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Random Forest Classifier for News Articles Sentiment Analysis
 
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Introduction DATA MINING It is the process to discover the knowledge or hidden pattern form large databases. The overall goal of data mining is to extract and obtain information from databases and transfer it into an understandable format for use in future. It is used by Business intelligence organizations, Financial analysts, Marketing organizations, and companies with a strong consumer focus like retail ,financial and communication . It can also be seen as one of the core process of knowledge discovery in data base (KDD). It can be viewed as process of Knowledge Discovery in database. Data Extraction/gathering:- To collect the data from sources . Eg: data warehousing. Data cleansing :- To eliminate bogus data and errors. Feature extraction:- To extract only task relevant data : i.e to obtain the interesting attributes of data . Pattern extraction and discovery :- This step is seen as process of data mining , where one should concentrate the effort. Visualization of the data and Evaluation of results :- To create knowledge base. CLASSIFICATION Classification is a technique of data mining to classify each item into predefined set of groups or classes. The goal of classification is to accurately predict the target class for each item in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating. SENTIMENT ANALYSIS Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be his or her judgment or evaluation, affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). With opinion mining, we can distinguish poor content from high quality content. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Opinion Essay or Persuasive Essay
 
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Watch Shaun's Smrt Live Class live for free on YouTube every Thursday at 17 00 GMT (17 00 GMT = https://goo.gl/cVKe0m). Become a Premium Subscriber: http://www.smrt.me/smrt/live Premium Subscribers receive: - Two 1-hour lessons per week with a Canadian or American teacher - Video-marked homework & assignments - Quizzes & exams - Official Smrt English Certification - Weekly group video chats This video is on how to write a successful persuasive, opinion-based academic essay in English. Students will learn how to structure and organize an opinion essay and will be given tips to make their essays successful. Join the Facebook group: http://www.facebook.com/groups/leofgroup If you would like to support the stream, you can donate here: https://goo.gl/eUCz92 Exercise: http://smrtvideolessons.com/2013/07/26/opinion-essay-or-persuasive-essay/ Learn English with Shaun at the Canadian College of English Language! http://www.canada-english.com
Views: 366188 Smrt English
MLSA - Multi Language Sentiment Analysis
 
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JHU Information Retrieval class project. Performing sentiment analysis on ranked documents retrieved per user query on multiple languages.
Views: 46 Jorge M Ramirez
Movies Review Sentiment Analysis
 
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DATA MINING It is the process to discover the knowledge or hidden pattern form large databases. The overall goal of data mining is to extract and obtain information from databases and transfer it into an understandable format for use in future. It is used by Business intelligence organizations, Financial analysts, Marketing organizations, and companies with a strong consumer focus like retail ,financial and communication . DATA MINING (cont.): It can also be seen as one of the core process of knowledge discovery in data base (KDD). It can be viewed as process of Knowledge Discovery in database. Data Extraction/gathering:- To collect the data from sources . Eg: data warehousing. Data cleansing :- To eliminate bogus data and errors. Feature extraction:- To extract only task relevant data : i.e to obtain the interesting attributes of data . Pattern extraction and discovery :- This step is seen as process of data mining , where one should concentrate the effort. Visualization of the data and Evaluation of results :- To create knowledge base. CLASSIFICATION Classification is a technique of data mining to classify each item into predefined set of groups or classes. The goal of classification is to accurately predict the target class for each item in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating. SENTIMENT ANALYSIS Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be his or her judgment or evaluation, affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader). With opinion mining, we can distinguish poor content from high quality content. For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 780324 Kent Löfgren
Named Entity Recognition - Natural Language Processing With Python and NLTK p.7
 
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Named entity recognition is useful to quickly find out what the subjects of discussion are. NLTK comes packed full of options for us. We can find just about any named entity, or we can look for specific ones. NLTK can either recognize a general named entity, or it can even recognize locations, names, monetary amounts, dates, and more. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 82046 sentdex
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 142491 James Woodall
The Hazards of AI: Beware! | Hamidreza Keshavarz Mohammadian | TEDxTehran
 
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AI is improving every day and we found a widespread application of it in our daily lives.How deep is this influence? We did get into top gear but do we have a destination or we are going nowhere? Is this beautiful forest road going to the valley? Hamidreza Keshavarz was born in Tehran in 1983. He attended the Allameh Helli school (NODET) where he later became a teacher and head of the department. He holds a Ph.D. degree in Computer Engineering from Tarbiat Modares University. His main interest areas are data science and artificial intelligence and his thesis, entitled “Sentiment analysis based on the extraction of lexicon features” is about opinion mining on social media. He has published 12 papers and is a reviewer for international journals and conferences. He was awarded for presenting his thesis in the countrywide “Presenting your thesis in three minutes” competition. He has been in love with computers since early childhood when computers were not as widespread as today. His love with computers was intensified when he started programming at age 11. He wrote a Paintbrush program in the Assembly language at age 12, which cemented his desire to become active in this field. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Views: 523 TEDx Talks
How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 551405 Thales Sehn Körting
Document Classification and Unstructured Data Extraction SaaS Solution Offering for BPO and SI’s
 
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Experts estimate that up to 80% of the data in an organization is unstructured, information that does not have a well-defined or organized data model. The amount of this information in enterprises is staggering and growing substantially, often many times faster than structured information. Unstructured content is characteristically text-heavy, but may also contain critical data elements such as: amounts, percentage's, dates, numbers, and facts as well, like that found in contracts, loan amounts and terms, correspondences, proposals, legal descriptions, vesting information, EOB’s, transcriptions, and much more. Unfortunately, it is often very difficult to analyze, classify and extract this unstructured data as it is typically highly variable in nature. Due this complexity, BPO’s and SI’s have traditionally relied on data entry shops to manually enter this data to process and/or store this information into a more structured database-friendly format. With offshoring offering lower price solutions (as compared to onshore), many companies have settled as this to be the only practical solution. However, is this really the most affordable, scalable, secure and flexible option? Axis Technical Group has developed the next generation alternative with a hosted advanced data extraction and classification solution as a Service (SaaS). Axis AI uses its proprietary Natural Language Processing (NLP) and Machine Learning algorithms and processes that can now classify and capture data from all your content, complex, unstructured documents, as well as, the traditional structured and semi-structured documents. During the presentation, we’ll cover the following. •Challenges of capturing information from complex unstructured document formats •Overview of how NLP works and how these advanced technologies take capture to a new level •Various document candidates for advanced unstructured data extraction and classification •How you can save money as an SI/BPO developing solutions for your clients and offering Classification/Extraction as a Service •How Axis AI eliminates the upfront time and cost associated with a installing and configuring, training your technical teams and supporting a new solution •Product process overview and demonstration •Axis client case studies and success stories
Comparison / Contrast Essays
 
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Watch Shaun's Smrt Live Class live for free on YouTube every Thursday at 17 00 GMT (17 00 GMT = https://goo.gl/cVKe0m). Become a Premium Subscriber: http://www.smrt.me/smrt/live Premium Subscribers receive: - Two 1-hour lessons per week with a Canadian or American teacher - Video-marked homework & assignments - Quizzes & exams - Official Smrt English Certification - Weekly group video chats In this video, we will discuss the structure and organization of a comparison/contrast essay. Students will learn the different styles of comparing and contrasting, and after the video, will be able to organize and write a more effective essay. Join the Facebook group: http://www.facebook.com/groups/leofgroup If you would like to support the stream, you can donate here: https://goo.gl/eUCz92 Exercise: http://smrtvideolessons.com/2013/07/26/comparison-contrast-essays/ Learn English with Shaun at the Canadian College of English Language! http://www.canada-english.com
Views: 420969 Smrt English
Finding Main Ideas and Supporting Details Example
 
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A simple explanation and example of finding the main idea and supporting details in a paragraph.
Views: 143612 ProgressiveBridges
QDA Miner - Creating a Project from a List of Documents
 
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The easiest method to create a new project and start doing analysis in QDA Miner is by specifying a list of existing documents or images and importing them into a new project. Using this method creates a simple project with two or three variables: A categorical variable containing the original name of the files from which the data originated, a DOCUMENT variable containing imported documents and/or an IMAGE variable containing imported graphics. All text and graphic files are stored in different cases so, if 10 files have been imported, the project will have 10 cases with two or three variables each. To split long documents into several ones or extract numerical, categorical, or textual information from those documents and store them into additional variables, use the Document Conversion Wizard.
Natural Language Processing (NLP)- Part 1
 
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Natural language processing is a very important part of machine learning. Many of you are doing your final year thesis on NLP. But in traditional books and tutorials these thing are theoretically explained, whereas application based lessons are much needed to complete projects. I hope you like these videos. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials
Views: 560 Planeter
How to convert Pdf to Text format using python | +91-7307399944 For query
 
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Python script to convert pdf file to text file, so to access the information and use/manipulate it to analyze the data within the pdf file. Also, Visit our website to know more about our services at https://www.researchinfinitesolutions... https://www.ris-ai.com/ Direct at +91-7307399944 Whats App at +91-7307399944 If you liked the video then you can also promote us. your small contribution will encourage us to keep contributing to Educational AI. Please make a small donation via Paypal at [email protected]
Views: 2599 Fly High with AI
Data Mining using the Excel Data Mining Addin
 
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The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 75429 Steve Fox
Comparative Language Analysis – Multiple Text Language Analysis: Structure
 
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If my head is in the way of the slides, listen closer, or refer to the powerpoint here: https://www.slideshare.net/secret/Igsjh7iEZWMYcb Comparative Language Analysis - Style and approach for responding to multiple text forms of language analysis. Slides and Worksheets available here: http://www.slideshare.net/skolber Email me at :[email protected] Backdrop images from: https://pixabay.com/en/users/hadania-19110/
C-word: Cancer dataset Data Mining
 
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All the Machine Learning and other data mining aspects of govHack. Live version: http://thomas-mitchell.net/govhack/home.php Source code: https://github.com/sachinruk/govhack
Views: 96 The Math Student
Improve your teaching skills using IntenCheck Text Analysis Software
 
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http://www.intentex.com We make effective communication easy for everyone. Intentex is a startup that offers a free next-generation text analysis software which will help you improve your communication and get better results.
Views: 91 Intentex
Find themes and analyze text in NVivo 9 | NVivo Tutorial Video
 
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Learn how to use NVivo's text analysis features to help you identify themes and explore the use of language in your project. For more information about NVivo visit: http://bit.ly/sQbS3m
Views: 111483 NVivo by QSR
What Is A Textual Analysis?
 
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"What Is A Textual Analysis? Watch more videos for more knowledge What is..? textual analysis - YouTube https://www.youtube.com/watch/ltIBMagVAEw What Is A Textual Analysis? - YouTube https://www.youtube.com/watch/vKgd9CdzEwg Tips for Writing a Textual Analysis Paper - YouTube https://www.youtube.com/watch/tZFHW79RsXM How to Analyze a Text - YouTube https://www.youtube.com/watch/R7ot_rYN3UI Textual Analysis Video - YouTube https://www.youtube.com/watch/YQujsmnGJwA Text Analysis 1 Introduction - YouTube https://www.youtube.com/watch/utJv5DMZS-o Text Analysis - Intro to Computer Science - YouTube https://www.youtube.com/watch/679-n8LWaVo Textual Analysis-Content Analysis - YouTube https://www.youtube.com/watch/W0r076IZAZ0 Graphing for Textual Analysis - YouTube https://www.youtube.com/watch/Zgkmbk-Qf_4 How to Analyze Literature - YouTube https://www.youtube.com/watch/pr4BjZkQ5Nc √ How to Analyse Texts Critically - Critical Thinking ... https://www.youtube.com/watch/gXMd0oS47sU HSC English Advanced - Discovery Text Analysis ... https://www.youtube.com/watch/3BRGfpaG_qo Text Analysis Masterclass - YouTube https://www.youtube.com/watch/fCUhQ7BBemA What is Text Analytics? - YouTube https://www.youtube.com/watch/GHtEvMdqV2E Textual Analysis of Music Videos - YouTube https://www.youtube.com/watch/SVeJOuBI72I (Basic) Text Analysis with WORDij - YouTube https://www.youtube.com/watch/7lpvQW360js Quantitative Text Analysis & Text Exploration with ... https://www.youtube.com/watch/XUh75Gpc4kk Celine Marie Pascale-Qualitative Textual Analysis ... https://www.youtube.com/watch/76B6AOdvj_4 Enrich Your Research with Open Text Analysis - YouTube https://www.youtube.com/watch/xoFVCj6tYd4 Timothy Loughran: Textual Analysis in Finance - YouTube https://www.youtube.com/watch/4wc6_sy_iVo"
Views: 1242 Ask Question II
Towards a Generic Framework for Table Extraction and Processing - Roya Rastan UNSW
 
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Large volumes of textual data are produced by companies through various media outlets. But the format and quality of data produced varies greatly between the data source outlets, making it difficult for effective and efficient access to the data for meaningful analysis (e.g., how do we answer a question like 'What articles today are about the profitability of Rio Tinto?', 'Is this new good or bad for Rio Tinto?'). However, the wealth of information present in the data can be explored via various text based analysis methods such as keyword search, concept analysis and entity recognition / resolution, sentimental analysis and so on. Therefore, there should be a solution for this problem. A part of this thesis particularly aims to solve table extraction problem associated with PDF file format of Australian company announcements for Sirca. Some of these files often contain market sensitive information presented as tables and the financial users will benefit from quickly gaining access to the data and use it in various search and analysis tasks. On the other hand, PDF files are usually unstructured and this makes texts and data structures (such as tables, graphs, diagrams) recognition and extraction so difficult. Since, successful extraction is the fundamental prerequisite for accurate financial interpretation, this project will provide a solution to identify useful data structures from input files and then automatically import the extracted structures into Table Base (tables repository), to add annotations and manage annotations to enhance the semantic quality of the data collected at different levels of data and structures, and to enable sophisticated reasoning/analysis tasks over the extracted structures. As a result of this process, it also will be shown that having an integrated (semantic) data platform will better enable other textbased analysis methods. So far, table detection phase are implemented completely and table extraction is being implemented. We look at the table extraction problem from the process point of view and propose a table extraction workflow, which can be considered as a plug and‐play architecture for table extraction. The next phase of the project is to find an automatic way for tables' header detection with less user interaction, which enables us to interpret tables and go another step further to more automatic table understanding.
AntConc 101: A quick introduction to text corpus analysis
 
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A quick introduction to AntConc that covers loading a collection of text files (we use Project Gutenberg to download Sherlock Holmes novels), exploring with collocation generation, and exporting outputs to an Excel-friendly tab-spaced txt format. Download AntConc: http://www.laurenceanthony.net/software/antconc/ Project Gutenberg: http://gutenberg.org/ Background music: "Uplifting" by Podington Bear (used via Creative Commons attribution no-commercial license) Full transcript: AntConc is an open-source corpus analysis toolkit. It’s main function is to identify patterns in large collections of texts, such as novels, blog posts, e-mails, or essays. These patterns might provide you with valuable clues for your research. Today, we’re going to show you how to get started with AntConc and quickly demo some of its powerful features. You can download the version for your operating system directly from the author’s website - we’ll include a link in the description too. Next, let’s load in some texts. Project Gutenberg hosts one of the largest collection of public domain e-books in the world. Let’s look at the Sherlock Holmes detective books written by Sir Arthur Conan Doyle. Note that you must use files in a plain text format like .txt with AntConc - you can use a program like Microsoft Word to convert a document to .txt format if you need to. Now that we’ve loaded our texts into AntConc, we’re ready to analyze. The Collocate feature works like a search engine that scans our entire corpus. Let’s look for all instances of MURDER. The Concordance Plot visualizes the exact moment where MURDER appears in our novels -- or at least where the word murder appears. AntConc contains a number of features to discover trends in the words that occur near or next to our search terms. For today, let’s try Collocate searches with a few different words. AntConc uses a combination of searching and statistical analysis to show us words that appear near our search term, and that were unlikely to appear there by chance alone. You may wish to play with some of the parameters in the bottom right of the screen. I’m increasing the minimum frequency to 5, which helps make sure AntConc is capturing repeated trends in our data and not just a single weird phrase that appeared once. AntConc can export your results through the Save Output to Text File feature. These are plain text files, but they use tab-separated formatting -- which means that you can load them into a spreadsheet with Excel! This gives you a lot of options to work further with the data. I hope you enjoyed this introduction, and that you keep exploring -- we’ll include some links in the description to other webinars and tutorials. Enjoy AntConc and thanks for watching!
Science Beam – using computer vision to extract PDF data
 
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There’s a vast trove of science out there locked inside the PDF format. From preprints to peer-reviewed literature and historical research, millions of scientific manuscripts can only be found in a print-era format that is effectively inaccessible. A move away from PDF and toward a more open and flexible format like XML would unlock a multitude of use cases for the discovery and reuse of existing research. We are embarking on a project to convert PDF to XML and improve the accuracy of the XML output by building on existing open-source tools. One aim of the project is to combine some of these tools in a modular conversion pipeline that achieves a better overall conversion result compared to using the tools on their own. In addition, we are experimenting with a novel approach to the problem: using computer vision to identify key components of the scientific manuscript in PDF format. We are calling on the community to help us move this project forward. We hope that as a community-driven effort we’ll make more rapid progress towards the vision of transforming PDFs into structured data with high accuracy. You can explore the project on GitHub: https://github.com/elifesciences/sciencebeam. Your ideas, feedback, and contributions are welcome by email to [email protected] Read More about Science Beam Project https://researchstash.com/2017/08/05/science-beam-using-computer-vision-to-extract-pdf-data/
Views: 231 Research Stash
Normality test using SPSS: How to check whether data are normally distributed
 
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If data need to be approximately normally distributed, this tutorial shows how to use SPSS to verify this. On a side note: my new project: http://howtowritecitations.com. Statistical analyses often have dependent variables and independent variables and many parametric statistical methods require that the dependent variable is approximately normally distributed for each category of the independent variable. Let us assume that we have a dependent variable, exam scores, and an independent variable, gender. In short, we must investigate the following numerical and visual outputs (and the tutorial shows how to do just that): -The Skewness & kurtosis z-values, which should be somewhere in the span -1.96 to +1.96; -The Shapiro-Wilk p-value, which should be above 0.05; -The Histograms, Normal Q-Q plots and Box plots, which should visually indicate that our data are approximately normally distributed. Remember that your data do not have to be perfectly normally distributed. The main thing is that they are approximately normally distributed, and that you check each category of the independent variable. (In our example, both male and female data.) Step 1. In the menu of SPSS, click on Analyze, select Descriptive Statistics and Explore. Step 2. Set exam scores as the dependent variable, and gender as the independent variable. Step 3. Click on Plots, select "Histogram" (you do not need "Stem-and-leaf") and select "Normality plots with tests" and click on Continue, then OK. Step 4. Start with skewness and kurtosis. The skewness and kurtosis measures should be as close to zero as possible, in SPSS. In reality, however, data are often skewed and kurtotic. A small departure from zero is therefore no problem, as long as the measures are not too large compare to their standard errors. As a consequence, you must divide the measure by its standard error, and you need to do this by hand, using a calculator. This will give you the z-value, which, as I said, should be somewhere within -1.96 to +1.96. Let us start with the males in our example. To calculate the skewness z-value, divide the skewness measure by its standard error. All z-values in the tutorial video are within ±1.96. We can conclude that the exam score data are a little skewed and kurtotic, for both males and females, but they do not differ significantly from normality. Step 5. Check the Shapiro-Wilk test statistic. The null hypothesis for this test of normality is that the data are normally distributed. The null hypothesis is rejected if the p-value is below 0.05. In SPSS output, the p-value is labeled "Sig". In our example, the p-values for males and females are above 0.05, so we keep the null hypothesis. The Shapiro-Wilk test thus indicates that our example data are approximately normally distributed. Step 6. Next, let us look at the graphical figures, for both male and female data. Inspect the histograms visually. They should have the approximate shape of a normal curve. Then, look at the normal Q-Q plot. The dots should be approximately distributed along the line. This indicates that the data are approximately normally distributed. Skip the Detrended Q-Q plots. You do not need them. Finally, look at the box plots. They should be approximately symmetrical. The video contains references to books and articles. About writing out the results: I would put it under the sub-heading "Sample characteristics", and the video contains examples of how I would write. In this tutorial, I show you how to check if a dependent variable is approximately normally distributed for each category of an independent variable. I am assuming that you, eventually, want to use a certain parametric statistical methods to explore and investigate your data. If it turns out that your dependent variable is not approximately normally distributed for each category of the independent variable, it is no problem. In such case, you will have to use non-parametric methods, because they make no assumptions about the distributions. Good luck with your research. Text and video (including audio) © Kent Löfgren, Sweden Here are the references that I discuss in the video (thanks Abdul Syafiq Bahrin for typing them our for me): Cramer, D. (1998). Fundamental statistics for social research. London: Routledge. Cramer, D., & Howitt, D. (2004). The SAGE dictionary of statistics. London: SAGE. Doane, D. P., & Seward, L.E. (2011). Measuring Skewness. Journal of Statistics Education, 19(2), 1-18. Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Liliefors and Anderson-Darling test. Journal of Statistical Modeling and Analytics, 2(1), 21-33. Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591-611.
Views: 458228 Kent Löfgren
Thesis writing in Chandigarh| MATLAB Lecture 6
 
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M Tech Ph D Thesis work on MATLAB in chandigarh www.thesisworkchd.com THESIS WORK ON MATLAB| THESIS IN CHANDIGARH| M TECH THESIS| PHD THESIS IN CHANDIGARH Thhis is my sixth lecture on MATLAB introduction. Please like, subscribe and share my video. THESIS WORK ON MATLAB We provide thesis assistance and guidance in Chandigarh with full thesis help and readymade M.Tech thesis writing in MATLAB with full documentation in Chandigarh , Delhi, Haryana, Punjab, Jalandhar, Mohali, Panchkula, Ludiana, Amritsar and nearby area’s M.Tech. students by providing a platform for knowledge sharing between our expert team. Some of the important areas in which we provide thesis assistance presently have been listed below: BIOMEDICAL BASED PROJECTS: 1. AUTOMATIC DETECTION OF GLAUCOMA IN FUNDUS IMAGES. 2. DETECTION OF BRAIN TUMOR USING MATLAB 3. LUNG CANCER DIAGNOSIS MODEL USING BNI. 4. ELECTROCARDIOGRAM (ECG) SIMULATIONN USNG MATLAB FACE RECOGNITION: 5. FACE DETECTION USING GABOR FEATURE EXTRACTION & NEURAL NETWORK 6. FACE RECOGNITION HISTOGRAM PROCESSED GUI 7. FACE RECOGNITION USING KEKRE TRANSFORM FINGERPRINT RECOGNITION: 8. MINUTIAE BASED FINGERPRINT RECOGNITION. 9. FINGERPRINT RECOGNITION USING NEURAL NETWORK RECOGNITION/ EXTRACTION/ SEGMENTATION/WATERMARKING: 10. ENGLISH CHARACTER RECOGNITION USING NEURAL NETWORK 11. NUMBER RECOGNITION USING IMAGE PROCESSING 12. CHECK NUMBER READER USING IMAGE PROCESSING 13. DETECTION OF COLOUR OF VEHICLES. 14. SEGMENTATION & EXTRACTION OF IMAGES, TEXTS, NUMBERS, OBJECTS. 15. SHAPE RECOGNITION USING MATLAB IN THE CONTEXT OF IMAGE PROCESSING 16. RETINAL BLOOD VESSEL EXTRACTION USING MATLAB 17. RECONGITION AND LOCATING A TARGET FROM A GIVEN IMAGE. 18. PHASE BASED TEMPLATE MATCHING 19. A DETECTION OF COLOUR FROM AN INPUT IMAGE 20. CAESAR CIPHER ENCRYPTION-DECRIPTION 21. IMAGE SEGMENTATION - MULTISCALE ENERGY-BASED LEVEL SETS 22. THE IMAGE MEASUREMENT TOOL USING MATLAB 23. A DIGITAL VIDEO WATERMARKING TECHNIQUE BASED ON IDENTICAL FRAME EXTRACTION IN 3-LEVEL DWT (ALSO FOR 5-LEVEL DWT) 25. RELATED TO STEGANOGRAPHY AND CRYPTOGRAPHY 26. RELATED TO THE ALL TYPES OF WATERMARKING TECHNIQUES A. TEXT WATERMARKING B. IMAGE WATERMARKING C. VIDEO WATERMARKING D. COMBINATION OF TEXT AND IMAGE WITH KEY 27. OFFLINE SIGNATURE RECOGNITION USING NEURAL NETWORKS APPROACH 28. FRUIT RECOGNITAION RELATED PROJECTS 29. VESSEL SEGMENTATION AND TRACKING 30. PROPOSED SYSTEM FOR DATA HIDING USING CRYPTOGRAPHY AND STEGANOGRAPHY 31. BASED ON IMAGE COMPRESSION ALGORITHM USING DIFFERENT TECHNIQUES 32. GRAYSCALE IMAGE DIGITAL WATERMARKING TECHNOLOGY BASED ON WAVELET ANALYSIS 33. CONTENT-BASED IMAGE RETRIEVAL 34. IMAGE PROCESSING BASED INTELLIGENT TRAFFIC CONTROLLER 35. MORPHOLOGY APPROACH IN IMAGE PROCESSING And many more
Views: 56 Pushpraj Kaushik
My Text Analysis Presentation!
 
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This is for my English 11 class. Feel free to watch!
Views: 103 Hannah Borst
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 321966 Data School
Preparing Data in Excel to Import into SPSS
 
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This video demonstrates how to prepare data in Excel before importing into SPSS. Proper data entry coding and common errors are reviewed.
Views: 115105 Dr. Todd Grande
Masters Thesis Defense Part 5 of 6
 
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My Masters Thesis Defense in Computational Science at San Diego State University (SDSU) Part 5 of 6. Thesis title: "Microarray Analysis of the Effects of Rosiglitazone on Gene Expression in Neonatal Rat Ventricular Myocytes", Fall 2009.
Views: 802 ntselliot
Data Mining | Web Scrapping | Data Extraction
 
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The term Data Mining refers to the extraction of vital information by processing a huge amount of data. Data Mining plays a prominent role in predictive analysis and decision making. Companies basically uses these techniques to know the exact customer focus and finalize the marketing goals. DM is also useful in market research, industry research and competitor's analysis. Major activities involved in DM is: • Extract Data from web databases. • Load them into data store systems • Classify stored data in multidimensional database system • Analysis using some automated technical software application. • Presentation of Extracted information useful format like PPT, XLS file For more details: http://bit.ly/1iAor17
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 653253 Quantitative Specialists
Data mining projects using weka
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-projects-uk/
Views: 3597 PhDprojects. org
Topic Analysis
 
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Learn how to use topic analysis to better understand your question and select keywords for searching. http://student.csu.edu.au/__data/assets/pdf_file/0007/463381/Common-Instruction-Words.pdf Visit CSU Library: http://www.csu.edu.au/division/library Contact us: http://www.csu.edu.au/division/library/contacts-help
Mining Your Logs - Gaining Insight Through Visualization
 
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Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25673 GoogleTechTalks
Using Twitter for Academic Research
 
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Learn how Twitter can be a useful tool for conducting academic research.
Views: 2221 WolfgramLibrary
RESEARCH PAPER FORMAT (in Hindi)
 
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Please Support LearnEveryone Channel,Small Contribution shall help us to put more content for free: Patreon - https://www.patreon.com/LearnEveryone ------------------------------------------------- find relevant notes-https://viden.io/search/knowledge?query=computer+science also search PDFs notes-https://viden.io More videos like this: https://www.youtube.com/playlist?list=PL9P1J9q3_9fNmTX2ZkUnboMBp8yU_GHYj
Views: 104713 LearnEveryone
Training/test data for developing read/write variational autoencoders
 
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A simple open source data set to help people collaborate on developing recurrent variational autoencoders, with the ability to read/write to external memory. A XML parsing, preprocessing and data checking script was written in R. The timeseries data for each line the writter wrote onto the whiteboard, can then be saved in csv format. These files can then be read into Python/Theano or Lua/Torch to construct training/validation/test sets as the user requires. The R, Python and Lua scripts will be posted on Github. For background on generative models & program learning see, Towards more human-like concept learning in machines: Compositionality, causality, and learning-to-learn (http://cims.nyu.edu/~brenden/LakePhDThesis.pdf) - Brenden Lake - PhD thesis, Massachusetts Institute of Technology, 2014. Concept learning as motor program induction: A large-scale empirical study (http://www.cs.toronto.edu/~rsalakhu/papers/LakeEtAl2012CogSci.pdf) - Brenden M. Lake, Ruslan Salakhutdinov and Joshua B. Tenenbaum DRAW: A Recurrent Neural Network For Image Generation (http://arxiv.org/abs/1502.04623) - Karol Gregor, Ivo Danihelka, Alex Graves, Daan Wierstra - this is the only paper which uses both a recurrent variational autoencoder/decoder with a external read/writable memory. Generating Sequences With Recurrent Neural Networks (http://arxiv.org/abs/1308.0850) - Alex Graves Neural Turing Machines (http://arxiv.org/abs/1410.5401) - Alex Graves, Greg Wayne, Ivo Danihelka Neural Variational Inference and Learning in Belief Network (http://arxiv.org/abs/1402.0030) - Andriy Mnih, Karol Gregor For a desciption and access to the online handwritting dataset go here (http://www.iam.unibe.ch/fki/databases/iam-on-line-handwriting-database) For a description and to download the Hutter prize dataset used in section 3 of [Gra13] go here (http://mattmahoney.net/dc/textdata.html) and for the latest compression benchmarks here (http://mattmahoney.net/dc/text.html).
Views: 944 Ajay Talati
Text Mining with the HathiTrust & Empowering Librarians to Support Digital Scholarships
 
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Arm librarians with instructional content and tools in digital scholarships and digital humanities. Enable librarians to build foundations for digital scholarship centers and services. For transcript and more information, visit http://www.loc.gov/today/cyberlc/feature_wdesc.php?rec=8520
Views: 275 LibraryOfCongress
Analyzing Documents for Content
 
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7th grade students- as you being to analyze primary source documents for the first time, use this video to see the thought process behind annotating and filling out the Content Graphic Organizer. Leave a comment below or email Ms. Russell if you have any questions.
Views: 254 MsABRussell
Analysis of a Visual Text ("Gleaners")
 
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This clip focuses on "The Gleaners," by Millet. Visit the class blog (www.micki-clark.com/blog) for specifics about the assignment.
Views: 1350 Micki Clark
How To Write A Good Research Paper Fast
 
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An eye-opening talk. Professor Simon Peyton Jones, Microsoft Research, gives a guest lecture on writing. Seven simple suggestions: don't wait - write, identify . Writing papers and giving talks are key skills for any researcher, but they aren't easy. In this pair of presentations, I'll describe simple guidelines that I follow for . In this video we take you through steps of writing a quality custom college research paper and ensuring you get a high grade while at it.. To buy a research paper .
Views: 104 Michael Nielson
Ido Dagan: Open Knowledge Graphs: Consolidating and Exploring Textual Information
 
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IDO DAGAN TITLE: Open Knowledge Graphs: Consolidating and Exploring Textual Information ABSTRACT: How can we capture effectively the information expressed in multiple texts? How can we allow people, as well as computer applications, to easily explore it? The current semantic NLP pipeline typically ends at the single sentence level, putting the burden on applications to consolidate related information that is spread across different texts. Further, semantic representations are often based on non-trivial pre-specified schemata, which require expert annotation and hence complicate the creation of large scale corpora for effective training. In this talk, I will outline a proposal for a novel open representation of the information exressed jointly by multiple texts, which we term Open Knowledge Graphs (OKG). First, we follow the spirit of “open” semantic approaches, such as Open Information Extraction (OIE) and more concretely the recent Question-Answer SRL (QA-SRL) paradigm, which represent semantic structure solely via natural language expressions. We extend this approach to define a schema-free graph structure that captures core semantic relationships within a sentence. Second, we follow recent proposals that collapse co-referring elements into a single node in semantic graph structures, and apply them to our open graphs over multiple texts. The resulting consolidated graph bears similarities to traditional knowledge graphs, with nodes corresponding to real-world elements and edges corresponding to statements that relate them. Yet, our graphs remain completely open, capturing a set of natural-language statements that are expressed jointly by the input texts. Finally, an entailment-based layer is proposed to capture information redundancies between these statements. We created a medium-size data set of expert-annotated graphs for news tweets, and use it as a test set for devising a baseline system for predicting graph structure. In parallel, we are working to derive open graph structures from QA-SRL towards large scale crowd-sourced annotations, that will enable the application of more principled learning techniques. As a flagship application, we are developing an interactive abstractive summarization system that allows exploring graph information, showing promising prospects in an initial user study. I will conclude by pointing at possible directions in which Open Knowledge Graphs might evolve.
Views: 524 AI2
Step Up to Writing: Synthesizing Information from Sources (T7 11)
 
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http://www.voyagersopris.com/literacy/step-up-to-writing/overview