaluation of the crowd data by comparing it with the current gold standard in medical relation extraction. The evaluation is performed by training a relation extraction classifier with both datasets, and comparing the results for F1 measure in a cross-validation experiment.
Supervised Sentiment Classification using DTDP algorithmIJSRD
Sentiment analysis is the process widely used in all fields and it uses the statistical machine learning approach for text modeling. The primarily used approach is Bag-of-words (BOW). Though, this technique has some limitations in polarity shift problem. Thus, here we propose a new method called Dual sentiment analysis (DSA) which resolves the polarity shift problem. Proposed method involves two approaches such as dual training and dual prediction (DPDT). First, we propose a data expansion technique by creating a reversed review for training data. Second, dual training and dual prediction algorithm is developed for doing analysis on sentiment data. The dual training algorithm is used for learning a sentiment classifier and the dual prediction algorithm is developed for classifying the review by considering two sides of one review.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
https://www.youtube.com/watch?v=nvlHJgRE3pU
Won ITAC Graduation Projects Competition, ITAC ID: GP2015.R10.75
A web application that analyze big volumes of product reviews, social networks posts and tweets related to a given product. Then, present these results of this big data analytical job in a user friendly, understandable, and easily interpreted manner that can be used by different customers for different purposes.
Technologies used:
1- Hadoop
2- Hadoop Streaming
3- R Statistical
4- PHP
5- Google Charts API
please just write the bulk of the paper with in text citations and.docxrandymartin91030
please just write the bulk of the paper with in text citations and a work cited page as well don’t worry about title page and header and footer I will edit that upon completion.
To access articles in the Library for this class and others, please refer to the instructions on the Syllabus and in Case 1.
For the session long project, choose one area within the health issue below as your research topic. You will focus on the same topic for your SLP throughout the session.
Traumatic brain injury
Before you begin, read the instructions and expectations carefully -- this is not a typical report-style assignment.
Narrow down the topic to a certain part of the population (i.e. an age group, gender, a certain race or ethnicity, or a particular geographic area). It will help to do some research before choosing your focus, so you can see what literature will be available to use throughout the session. Look at the SLP in Modules 2 - 5 so you can plan ahead as approporiate.
Use credible professional sources such as ProQuest or EBSCO articles, or Websites from a university, government, or nonprofit organization to search for information about the issue. Consumer sources such as e-magazines, newspapers, and .com sites are not appropriate.
1. Introduce the topic and write a brief background about the scope of the problem. What is the health effect? How many people does it affect? Is there a treatment or a cure? What kind of research is being conducted about the problem? This part of the paper should be approximately 1 page.
2. Now, based on what you learned about the topic, think about what the gaps in knowledge seem to be. They are often stated in the "conclusions" of research articles. Using that information, do the following:
State a properly phrased health-related research question that you would like to answer if you were a researcher. Review the information in the link provided on the Background Information page so you are clear as to what a research question is. This should not be a paragraph or an explanation, just a research question.
3. Now, formulate a specific hypothesis to investigate that research question. Again, this should not be a paragraph or an explanation, just a properly stated hypothesis. Review the information in the links provided on the Background Information page so you are clear as to what a hypothesis is.
ASSIGNMENT EXPECTATIONS: Please read before completing assignments.
· Copy the actual assignment from this page onto the cover page of your paper (do this for all papers in all courses).
· Assignment should be 2 pages in length (double-spaced).
· Please use major sections corresponding to the major points of the assignment, and where appropriate use sub-sections (with headings).
· Remember to write in a Scientific manner (try to avoid using the first person except when describing a relevant personal experience).
· Quoted material should not exceed 10% of the total paper (since the focus of these assignments is on independent t.
Book recommendation system using opinion mining techniqueeSAT Journals
Abstract
The purpose of this project is to create and deploy a book recommendation system that will help people to recommend books. Our project is the online system that helps people to get reviews about the books and give recommendations to them. Online recommendation system will also allow the users to give feedback comments that will be analyzed by opinion mining technique so as to imply the true nature of the comment .i .e whether the comment is positive, negative or a neutral one. People then searching for a particular book will be displayed with the top 10(approx.) books on that particular subject based on the reviews and feedbacks given by the earlier people who read the same book.
Keywords: - Books, Recommendation, User reviews, Opinion mining, Feedback
A set of practical strategies and techniques for tackling vagueness in data modeling and creating models that are semantically more accurate and interoperable.
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
Introduction to RAG (Retrieval Augmented Generation) and its applicationKnoldus Inc.
Embark on a comprehensive exploration of Retrieval Augmented Generation (RAG) in this illuminating session. Delve into the architecture seamlessly merging retrieval and generation models and uncover its versatile applications. From refining search processes to enhancing content generation, RAG is reshaping the landscape of natural language processing. Join us for a brief yet comprehensive Introduction to RAG and its transformative potential, along with insights into its applications.
Become familiar with the User Story approach to formulating Product Backlog Items and how it can be implemented to improve the value and quality of the product by facilitating a user-centric approach to development
Pair Programming with a Large Language ModelKnoldus Inc.
In this session we will Learn how LLMs can enhance, debug, and document our code. AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets. We will learn how to use an LLM in pair programming to: Simplify and improve your code. Write test cases. Debug and refactor your code. Explain and document any complex code written in any coding language
Agile Mumbai 2022 - Rohit Handa | Combining Human and Artificial Intelligence...AgileNetwork
Agile Mumbai 2022
Combining Human and Artificial Intelligence for Business Agility
Rohit Handa
Director, Digital Products & Platforms, HCL Technologies Ltd
Improved Interpretability and Explainability of Deep Learning Models.pdfNarinder Singh Punn
This file aims to give a thorough overview of the current state and future prospects of interpretability and explainability in deep learning, making it a valuable resource for students, researchers, and professionals in the field. The post will comprehensively cover the following aspects:
Introduction to Interpretability and Explainability: Explaining what these concepts mean in the context of deep learning and why they are critical.
The Need for Transparency: Discussing the importance of interpretability and explainability in AI, focusing on ethical considerations, trust in AI systems, and regulatory compliance.
Key Concepts and Definitions: Clarifying terms like “black-box” models, interpretability, explainability, and their relevance in deep learning.
Methods and Techniques:
Visualization Techniques: Detailing methods like feature visualization, attention mechanisms, and tools like Grad-CAM.
Feature Importance Analysis: Exploring techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for understanding feature contributions.
Decision Boundary Analysis: Discussing methods to analyze and visualize the decision boundaries of models.
Practical Implementations and Code Examples: Providing examples of how these techniques can be implemented using popular deep learning frameworks like TensorFlow or PyTorch.
Case Studies and Real-World Applications: Presenting real-world scenarios where interpretability and explainability have played a vital role, especially in fields like healthcare, finance, and autonomous systems.
Challenges and Limitations: Addressing the challenges in achieving interpretability and the trade-offs with model complexity and performance.
Future Directions and Research Trends: Discussing ongoing research, emerging trends, and potential future advancements in making deep learning models more interpretable and explainable.
Conclusion: Summarizing the key takeaways and the importance of continued efforts in this area.
References and Further Reading: Providing a list of academic papers, articles, and resources for readers who wish to delve deeper into the topic.
Section 1: Introduction to Interpretability and Explainability
The field of deep learning has witnessed exponential growth in recent years, leading to significant advancements in various applications such as image recognition, natural language processing, and autonomous systems. However, as these neural network models become increasingly complex, they often resemble “black boxes”, where the decision-making process is not transparent or understandable to users. This obscurity raises concerns, especially in critical applications, and underscores the need for interpretability and explainability in deep learning models.
What are Interpretability and Explainability?
Interpretability: This refers to the degree to which a human can understand the cause of a decision made by a machine learning model. It’s about answering the questio
Three experiments I have done with data science. Related to text analysis, integration. Focusing on the learning's rather than details on how it was done with source code. I feel it is important to see this subject in relation to business problems rather than as pure branch of Statistics. Focusing on what has to be done enabled me to find the right solution from a complicated and very interesting subject.
I N N O V A T I O N N E T W O R K , I N C . www.innone.docxeugeniadean34240
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
L o g i c M o d e l W o r k b o o k
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
L o g i c M o d e l W o r k b o o k
T a b l e o f C o n t e n t s
P a g e
Introduction - How to Use this Workbook .....................................................................2
Before You Begin .................................................................................................................3
Developing a Logic Model .................................................................................................4
Purposes of a Logic Model ............................................................................................... 5
The Logic Model’s Role in Evaluation ............................................................................ 6
Logic Model Components – Step by Step ....................................................................... 6
Problem Statement: What problem does your program address? ......................... 6
Goal: What is the overall purpose of your program? .............................................. 7
Rationale and Assumptions: What are some implicit underlying dynamics? ....8
Resources: What do you have to work with? ......................................................... 9
Activities: What will you do with your resources? ................................................ 11
Outputs: What are the tangible products of your activities? ................................. 13
Outcomes: What changes do you expect to occur as a result of your work?.......... 14
Outcomes Chain ....................................................................................... 16
Outcomes vs. Outputs ............................................................................. 17
Logic Model Review ...........................................................................................................18
Appendix A: Logic Model Template
Appendix B: Worksheet: Developing an Outcomes Chain
Logic Model Workbook
Page 2
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
I n t r o d u c t i o n - H o w t o U s e t h i s W o r k b o o k
Welcome to Innovation Network’s Logic Model Workbook. A logic model is a commonly-used
tool to clarify and depict a program within an organization. You may have heard it described as
a logical framework, theory of change, or program matrix—but the purpose is usually the same:
to graphically depict your program, initiative, project or even the sum total of all of your
organization’s work. It also serves as a
foundation for program planning and
evaluation.
This workbook is a do-it-yourself guide to
the concepts and use of the logic model. It
describes the steps necessary for you to
create logic models fo.
I N N O V A T I O N N E T W O R K , I N C . www.innone.docxsheronlewthwaite
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
L o g i c M o d e l W o r k b o o k
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
L o g i c M o d e l W o r k b o o k
T a b l e o f C o n t e n t s
P a g e
Introduction - How to Use this Workbook .....................................................................2
Before You Begin .................................................................................................................3
Developing a Logic Model .................................................................................................4
Purposes of a Logic Model ............................................................................................... 5
The Logic Model’s Role in Evaluation ............................................................................ 6
Logic Model Components – Step by Step ....................................................................... 6
Problem Statement: What problem does your program address? ......................... 6
Goal: What is the overall purpose of your program? .............................................. 7
Rationale and Assumptions: What are some implicit underlying dynamics? ....8
Resources: What do you have to work with? ......................................................... 9
Activities: What will you do with your resources? ................................................ 11
Outputs: What are the tangible products of your activities? ................................. 13
Outcomes: What changes do you expect to occur as a result of your work?.......... 14
Outcomes Chain ....................................................................................... 16
Outcomes vs. Outputs ............................................................................. 17
Logic Model Review ...........................................................................................................18
Appendix A: Logic Model Template
Appendix B: Worksheet: Developing an Outcomes Chain
Logic Model Workbook
Page 2
I N N O V A T I O N N E T W O R K , I N C .
www.innonet.org • [email protected]
I n t r o d u c t i o n - H o w t o U s e t h i s W o r k b o o k
Welcome to Innovation Network’s Logic Model Workbook. A logic model is a commonly-used
tool to clarify and depict a program within an organization. You may have heard it described as
a logical framework, theory of change, or program matrix—but the purpose is usually the same:
to graphically depict your program, initiative, project or even the sum total of all of your
organization’s work. It also serves as a
foundation for program planning and
evaluation.
This workbook is a do-it-yourself guide to
the concepts and use of the logic model. It
describes the steps necessary for you to
create logic models fo ...
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Learning Vague Knowledge From Socially Generated Content in an Enterprise Framework
1. Learning Vague Knowledge From Socially
Generated Content in an Enterprise Framework
Panos Alexopoulos, John Pavlopoulos, Phivos Mylonas
1st Mining Humanistic Data Workshop,
Halkidiki, Greece, September 27th, 2012
2. 2
Introduction
Background and Problem Definition
Approach Overview and Rationale
Vague Knowledge Acquisition
Conceptualization and Initialization
Microblogging Framework
Extraction of Vague Knowledge
Assertions
Assertion Strength Assessment
Generation of Membership
Functions and Fuzzy Degrees
Conclusions and Future Work
Agenda
3. 3
Background
Introduction
●Knowledge Management is a discipline that aims to enable enterprises and
organizations to fully leverage their knowledge in their effort to grow more
efficient and competitive.
●This leverage involves several key objectives such as:
● Identification, gathering and organization of existing knowledge
● Sharing and reusing of this knowledge for different applications and
users and facilitation of new knowledge creation
●However, a dimension of this knowledge that has so far been inadequately
considered is vagueness.
4. 4
Vagueness
Introduction
● Vagueness is manifested through
predicates that admit borderline cases,
i.e. cases where it is unclear whether or
not the predicate applies
● E.g. Tall, High, Experienced etc.
Definition
● Degree Vagueness: Lack of crisp
boundaries between application and non
application in some dimension.
● E.g. Tall, Rich, Recent
● Combinatory Vagueness: Inability to
clearly define adequate applicability
criteria.
● Π.χ. Modern, Expert, Religion
Types of Vagueness
● Uncertainty: E.g. Today it might rain
● Inexactness: E.g. Someone has height
between 170 and 180 cm.
Frequently Confused Concepts
● A person can be tall with respect to the
average population height and not tall
with respect to professional basketball
players
● This doesn’t mean that a vague predicate
can stop being vague in a different
context but merely that the interpretation
of its vagueness can change.
Context Dependence
5. 5
Fuzzy Ontologies
Introduction
●Fuzzy Ontologies are extensions of classical ontologies that allow the
assignment of truth degrees to vague ontological elements.
●For example:
● “The project's budget is satisfactory to a degree of 0.7"
● “Jane is an expert at Artificial Intelligence to a degree of 0.5".
6. 6
Fuzzy Ontological Elements
Introduction
● A fuzzy ontology concept may have
instances that belong to it at certain degrees.
● E.g. John is a TallPerson to a degree of 0.5.
Fuzzy Concepts
● A fuzzy ontology relation links concept
instances at certain degrees.
● Π.χ. John is expert at Machine Learning to a
degree of 0.9.
● Similarly, a fuzzy attribute assigns literal
values to concept instances at certain
degrees.
Fuzzy Relations and Attributes
● A fuzzy datatype consists of a set of vague
terms which may be used within the
ontology as attribute values.
● Π.χ. Low, Average, High for the
attribute Project Budget.
● Each term is mapped to a fuzzy set that
defines the term’s meaning.
Fuzzy Datatypes
7. 7
Problem Definition
Introduction
● An important bottleneck in developing fuzzy ontologies is the definition of the degrees and
membership functions of the fuzzy elements.
● High level of subjectivity.
● Context dependence.
● Thus the problem is defined as follows: Given a fuzzy enterprise ontology, what are the
optimal fuzzy degrees and membership functions that should be assigned to its
elements (concepts, relations and datatypes) in order to represent the domain’s vagueness
as accurately as possible?
● E.g. given the fuzzy concept CompanyCompetitor and a set of individual components,
what is the degree to which each of these companies is considered a competitor?
● E.g. given the fuzzy relation isExpertAt and a set of related through it pairs of
instances (e.g. persons related to business areas), what is the degree to which the
relation between these pairs actually stands?
● E.g. given the fuzzy datatype ProjectBudget and the terms it consists of (e.g. low,
average, high), what are the membership functions of the fuzzy sets that best reflect
the meaning of each of these terms?
8. 8
Process Overview
Vague Knowledge Acquisition
1. Identification within the enterprise of vague knowledge and conceptual modeling of it
in the form of a fuzzy enterprise ontology.
2. Setting up of a microblogging platform in which the members of the enterprise are
expected to participate and perform discussions and information exchange on all
aspects regarding the enterprise and its environment.
3. Detection and extraction from the user generated platform’s content of vague
knowledge assertions, namely statements related to the elements already defined in
the fuzzy enterprise ontology.
4. Calculation for each vague assertion of a strength value based on the utilization of
various characteristics of the discussions they are involved in.
5. Aggregation of these assertions and automated generation of fuzzy degrees and
membership functions.
9. 9
Conceptualization and Initialization with IKARUS-Onto
Vague Knowledge Acquisition
Acquire
Crisp Ontology
Define Fuzzy
Ontology Elements
Formalize Fuzzy
Elements
Validate Fuzzy
Ontology
● Establish a basis for the development
of the fuzzy ontology.
● Develop or acquire the crisp ontology.
● Justify and estimate the necessary
work for the fuzzy ontology
development.
● Ensure existence of vagueness in the
domain.
● Ensure vagueness is a requirement.
● Conceptualization of vagueness in an
explicit way and shareable way.
● Definition of fuzzy ontology elements
● Specification of fuzzy degrees and
membership functions by experts.
● Make fuzzy ontology machine-
processable.
● Select fuzzy ontology language and
use it to represent the defined
elements.
● Ensure adequate and correct
capturing of the domain’s vagueness
● Check correctness, accuracy,
completeness and consistency.
Step Goals Actions
Establish Need
for Fuzziness
10. 10
Microblogging Platform
Vague Knowledge Acquisition
● The microblogging platform we adopt for the purposes of this work is miKrow, an
intra-enterprise semantic microblogging tool that allows its end-users to share short
messages expressing what are they working at.
● The platform works mostly like Twitter, with two important enhancements:
● When users reply to a message they are able to denote the nature of their reply
by using the predefined hashtags #support and #attack.
● Users are also able to denote their agreement or disagreement to a message
through a rating functionality
● These two features allows us to use the platform as an argumentation tool and
capture the disagreements and debates over vague knowledge statements that may
occur.
12. 12
Extraction of Vague Knowledge Assertions
Vague Knowledge Acquisition
● Vague knowledge assertions are practically statements related to the elements of
fuzzy ontology.
● E.g. The assertion “The budget for the project X is low" is related to the fuzzy
datatype “ProjectBudget”
● E.g. The assertion “John is expert at ontologies" is related to the fuzzy relation
“isExpertAt”.
● Our goal is to detect and extract such assertions from the user messages so that we
can use them for determining the fuzzy degrees of their respective elements.
● To achieve this, we use an in-house developed semantic annotation tool that, given a
fuzzy ontology, is able to recognize such assertions within a piece of text.
● An important factor that contributes to higher levels of precision for this detection is
the fact that microblogging messages are short.
● In any case, the detection process may be performed in a semi-automatic fashion
where the correctness of the extracted assertions could be checked by the system’s
administrator.
13. 13
Knowledge Assertion Strength Calculation
Vague Knowledge Acquisition
● To calculate the strength of the extracted vague assertions we aggregate the strength
of messages that are directly or indirectly related to these assertions.
● Strength of a message depends on:
● On the number of agreements and disagreements it has received by the users.
● On the number and strength of attacking and supporting messages.
● On the overall influence of the user who has published the message. This is
generally relevant to the number of users that follow the message publisher but
also to the person’s expertise on the messages topic.
14. 14
Generation of Membership Functions and Fuzzy Degrees
Vague Knowledge Acquisition
● Given an instance and a fuzzy concept we consider all the relevant assertions along
with their strengths:
● A1: “Accenture is a Competitor to a strength of 0.5.”
● A2: “Accenture is a Competitor to a strength of 0.7.”
● A3: “Accenture is a Competitor to a strength of 0.4.”
● To aggregate these strengths into a single degree we:
● Compute the mean value of all the strength values
● We estimate confidence intervals and we only allow those mean values with
significance level of no less than 0.05.
● In most cases we expect to have most assertions gathered very close to a single
mean value which can then be considered as the degree of the relevant ontological
statement.
● In case many assertions seem to be out of the confidence intervals, then that’s an
indication that the statement’s interpretation might be context-dependent.
Fuzzy Concept and Relation Assertions
15. 15
Generation of Membership Functions and Fuzzy Degrees
Vague Knowledge Acquisition
● Given a term and a fuzzy datatype we consider all the relevant assertions along with
their strengths:
● A1: “A budget of 20,000 is low to a strength of 0.5.”
● A2: “A budget of 24,000 is low to a strength of 0.3.”
● A3: “A budget of 26,000 is average to a strength of 0.6.”
● A4: “A budget of 30,000 is high to a strength of 0.4.”
● Based on the pairs of these values and strengths, we determine the optimal function
fuzzy membership function that links them.
● This is a well-studied problem in the area of fuzzy expert systems and several related
methods that construct such functions from training data are available.
Fuzzy Datatypes
16. 16
Key Points
Conclusions and Future Work
● We proposed a framework for automatic vague knowledge acquisition in enterprise
settings based on:
● A semantically enhanced microblogging system.
● A fuzzy ontology learning process that acts upon the social content produced by
the enterprise’s people.
● The key characteristic of our approach is the utilization of the content’s social
features in order to assign strengths to vague assertions.
● The relative agreement and support that microposts enjoy.
● The status and influence of the users.
17. 17
Future Work
Conclusions and Future Work
● In the future we intend to apply our framework in an actual enterprise setting and
evaluate its effectiveness in acquiring vague knowledge.
● This evaluation will focus on two dimensions:
● The ability of the microblogging approach in producing rich social context over
the vague knowledge.
● The accuracy of the fuzzy ontology degrees and membership functions learned
using this context.
18. 18
Contact iSOCO
Where we are
Questions?
Barcelona
Tel +34 935 677 200
Edificio Testa A
C/ Alcalde Barnils, 64-68
St. Cugat del Vallès
08174 Barcelona
Valencia
Tel +34 963 467 143
Oficina 107
C/ Prof. Beltrán Báguena, 4
46009 Valencia
Pamplona
Tel +34 948 102 408
Parque Tomás
Caballero, 2, 6º-4ª
31006 Pamplona
Dr. Panos Alexopoulos
Senior Researcher
palexopoulos@isoco.com
Madrid
Tel +34 913 349 797
Av. del Partenón, 16-18, 1º7ª
Campo de las Naciones
28042 Madrid