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Text Mining and Analytics Made Easy with DSTK Text Explorer
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Text Explorer helps user to do text mining and text analytics task easily. It allows text processing using stopwords, stemming, uppercase, lowercase and etc. It also has features in sentiment analysis, text link analysis, name entity, pos tagging, text classification using stanford nlp classifier. It allows data scraping from images, videos, and webscraping from websites. For more information, visit: http://dstk.tech
Views: 3647 SVBook
SAS TextParsing TextFilter
 
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How to use Text Parsing and Text Filter in SAS Enterprise Miner?
Views: 2148 Dothang Truong
Text Mining
 
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An overview of the research I've done so far. Focuses mainly on the history of text mining and named entity recognition.
Views: 43 Jeff Shaul
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 482340 sentdex
Data Mining Tasks
 
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Including core tasks
Views: 1866 bade rebecca
SAS TextTopic
 
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How to use Text Topic in SAS Enterprise Miner?
Views: 1206 Dothang Truong
TEXT MINING
 
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MSC.IT PART 1 SEM I SUBJECT:DATA MINING Consider the suitable data for text mining and Implement the Text Mining technique using R-Tool
Views: 287 Priyanka Jadhav
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 7144 Story by Data
Tagging Text for NLP with LightTag
 
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How do we get labeled data for our NLP tasks? LightTag makes it easy to label text with a team.
Views: 2366 Tal Perry
Deep Learning vs Multidimensional Classification in Human-Guided Text Mining (GI研・天神イムズ・日本語・スクリーン...
 
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Deep (Neural) Learning has recently become popular in AI research.  The method is traditionally showcased in vision-related tasks where input can be easily regulated.  However, when applied to text mining, the irregular textual input becomes a hurdle.  Overcoming the hurdle involves processing the text and using its frequency distribution as a numeric input.  This paper compares the technology with a recently proposed method in multidimensional classification. The specific feature in focus is a human-guided system where the learning dataset is not available at once but arrives gradually, along with human annotation.
Views: 118 Marat Zhanikeev
KNIME: A Tool for Data Mining
 
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KNIME is very helpful tool for Data Mining tasks like Clustering, Classification, Standard Deviation and Mean
Views: 31235 Sania Habib
Data Mining and Analytics Made Easy with DSTK DSTKStudio
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Studio allows user to do data mining task easily. DSTK Studio allows Data Preparation which includes normalization and feature scaling, Data Exploration with descriptives statistics, inferential statistics, regressions and data visualizations. DSTK Studio allows predictive analytics using KNN, neural network, linear regressions and naive bayes. DSTK Studio allows you to set your own preferred RStudio, Spreadsheet application, and modeling application, and can be expanded with plugins with R Scripts. For more information, visit: http://dstk.tech
Views: 3587 SVBook
Natural Language Processing with Graphs
 
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William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We'll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.
Views: 33960 Neo4j
SAS TextMining Introduction
 
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Introduction to Text Mining
Views: 1634 Dothang Truong
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 42000 Red Apple Tutorials
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 271260 Last moment tuitions
Create Recommendation and Prediction Data Product in Five Minutes
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK helps you to create recommendation and prediction application based on your data and allow you to reuse and distribute the application. You can create your software to interface with the application. For more information, visit: http://dstk.tech
Views: 51 SVBook
Machine Learning - Text Similarity with Python
 
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Learn Machine Learning https://pythonprogramminglanguage.com/machine-learning/ https://pythonprogramminglanguage.com/machine-learning-tasks/ https://pythonprogramminglanguage.com/bag-of-words/ https://pythonprogramminglanguage.com/bag-of-words-euclidian-distance/ Learn Python? https://pythonprogramminglanguage.com/
Views: 10839 Machine Learning
Twitter Text Mining with SAS
 
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This video is an exercise doing Twitter Text mining with SAS. You may get relevant information, codes, and jar files from my website, http://web.ics.purdue.edu/~jinsuh/analyticspractice-twittersas.php.
Views: 416 Jinsuh Lee
Text-mining for rapid knowledge discovery
 
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Discussion as to how Elsevier Life Sciences Technologies help researchers gain access to insights and scientific data details without having to read dense, detailed articles in detail.
DM3 Data Mining Tasks مهام التنقيب عن البيانات
 
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أ.محمود رفيق الفرا مختصر مساق التنقيب عن البيانات Data Mining
Views: 4825 MahmoudRFarra
Intro to Text Mining - The Future of Social Science Research
 
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How will computational methods benefit social science research in the near future? Rada Mihalcea, Professor in the Computer Science and Engineering department at the University of Michigan, shares her thoughts. Rada is an instructor on SAGE Campus’ Introduction to Text Mining for Social Scientists online course. Find out more: https://campus.sagepub.com/introduction-to-text-mining-for-social-scientists/
Views: 73 SAGE Ocean
SAS Visual Text Analytics Demo
 
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Mary Beth Ainsworth, SAS Global Product Marketing Manager for Text Analytics, and Simran Bagga, Principal Product Manager for Text Analytics at SAS, provide a look at SAS Visual Text Analytics in action. LEARN MORE ABOUT SAS VISUAL TEXT ANALYTICS Get maximum value from your unstructured data using a wide variety of modeling approaches – including supervised and unsupervised machine learning, linguistic rules, categorization, entity extraction, sentiment analysis and topic detection. SAS Visual Text Analytics helps you overcome the challenges of identifying and categorizing large volumes of text data. https://www.sas.com/vta SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 6197 SAS Software
INTRODUCTION TO DATA MINING 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 ------------------------------------------------- Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 118050 LearnEveryone
Extracting Knowledge from Informal Text
 
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The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-time information on current events. I will describe several NLP tools we have adapted to handle Twitter�s noisy style, and present a system which leverages these to automatically extract a calendar of popular events occurring in the near future (http://statuscalendar.cs.washington.edu). I will further discuss fundamental challenges which arise when extracting meaning from such massive open-domain text corpora. Several probabilistic latent variable models will be presented, which are applied to infer the semantics of large numbers of words and phrases and also enable a principled and modular approach to extracting knowledge from large open-domain text corpora.
Views: 4719 Microsoft Research
Python NLTK Synonyms and Antonyms | Python Text Mining | Python Natural Language Processing
 
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This video tutorial helps you to learn basics of Python NLTK. In this example I taught how to use wordnet library to identify antonyms and synonym of the given word.
Views: 156 Amit Sharma
Text Mining and Analytics | DelftX on edX | Course About Video
 
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Take this course on edX: https://www.edx.org/course/text-mining-analytics-delftx-txt1x#! ↓ More info below. ↓ Follow on Facebook: https://www.facebook.com/edX Follow on Twitter: https://www.twitter.com/edxonline Follow on YouTube: https://www.youtube.com/user/edxonline About this course The knowledge base of the world is rapidly expanding, and much of this information is being put online as textual data. Understanding how to parse and analyze this growing amount of data is essential for any organization that would like to extract valuable insights and gain competitive advantage. This course will demonstrate how text mining can answer business related questions, with a focus on technological innovation. This is a highly modular course, based on data science principles and methodologies. We will look into technological innovation through mining articles and patents. We will also utilize other available sources of competitive intelligence, such as the gray literature and knowledge bases of companies, news databases, social media feeds and search engine outputs. Text mining will be carried out using Python, and could be easily followed by running the provided iPython notebooks that execute the code. FAQ Who is this course for? The course is intended for data scientists of all levels as well as domain experts on a managerial level. Data scientists will receive a variety of different toolsets, expanding knowledge and capability in the area of qualitative and semantic data analyses. Managers will receive hands-on oversight to a high-growth field filled with business promise, and will be able to spot opportunities for their own organization. You are encouraged to bring your data sources and business questions, and develop a professional portfolio of your work to share with others. The discussion forums of the course will be the place where professionals from around the world share insights and discuss data challenges. How will the course be taught? The first week of the course describes a range of business opportunities and solutions centered around the use of text. Subsequent weeks identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. Using real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers. What types of certificates does DelftX offer? Upon successful completion of this course, learners will be awarded a DelftX Professional Education Certificate. Can I receive Continuing Education Units? The TU Delft Extension School offers Continuing Education Units for this course. Participants of TXT1x who successfully complete the course requirements will earn a Certificate of Completion and are eligible to receive 2.0 Continuing Education Units (2.0 CEUs) How do I receive my certificate and CEUs? Upon successful completion of the course, your certificate can be printed from your dashboard. The CEUs are awarded separately by the TU Delft Extension School. ------- LICENSE The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Views: 3012 edX
Data Science for Business: The 9 Most Common Data Mining Tasks
 
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This video highlights the 9 most common data mining methods used in practice. For a related video, watch "Supervised vs. Unsupervised Methods": https://www.youtube.com/watch?v=i3itDGwhLq4 This video was created by Cognitir. Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 2825 Cognitir
Data Preprocessing
 
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Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 40365 Vidya-mitra
Weka text mining
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/sci-indexed-computer-science-journals/
Views: 523 PHDPROJECTS. ORG
Text Analysis With SpaCy, NLTK, Gensim, Skearn... - Bhargav Srinivasa Desikan - PyCon Israel 2018
 
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Text Analysis With SpaCy, NLTK, Gensim, Skearn, Keras and TensorFlow The explosion in Artificial Intelligence and Machine Learning is unprecedented now - and text analysis is likely the most easily accessible and understandable part of this. And with python, it is crazy easy to do this - python has been used as a parsing langauge forever, and with the rich set of Natural Language Processing and Computational Linguistic tools, it's worth doing text analysis even if you don't want to. The purpose of this talk is to convince the python community to do text analysis - and explain both the hows and the whys. Python has traditionally been a very good parsing language, aruguably replacing perl for all text file handling tasks. Reading files, regular expressions, writring to files, crawling on the web for textual data have all been standard ways to use python - and now with the Machine Learning and AI explosion - we have a great set of tools in python to understand all the textual data we can so easily play with. I will be briefly talking aboubt the merits, de-merits and use-cases of the most popular text processing libraries. In particular, these will be spaCy, NLTK, gensim. I will also talk about how to use traditional Machine Learning libraries for text analysis, such as scikit-learn, Keras and TensorFlow. Pre-processing is the one of the most important steps of Text Analysis, and I will talk more about this - after all, garbage in, garbage out! The final part of the talk will be about where to get your data - and how to create your own textual data as well. You could analyse anything, from your own emails and whatsapp conversations to freely available British Parliament transcripts!
Views: 1311 PyCon Israel
Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 25911 Web Educator
Piotr Borkowski - Semantic methods of categorization in the tasks of text document analysis
 
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In my PhD thesis entitled ``Semantic methods of categorization in the tasks of text document analysis'', a new algorithm of semantic categorization of documents was proposed and examined. On its basis, a new algorithm for category aggregation was developed, a family of semantic algorithms of classifiers, as well as a heterogeneous classifier committee (which combines the algorithm of semantic categorization and previously known classifiers). In my talk I will briefly present their concepts and the results of their effectiveness studies.
Views: 73 IPI PAN
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 57027 Last Minute Tutorials
Power up your Google Sheets with Text Analysis
 
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Analyze thousands of texts at scale with Machine Learning. Stop spending time tagging every single row of text, let AI do the work for you. Eliminate manual and repetitive tasks when processing rows of text: - Save time by automatically tagging text in Google Sheets. - 100x faster than doing it with humans. - 50x cheaper than doing it with humans. Make the analysis of your spreadsheets more efficient: - Ensure consistent tagging criteria, 24/7, no errors. - Get insights faster from your data with automated analysis. - Learn from your data with customized tags. Obtain reporting and insights: - Directly integrated into Google Sheets. - Build customized reports with Google Sheets or your own BI tools. - Quick start with pre-made models such as sentiment analysis or keyword extraction. Add MonkeyLearn to Google Sheets: https://chrome.google.com/webstore/detail/monkeylearn/cedpjjdkkbclbllppflfmoacfcjpmdng/ Request a demo: https://monkeylearn.typeform.com/to/nneRwV Learn more about MonkeyLearn: https://monkeylearn.com/
Views: 248 MonkeyLearn
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1483 UA German Department
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
35:04
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Clustering Sentence Level Text Using a Novel Fuzzy Relational Clustering Algorithm
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Clustering Sentence Level Text Using a Novel Fuzzy Relational Clustering Algorithm In comparison with hard clustering methods, in which a pattern belongs to a single cluster, fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of membership. This is important in domains such as sentence clustering, since a sentence is likely to be related to more than one theme or topic present within a document or set of documents. However, because most sentence similarity measures do not represent sentences in a common metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of Gaussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy clustering algorithm that operates on relational input data; i.e., data in the form of a square matrix of pairwise similarities between data objects. The algorithm uses a graph representation of the data, and operates in an Expectation-Maximization framework in which the graph centrality of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping clusters of semantically related sentences, and that it is therefore of potential use in a variety of text mining tasks. We also include results of applying the algorithm to benchmark data sets in several other domains.
Views: 590 jpinfotechprojects
BioNLP Open Shared Tasks 2019 SeeDev and BB4 tasks @ BLAH5
 
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https://togotv.dbcls.jp/20190315.html Biomedical Linked Annotation Hackathon (BLAH) 5 was held in Database Center for LifeScience (DBCLS) in Kashiwa, Japan. On the first day of the Hackathon (Feb. 12), public symposium of the BLAH 5 was held. In this talk, Mouhamadou Ba makes a presentation entitled "BioNLP Open Shared Tasks 2019 SeeDev and BB4 tasks". (12:17)
Views: 15 togotv
R and OpenNLP for Natural Language Processing NLP -  Part 1
 
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Overview and demo of using Apache OpenNLP library in R to perform basic Natural Language Processing (NLP) tasks like string tokenizing, word tokenizing, Parts of Speech (POS) tokenizing This is a getting started guide covering demos of OpenNLP coding in R
Views: 22111 Melvin L
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 249499 Augmented Startups
UiPath Citrix Automation | Image and Text Automation in UiPath | UiPath Training | Edureka
 
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** RPA Training - https://www.edureka.co/robotic-process-automation-training ** This Edureka video on "UiPath Citrix Automation" will help you know how to automate web using UiPath. Below are the topics covered in this UiPath Citrix Automation: 1. What is RPA 2. What are Virtual Machines 3. How to Automate Tasks on Virtual Machines 4. Citrix Automation using Uipath 5. Hands-On - Automating Tasks on Simple Desktop Application 6. Hands-On - Automating Tasks on Application Running on Virtual Machine Subscribe to our channel to get video updates. Hit the subscribe button above. How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s RPA training makes you an expert in Robotic Process Automation. Robotic Process Automation is Automation of repetitive and rule-based tasks. In Edureka's RPA online training, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you can automate the data extraction from the internet, login process, image recognition process and many more. After completing the RPA Training, you will be able to: 1. Know about Robotic Process Automations and how it works 2. Know about the patterns and key considerations while designing a RPA solution 3. Know about the leading RPA tool i.e. UiPath 4. Gain practical knowledge on designing RPA solutions using both the tools 5. Perform Image and Text automation 6. Create RPA bots and perform data manipulation 7. Debug and handle the exceptions through the tool - - - - - - - - - - - - - - Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 12731 edureka!
Tutorial on K Means Clustering using Weka
 
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Tutorial on how to apply K-Means using Weka on a data set
Views: 19984 Jyothi Rao
UiPath PDF Data Extraction | OCR Data Extraction | UiPath Tutorial | RPA Training | Edureka
 
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** RPA Training: https://www.edureka.co/robotic-process-automation-training ** This session on UiPath PDF Data Extraction will cover all the concepts on how to extract data from PDFs using UiPath. Below are the topics covered in the video: 02:03 Extracting Large Texts 16:49 Extracting Specific Elements Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka How it Works? 1. This is a 4 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------------------------- About the Robotic Process Automation Course Edureka’s RPA training helps you to understand the concepts around Robotic Process Automation using the leading RPA tool named ‘UiPath’. Robotic Process Automation is the automation of repetitive and rule based human tasks working with the software applications at the presentation/UI level i.e. no software integrations are needed at middleware, server or database levels. In this course, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you will be able to automate real-world processes at the enterprise level such as Insurance Claims Processing, Accounts Payable / Purchase Orders Processing, Invoice Processing, Complaints Management, Customer Feedback Analysis, Employee Onboarding, Compliance Reporting, and many more. -------------------------------------------------------------------------------------- What are the Objectives of our Robotic Process AutomationTraining? After completing this course, you will be able to: 1. Know about Robotics Process Automations and their working 2. Assess the key considerations while designing an RPA solution 3. Work proficiently with the leading RPA tool ‘UiPath 4. Have practical knowledge on designing RPA solutions using UiPath 5. Perform Image and Text automation 6. Learn Data Manipulation using variables and arguments 7. Create automations with applications running in Virtual Environments 8. Debug and handle exceptions in workflow automations ------------------------------------------------------------------------------------------------------- Why learn Robotic Process Automation? Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of wide variety of industries, hence reducing the time and increasing the output. Some of facts about RPA includes: 1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025 2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore 3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million ------------------------------------------------------------------------------------------------------- What are the pre-requisites for this course? To master the concept of RPA, you need to have basic understanding of : 1.Basic programming knowledge of using if/else and switch conditions 2.Logical thinking ability Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 18735 edureka!
Deep Learning Approach for Extreme Multi-label Text Classification
 
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Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 11488 Microsoft Research
Open and Exploratory Extraction of Relations and Common Sense from Large Text Corpora - Alan Akbik
 
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Alan Akbik November 10, 2014 Title: Open and Exploratory Extraction of Relations (and Common Sense) from Large Text Corpora Abstract: The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this talk, I present an approach and a graphical tool that allows even novice users to quickly and easily define extraction patterns over dependency trees and directly execute them on a very large text corpus. This enables users to explore a corpus for structured information of interest in a highly interactive and data-guided fashion, and allows them to create extractors for those semantic relations they find interesting. I then present a project in which we use Information Extraction to automatically construct a very large common sense knowledge base. This knowledge base - dubbed "The Weltmodell" - contains common sense facts that pertain to proper noun concepts; an example of this is the concept "coffee", for which we know that it is typically drunk by a person or brought by a waiter. I show how we mine such information from very large amounts of text, how we quantify notions such as typicality and similarity, and discuss some ideas how such world knowledge can be used to address reasoning tasks.
Views: 1288 AI2