This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
The STAT technical report provides an introduction to the Stat project, which aims to develop an open source machine learning framework in Java called Stat for text analysis. Stat focuses on facilitating common textual data analysis tasks for researchers and engineers. The report outlines the background, motivation, scope, and stakeholders of the project. It also describes an initial survey conducted to understand potential users and their needs in order to prioritize the framework's design and implementation. Finally, the report analyzes two existing toolkits, Weka and MinorThird, and discusses their strengths and limitations for text analysis tasks.
This document presents a system for detecting semantically similar questions in online forums like Quora to reduce duplicate content. It proposes using natural language processing techniques like tagging questions with keywords, vectorizing text with Google News vectors, and calculating similarity with Word Mover's Distance. The system cleans and preprocesses questions before generating tags and calculating similarity between questions to identify duplicates. An evaluation of the system achieved accurate detection of matching and non-matching question pairs.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
The document summarizes a workshop on Service-Oriented Programming (SOP). SOP is a new programming methodology that allows developing software applications by connecting and composing existing services, facilitating software reuse. The workshop is divided into two parts: the first part describes SOP concepts and motivation, and the second introduces teaching materials through a demonstration of SOP techniques. The qualifications of the three presenters are also provided, including their research interests and experience in computer science education.
IRJET - Mobile Chatbot for Information SearchIRJET Journal
This document summarizes a research paper on developing a mobile chatbot using IBM Watson services to allow students to search for their exam scores. The chatbot uses Watson Assistant for natural language processing, a SQL database as a knowledge base to store score information, and text-to-speech and speech-to-text for input and output. It was built with Android Studio and Java to provide an intuitive mobile interface for users to interact with the chatbot.
Industry-Academia Communication In Empirical Software EngineeringPer Runeson
This document discusses industry-academia communication in empirical software engineering. It provides context on a conference in 1968 that aimed to improve communication between industry and academia. It notes key differences in time horizons and languages between the two. Industry focuses on short-term market changes and profits, while academia focuses on long-term learning and publications. The document advocates for both sides to learn each other's languages and cultures to improve collaboration and help tear down walls between the two. It provides examples of successful collaboration projects over time that have helped improve practice.
This document describes a case study research approach for evaluating a requirements defect detection tool in a software engineering company. The following key points are discussed:
1. The study will evaluate the accuracy, usability, and areas for improvement of the tool using both quantitative and qualitative data collection methods.
2. Context details about the subject company and study participants are important to characterize. Quantitative data such as precision/recall scores and usability questionnaires will be collected. Qualitative data such as sources of inaccuracies and improvement feedback will be analyzed.
3. Validity will be addressed through triangulation of multiple data sources and manual classification of defects. The research questions aim to evaluate the tool's accuracy, sources of errors, us
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
The STAT technical report provides an introduction to the Stat project, which aims to develop an open source machine learning framework in Java called Stat for text analysis. Stat focuses on facilitating common textual data analysis tasks for researchers and engineers. The report outlines the background, motivation, scope, and stakeholders of the project. It also describes an initial survey conducted to understand potential users and their needs in order to prioritize the framework's design and implementation. Finally, the report analyzes two existing toolkits, Weka and MinorThird, and discusses their strengths and limitations for text analysis tasks.
This document presents a system for detecting semantically similar questions in online forums like Quora to reduce duplicate content. It proposes using natural language processing techniques like tagging questions with keywords, vectorizing text with Google News vectors, and calculating similarity with Word Mover's Distance. The system cleans and preprocesses questions before generating tags and calculating similarity between questions to identify duplicates. An evaluation of the system achieved accurate detection of matching and non-matching question pairs.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
The document summarizes a workshop on Service-Oriented Programming (SOP). SOP is a new programming methodology that allows developing software applications by connecting and composing existing services, facilitating software reuse. The workshop is divided into two parts: the first part describes SOP concepts and motivation, and the second introduces teaching materials through a demonstration of SOP techniques. The qualifications of the three presenters are also provided, including their research interests and experience in computer science education.
IRJET - Mobile Chatbot for Information SearchIRJET Journal
This document summarizes a research paper on developing a mobile chatbot using IBM Watson services to allow students to search for their exam scores. The chatbot uses Watson Assistant for natural language processing, a SQL database as a knowledge base to store score information, and text-to-speech and speech-to-text for input and output. It was built with Android Studio and Java to provide an intuitive mobile interface for users to interact with the chatbot.
Industry-Academia Communication In Empirical Software EngineeringPer Runeson
This document discusses industry-academia communication in empirical software engineering. It provides context on a conference in 1968 that aimed to improve communication between industry and academia. It notes key differences in time horizons and languages between the two. Industry focuses on short-term market changes and profits, while academia focuses on long-term learning and publications. The document advocates for both sides to learn each other's languages and cultures to improve collaboration and help tear down walls between the two. It provides examples of successful collaboration projects over time that have helped improve practice.
This document describes a case study research approach for evaluating a requirements defect detection tool in a software engineering company. The following key points are discussed:
1. The study will evaluate the accuracy, usability, and areas for improvement of the tool using both quantitative and qualitative data collection methods.
2. Context details about the subject company and study participants are important to characterize. Quantitative data such as precision/recall scores and usability questionnaires will be collected. Qualitative data such as sources of inaccuracies and improvement feedback will be analyzed.
3. Validity will be addressed through triangulation of multiple data sources and manual classification of defects. The research questions aim to evaluate the tool's accuracy, sources of errors, us
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
The document proposes a new user-friendly patent search paradigm to help users more easily find relevant patents. It introduces three techniques: error correction to suggest similar terms for typos in queries, topic-based query suggestion to recommend keywords as the user types, and query expansion to recommend related keywords. It also partitions patents into topics to efficiently process queries in highly relevant partitions and return top results. The proposed methods aim to improve usability and efficiency of patent search.
A Survey on Using Artificial Intelligence Techniques in the Software Developm...IJERA Editor
Software engineering and artificial intelligence are the two important fields of the computer science. Artificial Intelligence is about making machines intelligent, while Software engineering is knowledge –intensive activity, requiring extensive knowledge of the application domain and of the target software itself. This study intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The goal of this research paper is to give some guidelines to use the artificial intelligence techniques that can be applied in solving problems associated with software engineering processes. The aim of this paper is to find out the exact AI technique is likely to be fruitful for particular software development process
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
Presentation: Tool Support for Essential Use Cases to Better Capture Software...Naelah AlAgeel
The paper discusses developing a tool to help requirements engineers extract essential use cases (EUCs) from natural language requirements documents. The tool uses a library of abstract interaction patterns to automatically trace phrases in requirements documents and suggest EUCs. An evaluation found the tool was more accurate and faster than manual extraction. While users found the tool easy to use, they wanted more domain coverage and a better interface. The paper aims to overcome challenges in adopting EUCs by providing automated tool support to extract the correct essential interactions from requirements.
IRJET- Automated Essay Evaluation using Natural Language ProcessingIRJET Journal
This document discusses research on automated essay evaluation using natural language processing. It provides background on previous systems for automated essay scoring like Project Essay Grader (PEG) from the 1960s and more recent systems like e-Rater, IntelliMetric, and Intelligent Essay Assessors. The researchers extracted features from essays like word count, sentence count, spelling, and part-of-speech to train machine learning models. They achieved correlation scores between 0.86-0.87 when comparing predicted scores to human scores, showing the models can perform at similar reliability levels to human graders. The researchers conclude the models could be improved by incorporating features like parse trees and accounting for different essay prompts.
Text can be analysed by splitting the text and extracting the keywords .These may be represented as summaries, tabular representation, graphical forms, and images. In order to provide a solution to large amount of information present in textual format led to a research of extracting the text and transforming the unstructured form to a structured format. The paper presents the importance of Natural Language Processing (NLP) and its two interesting applications in Python Language: 1. Automatic text summarization [Domain: Newspaper Articles] 2. Text to Graph Conversion [Domain: Stock news]. The main challenge in NLP is natural language understanding i.e. deriving meaning from human or natural
language input which is done using regular expressions, artificial intelligence and database concepts.Automatic Summarization tool converts the newspaper articles into summary on the basis of frequency of words in the text. Text to Graph Converter takes in the input as stock article, tokenize them on various index (points and percent) and time and then tokens are mapped to graph. This paper proposes a business solution for users for effective time management.
IRJET - Online Assignment Plagiarism Checking using Data Mining and NLPIRJET Journal
This document presents a proposed system for detecting plagiarism in student assignments submitted online. The system would use data mining algorithms and natural language processing to compare submitted assignments against each other and identify plagiarized content. It would analyze assignments at both the syntactic and semantic levels. The proposed system is intended to more efficiently and accurately detect plagiarism compared to teachers manually reviewing all submissions. The document describes the workflow of the system, including preprocessing of assignments, text analysis, similarity measurement, and algorithms that would be used like Rabin-Karp, KMP and SCAM.
The analytic hierarchy process (AHP) has been applied in many fields and especially to complex
engineering problems and applications. The AHP is capable of structuring decision problems and finding
mathematically determined judgments built on knowledge and experience. This suggests that AHP should
prove useful in agile software development where complex decisions occur routinely. In this paper, the
AHP is used to rank the refactoring techniques based on the internal code quality attributes. XP
encourages applying the refactoring where the code smells bad. However, refactoring may consume more
time and efforts.So, to maximize the benefits of the refactoring in less time and effort, AHP has been
applied to achieve this purpose. It was found that ranking the refactoring techniques helped the XP team to
focus on the technique that improve the code and the XP development process in general.
IRJET- Sentimental Analysis for Students’ Feedback using Machine Learning App...IRJET Journal
This document discusses using machine learning approaches to perform sentiment analysis on students' feedback. Specifically, it proposes using a random forest classifier to analyze descriptive feedback collected through an online student portal and classify it as having positive, negative, or neutral sentiment. The proposed system would collect real-time feedback, preprocess it by removing stop words and tagging parts of speech, extract sentiment-related features, and use the trained random forest model to classify unseen feedback with 90% accuracy. The goal is to more accurately analyze both objective and descriptive feedback to evaluate teacher performance.
This document proposes a model to estimate overall sentiment score by applying rules of inference from discrete mathematics. It discusses sentiment analysis and related work using techniques like supervised/unsupervised learning. The problem is identifying sentiment components and restricting patterns for feature identification. Most approaches focus on nouns/adjectives but not verbs/adverbs. The model preprocesses product review datasets using NLTK for stemming, parsing and tokenizing. It builds a lexicon dictionary of positive and negative words. The Lexical Pattern Sentiment Analysis algorithm uses both lexicon and pattern mining - it selects sentence patterns, checks for positive/negative words in the lexicon, and calculates an overall sentiment score.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
IRJET - Twitter Sentiment Analysis using Machine LearningIRJET Journal
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
IRJET- Vernacular Language Spell Checker & AutocorrectionIRJET Journal
This document describes the development of a spell checker for the Hindi language. It discusses the importance of spell checkers for digitizing languages and some common techniques used in spell checking like n-gram analysis, edit distance algorithms, and probabilistic methods. The proposed system will use a corpus of Hindi text to build a language model and detect spelling errors. It will generate candidate corrections based on edit distance and rank them using n-gram frequency analysis. The goal is to develop a tool that can check for both non-word errors and real word errors in Hindi text.
Robotics-Based Learning in the Context of Computer ProgrammingJacob Storer
This document is a project report for research into whether robotics-based learning or simulation-based learning is more effective for teaching programming. It describes the objectives of developing tutorials for both an Arduino robot and visual basic simulation. Programming tasks for moving forwards/backwards and along shapes were developed. Tutorials and programs were implemented to teach these tasks. Surveys were given to test groups after using each method to collect data on their effectiveness for comparison. While results were mixed, all indicated learning was improved with a teacher. Due to the small sample size, no conclusive answer could be provided.
IRJET- Automated Exam Question Generator using Genetic AlgorithmIRJET Journal
The document describes a proposed system for automatically generating exam questions using genetic algorithms. The system would take in previous exam questions categorized by Bloom's Taxonomy levels and chapters selected by instructors. It would then use genetic algorithms to generate new exam questions that cover different Bloom's levels and avoid repeating questions from the past two years. This aims to ease instructor workload while producing high-quality exam questions at different difficulty levels to evaluate students. The proposed system is described to be implemented using Java, with questions and details stored in a MySQL database.
This document is a report on exploratory data analysis of the Zomato Bengaluru restaurant dataset. It contains the following key points:
1. The dataset contains information on over 51,000 restaurants in Bengaluru scraped from the Zomato website. It has 17 variables providing details like restaurant name, cuisine type, ratings, and services.
2. Exploratory data analysis was conducted using Python libraries like Pandas, NumPy, Matplotlib and Seaborn. Visualizations and summary statistics were used to analyze patterns in the data.
3. Key findings include the most popular restaurant chains, percentages of restaurants that don't offer online orders/reservations, distributions of ratings and costs, popular cuisine types and neighborhoods
This document advertises a business workshop that will take place over several months. The workshop will provide a short introduction to entrepreneurship, including exploring the identification and evaluation of new business concepts, developing business plans, market entry strategies, organizational structure, financing, and critical success factors for entrepreneurs. The workshop will be held once a month from January to June 2009 from 5:30-6:30pm. Advance registration is recommended as the class fills up quickly.
The document provides a recipe for a peanut butter and jelly sandwich using homemade strawberry jam and crunchy peanut butter on white bread. It lists the necessary ingredients and instructions for assembly. Additionally, it shares the results of a survey that found strawberry jam to be the most popular topping for peanut butter among 100 people. Finally, it lists some common occasions when peanut butter and jelly sandwiches are enjoyed.
El documento habla de la soledad de Jesús en su cumpleaños y en Navidad, cuando la gente celebra sin recordarlo a él. Jesús invita a la persona a creer en él y a asistir a la gran fiesta que está preparando en el cielo.
The document proposes a new user-friendly patent search paradigm to help users more easily find relevant patents. It introduces three techniques: error correction to suggest similar terms for typos in queries, topic-based query suggestion to recommend keywords as the user types, and query expansion to recommend related keywords. It also partitions patents into topics to efficiently process queries in highly relevant partitions and return top results. The proposed methods aim to improve usability and efficiency of patent search.
A Survey on Using Artificial Intelligence Techniques in the Software Developm...IJERA Editor
Software engineering and artificial intelligence are the two important fields of the computer science. Artificial Intelligence is about making machines intelligent, while Software engineering is knowledge –intensive activity, requiring extensive knowledge of the application domain and of the target software itself. This study intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The goal of this research paper is to give some guidelines to use the artificial intelligence techniques that can be applied in solving problems associated with software engineering processes. The aim of this paper is to find out the exact AI technique is likely to be fruitful for particular software development process
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
Presentation: Tool Support for Essential Use Cases to Better Capture Software...Naelah AlAgeel
The paper discusses developing a tool to help requirements engineers extract essential use cases (EUCs) from natural language requirements documents. The tool uses a library of abstract interaction patterns to automatically trace phrases in requirements documents and suggest EUCs. An evaluation found the tool was more accurate and faster than manual extraction. While users found the tool easy to use, they wanted more domain coverage and a better interface. The paper aims to overcome challenges in adopting EUCs by providing automated tool support to extract the correct essential interactions from requirements.
IRJET- Automated Essay Evaluation using Natural Language ProcessingIRJET Journal
This document discusses research on automated essay evaluation using natural language processing. It provides background on previous systems for automated essay scoring like Project Essay Grader (PEG) from the 1960s and more recent systems like e-Rater, IntelliMetric, and Intelligent Essay Assessors. The researchers extracted features from essays like word count, sentence count, spelling, and part-of-speech to train machine learning models. They achieved correlation scores between 0.86-0.87 when comparing predicted scores to human scores, showing the models can perform at similar reliability levels to human graders. The researchers conclude the models could be improved by incorporating features like parse trees and accounting for different essay prompts.
Text can be analysed by splitting the text and extracting the keywords .These may be represented as summaries, tabular representation, graphical forms, and images. In order to provide a solution to large amount of information present in textual format led to a research of extracting the text and transforming the unstructured form to a structured format. The paper presents the importance of Natural Language Processing (NLP) and its two interesting applications in Python Language: 1. Automatic text summarization [Domain: Newspaper Articles] 2. Text to Graph Conversion [Domain: Stock news]. The main challenge in NLP is natural language understanding i.e. deriving meaning from human or natural
language input which is done using regular expressions, artificial intelligence and database concepts.Automatic Summarization tool converts the newspaper articles into summary on the basis of frequency of words in the text. Text to Graph Converter takes in the input as stock article, tokenize them on various index (points and percent) and time and then tokens are mapped to graph. This paper proposes a business solution for users for effective time management.
IRJET - Online Assignment Plagiarism Checking using Data Mining and NLPIRJET Journal
This document presents a proposed system for detecting plagiarism in student assignments submitted online. The system would use data mining algorithms and natural language processing to compare submitted assignments against each other and identify plagiarized content. It would analyze assignments at both the syntactic and semantic levels. The proposed system is intended to more efficiently and accurately detect plagiarism compared to teachers manually reviewing all submissions. The document describes the workflow of the system, including preprocessing of assignments, text analysis, similarity measurement, and algorithms that would be used like Rabin-Karp, KMP and SCAM.
The analytic hierarchy process (AHP) has been applied in many fields and especially to complex
engineering problems and applications. The AHP is capable of structuring decision problems and finding
mathematically determined judgments built on knowledge and experience. This suggests that AHP should
prove useful in agile software development where complex decisions occur routinely. In this paper, the
AHP is used to rank the refactoring techniques based on the internal code quality attributes. XP
encourages applying the refactoring where the code smells bad. However, refactoring may consume more
time and efforts.So, to maximize the benefits of the refactoring in less time and effort, AHP has been
applied to achieve this purpose. It was found that ranking the refactoring techniques helped the XP team to
focus on the technique that improve the code and the XP development process in general.
IRJET- Sentimental Analysis for Students’ Feedback using Machine Learning App...IRJET Journal
This document discusses using machine learning approaches to perform sentiment analysis on students' feedback. Specifically, it proposes using a random forest classifier to analyze descriptive feedback collected through an online student portal and classify it as having positive, negative, or neutral sentiment. The proposed system would collect real-time feedback, preprocess it by removing stop words and tagging parts of speech, extract sentiment-related features, and use the trained random forest model to classify unseen feedback with 90% accuracy. The goal is to more accurately analyze both objective and descriptive feedback to evaluate teacher performance.
This document proposes a model to estimate overall sentiment score by applying rules of inference from discrete mathematics. It discusses sentiment analysis and related work using techniques like supervised/unsupervised learning. The problem is identifying sentiment components and restricting patterns for feature identification. Most approaches focus on nouns/adjectives but not verbs/adverbs. The model preprocesses product review datasets using NLTK for stemming, parsing and tokenizing. It builds a lexicon dictionary of positive and negative words. The Lexical Pattern Sentiment Analysis algorithm uses both lexicon and pattern mining - it selects sentence patterns, checks for positive/negative words in the lexicon, and calculates an overall sentiment score.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
IRJET - Twitter Sentiment Analysis using Machine LearningIRJET Journal
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
IRJET- Vernacular Language Spell Checker & AutocorrectionIRJET Journal
This document describes the development of a spell checker for the Hindi language. It discusses the importance of spell checkers for digitizing languages and some common techniques used in spell checking like n-gram analysis, edit distance algorithms, and probabilistic methods. The proposed system will use a corpus of Hindi text to build a language model and detect spelling errors. It will generate candidate corrections based on edit distance and rank them using n-gram frequency analysis. The goal is to develop a tool that can check for both non-word errors and real word errors in Hindi text.
Robotics-Based Learning in the Context of Computer ProgrammingJacob Storer
This document is a project report for research into whether robotics-based learning or simulation-based learning is more effective for teaching programming. It describes the objectives of developing tutorials for both an Arduino robot and visual basic simulation. Programming tasks for moving forwards/backwards and along shapes were developed. Tutorials and programs were implemented to teach these tasks. Surveys were given to test groups after using each method to collect data on their effectiveness for comparison. While results were mixed, all indicated learning was improved with a teacher. Due to the small sample size, no conclusive answer could be provided.
IRJET- Automated Exam Question Generator using Genetic AlgorithmIRJET Journal
The document describes a proposed system for automatically generating exam questions using genetic algorithms. The system would take in previous exam questions categorized by Bloom's Taxonomy levels and chapters selected by instructors. It would then use genetic algorithms to generate new exam questions that cover different Bloom's levels and avoid repeating questions from the past two years. This aims to ease instructor workload while producing high-quality exam questions at different difficulty levels to evaluate students. The proposed system is described to be implemented using Java, with questions and details stored in a MySQL database.
This document is a report on exploratory data analysis of the Zomato Bengaluru restaurant dataset. It contains the following key points:
1. The dataset contains information on over 51,000 restaurants in Bengaluru scraped from the Zomato website. It has 17 variables providing details like restaurant name, cuisine type, ratings, and services.
2. Exploratory data analysis was conducted using Python libraries like Pandas, NumPy, Matplotlib and Seaborn. Visualizations and summary statistics were used to analyze patterns in the data.
3. Key findings include the most popular restaurant chains, percentages of restaurants that don't offer online orders/reservations, distributions of ratings and costs, popular cuisine types and neighborhoods
This document advertises a business workshop that will take place over several months. The workshop will provide a short introduction to entrepreneurship, including exploring the identification and evaluation of new business concepts, developing business plans, market entry strategies, organizational structure, financing, and critical success factors for entrepreneurs. The workshop will be held once a month from January to June 2009 from 5:30-6:30pm. Advance registration is recommended as the class fills up quickly.
The document provides a recipe for a peanut butter and jelly sandwich using homemade strawberry jam and crunchy peanut butter on white bread. It lists the necessary ingredients and instructions for assembly. Additionally, it shares the results of a survey that found strawberry jam to be the most popular topping for peanut butter among 100 people. Finally, it lists some common occasions when peanut butter and jelly sandwiches are enjoyed.
El documento habla de la soledad de Jesús en su cumpleaños y en Navidad, cuando la gente celebra sin recordarlo a él. Jesús invita a la persona a creer en él y a asistir a la gran fiesta que está preparando en el cielo.
Este documento presenta una breve introducción al Perú, incluyendo su nombre oficial (República del Perú), área (1,285,215 km2), capital (Lima), moneda (Nuevo Sol), idiomas oficiales (castellano, quechua, aymara) y fecha de independencia (28 de julio). Además, muestra imágenes y nombra varios lugares y sitios históricos y culturales importantes del país.
El documento describe diferentes tipos y técnicas de comunicación oral, incluyendo conversaciones grupales donde los participantes comparten puntos de vista, debates con moderadores, entrevistas con entrevistadores y entrevistados, exposiciones para explicar temas, mesas redondas con especialistas, y paneles de discusión con expertos. También menciona la importancia de la primera impresión, empezar y terminar bien, escuchar a los demás, y expresarse de manera clara y respetuosa.
This document provides an introduction and overview of the Stat project, which aims to create an open source machine learning framework in Java for text analysis. The Stat framework is designed to be simple, extensible, and performant. It aims to simplify common text analysis tasks for researchers and engineers by providing reusable tools and wrappers for existing NLP and machine learning packages. The document outlines the goals, scope, stakeholders and provides an initial requirements analysis for the Stat framework.
The document discusses some of the promises and perils of mining software repositories like Git and GitHub for research purposes. It notes that while these sources contain rich data on software development, there are also challenges to consider. For example, decentralized version control systems like Git allow private collaboration that may be missed. And most GitHub projects are personal and inactive, while it is also used for storage and hosting. The document recommends researchers approach these data sources carefully and provides lessons on how to properly analyze and interpret the data from repositories like Git and GitHub.
Exploring the Efficiency of the Program using OOAD MetricsIRJET Journal
This document proposes a methodology to analyze the efficiency of object-oriented programs using OOAD (Object Oriented Analysis and Design) metrics. The methodology involves compiling a program successively until it is error-free, recording the error rate at each compilation. These results are then compared to determine how many compilations were needed for the program to be error-free, indicating its efficiency. The methodology is experimentally validated on a sample Java program, with results showing the error rate decreasing with each compilation until the program is error-free after the 8th compilation, demonstrating good efficiency.
Automatic Term Recognition with Apache SolrJIE GAO
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Requirementv4
1. Requirement Analysis Version 0.4
by the Stat Team
Mehrbod Sharifi
Jing Yang
The Stat Project, guided by
Professor Eric Nyberg and Anthony Tomasic
Feb. 25, 2009
2. Chapter 1
Introduction to Stat
In this chapter, we give an brief introduction to the Stat project to audience reading this document.
We explain the background, the motivation, the scope, and the stakeholders of this project so that
audience can understand why we are doing so, what we are going to do, and who may be interested
in our project.
1.1 Overview
Stat is an open source machine learning framework in Java for text analysis with focus on semi-
supervised learning algorithms. Its main goal is to facilitate common textual data analysis tasks
for researcher and engineers, so that they can get their works done straightforwardly and efficiently.
Applying machine learning approaches to extract information and uncover patterns from tex-
tual data has become extremely popular in recent years. Accordingly, many software have been
developed to enable people to utilize machine learning for text analytics and automate such pro-
cess. Users, however, find many of these existing software difficult to use, even if they just want
to carry out a simple experiment; they have to spend much time learning those software and may
finally find out they still need to write their own programs to preprocess data to get their target
software running.
We notice this situation and observe that many of these can be simplified. A new software
framework should be developed to ease the process of doing text analytics; we believe researchers
or engineering using our framework for textual data analysis would feel the process convenient,
conformable, and probably, enjoyable.
1.2 Purpose
Existing software with regard to using machine learning for linguistic analysis have tremendously
helped researchers and engineers make new discoveries based on textual data, which is unarguably
one of the most form of data in the real world.
As a result, many more researchers, engineers, and possibly students are increasingly interested
in using machine learning approaches in their text analytics. However, the bar for entering this
area is not low. Those people, some of which even being experienced users, find existing software
packages are not generally easy to learn and convenient to use.
1
3. For example, although Weka has a comprehensive suite of machine learning algorithms, it is
not designed for text analysis, lacking of naturally supported capabilities for linguistic concepts
representation and processing. MinorThird, on the other hand, though designed specifically as a
package for text analysis, turns out to be rather complicated and difficult to learn. It also does not
support semi-supervised and unsupervised learning, which are becoming increasingly important
machine learning approaches.
Another problem for many existing packages is that they often adopt their own specific input
and output format. Real-world textual data, however, are generally in other formats that are not
readily understood by those packages. Researchers and engineers who want to make use of those
packages often find themselves spending much time seeking or writing ad hoc format conversion
code. These ad hoc code, which could have been reusable, are often written over and over again
by different users.
Researchers and engineers, when presented common text analysis tasks, usually want a text-
specific, lightweight, reusable, understandable, and easy-to-learn package that help them get their
works done efficiently and straightforwardly. Stat is designed to meet their requirements. Moti-
vated by the needs of users who want to simplify their work and experiment related to textual data
learning, we initiate the Stat project, dedicating to provide them suitable toolkits to facilitate
their analytics task on textual data.
In a nutshell, Stat is an open source framework aimed at providing researchers and en-
gineers with a integrated set of simplified, reusable, and convenient toolkits for textual
data analysis. Based on this framework, researchers can carry out their machine learning
experiments on textual data conveniently and comfortably, and engineers can build their
own small applications for text analytics straightforwardly and efficiently.
From the comprehensiveness of features point of view, this framework may not be the most
suitable one compared to other existing packages. However, we should dedicated to make all the
code we write well-designed, efficient, and reliable. need change.
1.3 Scope
This project involves developing a simplified and reusable framework (a collection of foundation
classes) in Java that provides basic and common capabilities for people to easily perform machine
learning analysis on various kind of textual data.
Add what aspects specifically we will going to do here.
2
4. 1.4 Stakeholders
Below is the list of stakeholder and how this project will affect them:
• Researchers, particularly in language technology but also in other fields, would be able
to save time by focusing on their experiments instead of dealing with various input/output
format which is routinely necessary in text processing. They can also easily switch between
various tools available and even contribute to STAT so that others can save time by using
their adaptors and algorithms.
• Software engineers, who are not familiar with the machine learning can start using the
package in their program with a very short learning phase. STAT can help them develop clear
concepts of machine learning quickly. They can build their applications using functionality
provided STAT easily and achieve high level performance.
• Developers of learning package, can provide plug-ins for STAT to allow ease of integration
of their package. They can also delegate some of the interoperability needs through this
program (some of which may be more time consuming to be addressed within their own
package).
• Beginners to text processing and mining, who want fundamental and easy to learn
capabilities involving discovering patterns from text. They will be benefited from this project
by saving their time, facilitating their learning process, and sparking their interests to the
area of language technology.
3
5. Chapter 2
Survey Analysis
This project was faced with many challenges from the beginning. There are many question, some
of subjective nature, that really needs to be addresses by our target audience. For this reason, we
designed a survey to obtain a better understanding and provide a more suitable solution to this
problem. In this chapter, we explain the process of designing the survey, collecting information
and some analysis of the collected data.
2.1 Designing the Survey
The primary goals of doing a survey was the following:
• Understanding the potential users of the package: their programming habit, problem solving
strategies, experience in various area and tools, etc.
• Setting priority for which criteria to focus on for our design and implementation
The survey needed to be short and question to be very specific to get better responses. The
maximum number of question was set at 10 questions. Several draft of the questions was reviewing
within the STAT group and the software engineering class students and instructors several times
until finalize. We also obtained and incorporate some advices from other departments. The final
survey was designed on the SurveyMonkey.com.
2.2 Distribution
The target users of STAT are two main groups with different needs: researchers and industry
programmer. The survey contains questions to distinguish there two group but the final framework
should address the needs from both groups. After conducting a test run with this the STAT group
and the class, we sent the survey out to the Language Technology Institute student mailing list
(representing researchers) and also to student in iLab (Prof, Ramayya Krishnan, Heinz School of
Business) representing industry programmers.
2.3 Analysis of Results
As of 2/25/09, we have received 23 responses and they are individually reviewed by STAT members
and also in aggregate. Below we summarized the finding of the survey result and some charts:
• While many different programming language are used (Python, R, C++) but over 90
4
6. • Users don’t seem to distinguish much between industry and research applications and this is
perhaps more research for the different to be transparent.
• Most users are not familiar with Operation Research but everyone is somewhat familiar with
Machine Learning (if not specifically text classification or data mining).
• Data type expectedly were mostly textual (plain, XML, HTML, etc. as opposed to Excel,
though it was mentioned) and sources were files, databases and web.
• Over 50
• Easy of API use, Performance and Extensibility were the top three choice in design but in
addition to those in textual descriptions user pointed out mostly problems with input and
output formats.
Charts to be added here...
5
7. Chapter 3
Analysis of Related Packages
In this chapter, we analyze a few main competitors of our projects. We focus on two academic
toolkits – Weka and MinorThird. We comment on their strengths and explore their limitations, and
discuss why and how we can do better than these competitors.
3.1 Weka
Weka is a comprehensive collection of machine learning algorithms for solving data mining problems
in Java and open sourced under the GPL.
3.1.1 Strengths of Weka
Weka is a very popular software for machine learning, due to the its main strengths:
• Provide comprehensive machine learning algorithms. Weka supports most current
machine learning approaches for classification, clustering, regression, and association rules.
• Cover most aspects for performing a full data mining process. In addition to learn-
ing, Weka supports common data preprocessing methods, feature selection, and visualization.
• Freely available. Weka is open source released under GNU General Public License.
• Cross-platform. Weka is cross-platform fully implemented in Java.
Because of its supports of comprehensive machine learning algorithm, Weka is often used for
analytics in many form of data, including textual data.
3.1.2 Limitations of using Weka for text analysis
However, Weka is not designed specifically for textual data analysis. The most critical drawback
of using Weka for processing text is that Weka does not provide “built-in” constructs for natural
representation of linguistics concepts1 . Users interested in using Weka for text analysis often find
themselves need to write some ad-hoc programs for text preprocessing and conversion to Weka
representation.
• Not good at understanding various text format. Weka is good at understanding its
standard .arff format, which is however not a convenient way of representation text. Users
have to worry about how can they convert textual data in various original format such as
1
Though there are classes in Weka supporting basic natural language processing, they are viewed as auxiliary
utilities. They make performing basic textual data processing using Weka possible, but not conveniently and straight-
forwardly
6
8. raw plain text, XML, HTML, CSV, Excel, PDF, MS Word, Open Office document, etc. to
be understandable by Weka. As a result, they need to spend time seeking or writing external
tools to complete this task before performing their actual analysis.
• Unnecessary data type conversion. Weka is superior in processing nominal (aka, categor-
ical) and numerical type attributes, but not string type. In Weka, non-numerical attributes
are by default imported as nominal attributes, which usually is not a desirable type for text
(imagine treating different chunks of text as different values of a categorical attribute). One
have to explicitly use filters to do a conversion, which could have been done automatically if
it knows you are importing text.
• Lack of specialized supported for linguistics preprocessing. Linguistics preprocessing
is a very important aspect of textual data analysis but not a concern of Weka. Weka does
not (at least, not dedicated to) take care this issue very seriously for users. Weka has a
StringToWordVector class that performs all-in-one basic linguistics preprocessing, including
tokenization, stemming, stopword removal, tf-idf transformation, etc. However, it is less
flexible and lack of other techniques (such as part-of-speech tagging and n-gram processing)
for users who want fined grain and advanced linguistics controls.
• Unnatural representation of textual data learning concepts. Weka is designed for
general purpose machine learning tasks so have to protect too many variations. As a results,
domain concepts in Weka are abstract and high-level, package hierarchy is deep, and the
number of classes explodes. For example, we have to use Instance rather than Document and
Instances rather than Corpus. Concepts in Weka such as Attribute is obscure in meaning
for text processing. First adding many Attribute to a cryptic FastVector which then passed
to a Instances in order to construct a dataset appears very awkward to users processing
text. Categorize filters first according to attribute/instance then supervised /unsupervised
make non-expert users feel confusing and hard to find their right filters. Many users may feel
unconformable programmatically using Weka to carry out their experiments related to text.
In summary, for users who want enjoyable experience at performing text analysis, they need
built-in capabilities to naturally support representing and processing text. They need specialized
and convenient tools that can help them finish most common text analysis tasks straightforwardly
and efficiently. This cannot be done by Weka due to its general-purpose nature, despite its com-
prehensive tools.
3.1.3 Detail design defects of Weka from the perspective of text analysis
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10. Chapter 4
Requirements specifications
Here we first explain in detail the major features of our framework.
• Simplified. APIs are clear, consistent, and straightforward. Users with reasonable Java
programming knowledge can learn our package without much efforts, understand its logical
flow quickly, be able to get started within a small amount of time, and finish the most common
tasks with a few lines of code. Since our framework is not designed for general purposes and
for including comprehensive features, there are space for us to simplify the APIs to optimize
for those most typical and frequent operations.
• Reusable. Built-in modular supports are provided the core routines across various phases in
text analysis, including text format transformation, linguistic processing, machine learning,
and experimental evaluation. Additional functionalities can be extended on top of the core
framework easily and user-defined specifications are pluggable. Existing code can be used
cross environment and interoperate with external related packages, such as Weka, Minor-
Third, and OpenNLP. (I use reusable instead of extendable because it cover a higher level of
concept we might also need and able to follow, what’s your idea? )
• Any other?
4.1 Functional Requirements
In this section, we define most common use cases of our framework and address them in the degree
of detail of casual use case. The “functional requirements” of this project are that the users can
use libraries provided by our framework to complete these use cases more easily and comfortably
than not use.
Actors
Since our framework assumes that all users of interests are programming using our APIs, there is
only one role of human actor, namely the programmer. This human actor is always the primary
actor. There are some possible secondary and system actors, namely the external packages our
framework integrates, depending on what specific use cases the primary actor is performing.
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11. Fully-dressed Use Cases
Use Case UC1: Document Classification Experiment
Scope: Text analysis application using STAT framework
Level: User goal
Primary Actor: Researcher
Stakeholder and Interests:
• Researcher: Want to test and evaluate a classification algorithm (supervised, semi-
supervised or unsupervised) by applying it on a (probably well-known) corpus; the task
needs to be done efficiently with easy and straightforward coding
Preconditions:
• STAT framework is correctly installed and configured
• The corpus is placed on a source readable by STAT framework
Postconditions:
• A model is trained and test documents in the corpus are classified. Evaluation results
are displayed
Main Success Scenario:
1. Researcher imports the corpus from its source into memory. Specifically, the system
reads data from the source, parses the raw format, extracts information according to
the schema, and constructs an in-memory object to store the corpus
2. Researcher performs preprocessing on the corpus. Specifically, for each document, the
researcher tokenizes the text, removes the stopwords, performs stemming on the tokens,
performs filtering, and/or other potential preprocessing on body text and meta data
3. Researcher converts the corpus into the feature vectors needed for machine learning.
The feature vectors are created by analyzing the documents in the corpus, deriving or
filtering features, adding or removing documents, sampling documents, handling missing
entries, normalizing features, selecting features, and/or other potential processing
4. Researcher splits the processed corpus into training and testing set
5. Researcher chooses a machine learning algorithm, set its parameters, and uses it to train
a model from the training set
6. Researcher classifies the documents in the test set based on the model trained
7. Researcher evaluates the classification based on classification results obtained on the
test set and its true labels. Classification is evaluated mainly on classification accuracy
and classification time or if it is unsupervised, on other unsupervised metrics such as
Adjusted Rand Index.
8. Researcher displays the final evaluation result
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12. Use Case UC1: Document Classification Experiment (cont.)
Extensions:
1a. The framework is unable to find the specified source.
1. Throw source not found exception
1b. Researcher loads a previously saved corpus in native format from a file on the disk directly
to memory object, thus researcher does not handle source, format, or schema explicitly.
1a. File not found:
1.Throw file not found exception
1b. Malformed native format:
1.Throw malformed native format exception
4a. Researcher specify a parameter k larger than the number of document or smaller than 1
1. Throw invalid argument exception
1-3, 5a. Researcher saves the in-memory objects of different level of processed corpus rep-
resentation to disk in native format which can be loaded back lately, after finishing each
step.
1-3, 5b. Research exports the in-memory objects of different processed corpus representation
to disk in external formats (e.g., weka arff, csv) which can be processed by external software.
6a. Researcher saves the in-memory model object to disk, which can be loaded back lately.
6b. Researcher loads a previously saved model in native format from a file on the disk directly
to memory object.
1a. File not found:
1. Throw file not found exception
1b. Malformed native format:
1.Throw malformed native format exception
4-8b. To perform k-fold cross validation, the corpus is split to k parts in step 4, and steps
5-8 are repeated k-times by switching each split a testing split and the rest as training.
Researcher combines the evaluations on different test sets obtained in the previous steps and
forms a final classification evaluations
6c. Unsupported learning parameters (the learning algorithm cannot handle the combination
of parameters the researcher specifies)
1. Throw unsupported learning parameters exception
6d. Unsupported learning capability (the learning algorithm cannot handle the format and
data in training set, potentially caused by unsupported feature type, class type, missing
values, etc).
1. Identify exception cause(s)
2. Throw corresponding exception(s)
8a. Incompatible between test set and classification (potentially caused by difference in
schema between training set and test set)
1. Throw incompatible evaluation exception
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13. Use Case UC1: Document Classification Experiment (cont.)
10a. The researcher customizes the display instead of using the default display format.
1.The researcher obtains specific fields of the evaluations via interfaces provided
2.The researcher constructs a customized format using the fields he/she extracts
3.The researcher display it customized format and/or write to a destination
Special Requirements:
• Pluggable preprocessors in step 2-3
• Pluggable learning algorithm in step 6
• Learning algorithm should be scalable to deal with large corpus
• Researcher should be able to visualize results after various steps to trace the state of
different objects (e.g., preprocessed corpus, models, classifications, evaluations)
• Researcher should be able to customize the visualization output
Open Issues:
• How to address the variations issues in reading different sources
• How to (in what form) let research specify parameters for different learning algorithms
• What specifically need to be able to export, persist, and visualize?
• How to implement the corpus splitting in an efficient way (dont create extra objects)
• How to deal with performance issues of storing large corpora in the memory
• How to deal with internal representation of the dataset in efficient data structure
4.2 Non-functional Requirements
• Open source. It should be made available for public collaboration, allowing users to use,
change, improve, and redistribute the software.
• Portability. It should be consistently installed, configured, and run independent to different
platforms, given its design and implementation on Java runtime environment.
• Documentation. Its code should be readable, self-explained, and documented clearly and
unambiguously for critical or tricky part. It should include an introduction guide for users
to get started, and preferably, provides sample dataset, tutorial, and demos for user to run
examples out of the box.
• Performance. It should be able to response to user within reasonable amount of time given
a limited amount of data (unclear, need specify). Preferably, it can estimate the running
time needed to perform a task and notify user before user actually execute the task (is this
the responsibility for framework designers? )
• Dependency. It is actually a issue. The package integrates other external packages and has
many dependency. How to resolve this issue? How do we distribute our package?
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