This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 31095 cesine0
This is the part tWO tutorial showing how you can perform basic text mining with General architecture for text engineering (GATE) using the JAPE transducer
Views: 440 ChudeTV
In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 36263 Alba Madriz
In less than 5 minutes, we take 20,000 tweets from Datasift, perform text mining through the Lexalytics/Semantria Excel Plugin, import the results into Tableau, and start visualizing cool stuff. (This process applies to Tableau version 8.2).
Views: 19726 Lexalytics
In this introduction to text mining with Voyant I cover: 1) Data cleaning (text editors, Notepad++ and Sublime Text) 2) Loading your text into Voyant 3) Expectations, what Voyant can and cannot do 4) Working with common visualization tools and making possible connections 5) Exporting visualizations
Views: 428 Bruce Matsunaga - ASU
This example takes a Course syllabus (mostly semantics courses) and highlights the reading lists using Jape grammars. It recognizes things like Van Fintel and Heim 2003 as a citation and Chapters 1, 3 and 8 as a reading selections and Week 1 as a due date (among others). Its another example of what GATE can do, in this case to help automate tasks like downloading a reading list. The files are in here https://github.com/cesine/GATEinSpring/tree/master/gate/WEB-INF/gate-files
Views: 12952 cesine0
WordStat a content analysis and text mining software from Provalis Research.
Views: 14703 Provalis Research - Text Analytics Software
In this tutorial, we will go over how to utilize LIWC software (http://liwc.wpengine.com/) to conduct content and sentiment analysis on your very own documents. This is Part 2/2 of our video series showing how to scrape and analyze reddit comment threads. For Part 1, follow the link: https://www.youtube.com/watch?v=yexxcrPC7U8&feature=youtu.be
Views: 7205 I Johar
This video is a project to corroborate on whether movie plot summary would help in predicting a movie's box office success. Plot summary of a movie from any website is taken and is text processed to generate word vectors. Then a prediction model is developed which trains this data and applies the model to the testing data.
Views: 1286 dinesh yadav
Prodigy is an annotation tool for creating training data for machine learning models. In this video, I'll be talking about a few frequently asked questions and share some general tips and tricks for how to structure your NLP annotation projects, how to design your label schemes and how to solve common problems. PRODIGY ● Website: https://prodi.gy ● Forum: https://support.prodi.gy ● Recipes repo: https://github.com/explosion/prodigy-recipes THIS VIDEO [0:46] Binary of manual annotation? ● ner.teach vs. ner.match https://support.prodi.gy/t/877 ● Best practices for validation sets https://support.prodi.gy/t/693 [3:34] Accept or reject partial suggestions? ● How to score incompletely highlighted entities https://support.prodi.gy/t/625 ● Should I reject or accept partially correct predictions? https://support.prodi.gy/t/945 [5:35] Reject example or skip it? ● Reject or skip examples for text classifier annotations https://support.prodi.gy/t/998 ● Ignored sentences for text classification https://support.prodi.gy/t/1183 [7:30] What if I need to label long texts? Dealing with sparse data https://support.prodi.gy/t/518 Text categorization at document level https://support.prodi.gy/t/1160 [9:24] Fine-tune pre-trained model or start from scratch? ● Pre-trained model vs training a model from scratch https://support.prodi.gy/t/631/4 ● Fact extraction for earnings news https://support.prodi.gy/t/1023 ● Extracting current and prior company affiliations from bios https://support.prodi.gy/t/1176 ● NER or PhraseMatcher https://support.prodi.gy/t/686 FOLLOW US ● Explosion AI: https://twitter.com/explosion_ai ● Ines Montani: https://twitter.com/_inesmontani ● Matthew Honnibal: https://twitter.com/honnibal
Views: 2246 Explosion AI
Think of this as an unboxing video for annotation software - this is the first time I've tried running any of this software. Don't expect any good demos, I'm just showing you where to find them along with some resources. GATE https://gate.ac.uk/family/ MAE2 https://keighrim.github.io/mae-annotation/ BRAT http://brat.nlplab.org/features.html WebAnno https://webanno.github.io/webanno/ Annis http://corpus-tools.org/annis/ SLATE https://bitbucket.org/dainkaplan/slate/ Works cited: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications https://smile.amazon.com/Natural-Language-Annotation-Machine-Learning/dp/1449306667/ Overview of Annotation Creation: Processes & Tools. Finlayson, Mark & Erjavec, Tomaž. (2016). https://www.researchgate.net/publication/301847215_Overview_of_Annotation_Creation_Processes_Tools Handbook of Linguistic Annotation. "Collaborative Web-Based Tools for Multi-layer Text Annotation" pp 229-256 https://link.springer.com/chapter/10.1007/978-94-024-0881-2_8 Also, this is the document I meant to show at 14:21 in the video: Annotation Process Management Revisited Dain Kaplan, Ryu Iida, Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology http://www.lrec-conf.org/proceedings/lrec2010/pdf/129_Paper.pdf
Views: 2024 Norman Gilmore
Describes the process for building a tool to assist students in identifying relevant jobs using course descriptions. This was the term project for Business Intelligence Tools and Techniques (MSIS 5633), a graduate level course at Oklahoma State University.
Views: 331 Matthew Lee
This video demonstrates how to create text annotation templates in FLIR Tools. This is a great way to annotate images while processing them in FLIR Tools, or in the field during your thermographic inspections. All text data is saved with the JPEG and can be displayed in the inspection reports.
Views: 923 Infrared Training Center
There is an abundance of data in social media sites (Wikipedia, Facebook, Instagram, etc.) which can be accessed through web APIs. But how do we know that the data from the Wikipedia article on "Golden Gate Bridge" goes along with the data from "Golden Gate Bridge" Facebook page? This represents an important question about integrating data from various sources. In this talk, I'll outline important aspects of structured data mining, integration and entity resolution methods in a scalable system.
Views: 5578 PyTexas
Learn RapidMiner! Come ask questions! Learn how to share processes in XML and RMP formats. Load, configure, and process the Wordnet extension and sentiwordnet.
Views: 578 NeuralMarketTrends
A short introduction session on how to download, install and get going with the one month free trial of Quirkos - easy to use software for qualitative text data analysis.
Views: 200 Quirkos Software
Excel Formulas Below =========================== 1) ROUNDED TIME = HOUR(A5)/24+CEILING(MINUTE(A5),15)/(24*60) 2) MONTH =TEXT(A4, "mm") 3) DAY =TEXT(A4,"dd") 4) DAY OF WEEK =TEXT(A4, "dddd")
Views: 557 CodeCowboyOrg
Demonstration of Annotation Tools Wendy Chapman iDASH Annotation Environment: Recruiting, Training, and Facilitating Annotation Annotation Tools Chih- Hsuan Wei PubTator: A Web-based Tool for Machine-Assisted Annotation Brett South Realizing Efficient Annotation with eHOST: Extensible Human Oracle Suite of Tools Tyler Forbush The Whole is Greater Than the Sun of its Parts: An Integrated Approach to Char Review and Annotation in the VA iDash NLP Annotation Workshop Saturday, September 29, 2012 Calit2, Atkinson Hall Auditorium, UCSD
Views: 3595 Calit2ube
Mark Greenwood, Research Associate, University of Sheffield This talk will present a brief overview of the open-source GATE infrastructure for text mining and semantic search. The focus will be on practical examples of semantic enrichment through thesauri, term and entity recognition, and semantic annotation based on Linked Open Data. Semantic search interfaces developed within projects with the British Library, the UK National Archives, and FERA will also be presented.
Views: 2079 British Library Labs
In this video I will show you how to do sentiment or text analysis using Power BI. Let's say you run a website to sell handicrafts. Your users submit feedback on your site, and you'd like to find out what users think of your brand, and how that changes over time as you release new products and features to your site. The sentiment analysis service will return a score between 0 and 1 denoting overall sentiment in the input text. Scores close to 1 indicate positive sentiment, while scores close to 0 indicate negative sentiment. As the service used only supports English and Spanish data, in this video I will show you how to translate the text to english to then analyze it with the sentiment service. Donwload Power BI file (Free membership required): https://curbal.com/power-bi-solution-templates-for-facebook Enjoy! Link to Twitter sentiment analysis video: https://www.youtube.com/watch?v=Cof8cEE-0a4 Link to Microsoft Text Analytics Service: https://www.microsoft.com/cognitive-services/en-us/text-analytics-api Link to Google Translate API service: https://cloud.google.com/translate/docs/ Looking for a download file? Go to our Download Center: https://curbal.com/donwload-center SUBSCRIBE to learn more about Power and Excel BI! https://www.youtube.com/channel/UCJ7UhloHSA4wAqPzyi6TOkw?sub_confirmation=1 Our PLAYLISTS: - Join our DAX Fridays! Series: https://goo.gl/FtUWUX - Power BI dashboards for beginners: https://goo.gl/9YzyDP - Power BI Tips & Tricks: https://goo.gl/H6kUbP - Power Bi and Google Analytics: https://goo.gl/ZNsY8l ABOUT CURBAL: Website: http://www.curbal.com Contact us: http://www.curbal.com/contact ▼▼▼▼▼▼▼▼▼▼ If you feel that any of the videos, downloads, blog posts that I have created have been useful to you and you want to help me keep on going, here you can do a small donation to support my work and keep the channel running: https://curbal.com/product/sponsor-me Many thanks in advance! ▲▲▲▲▲▲▲▲▲▲ QUESTIONS? COMMENTS? SUGGESTIONS? You’ll find me here: ► Twitter: @curbalen, @ruthpozuelo ► Google +: https://goo.gl/rvIBDP ► Facebook: https://goo.gl/bME2sB ► Linkedin: https://goo.gl/3VW6Ky
Views: 1627 Curbal
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Using CART and logistic regression to predict negative sentiment. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 2698 MIT OpenCourseWare
Hey all. NO SOFTWARE'S ARE need to EXTRACT TEXT FROM THE IMAGE or a SCANNED DOCUMENT !! ITS REALLY EASY !! AND YOU CAN DO IT USING UR PC/LAPTOP TOO !! Have a look at it ! :) Please share it ^_^ I hope this helps MANY ! :) Do You Have any questions ?? please comment i will make solution through videos on that Problem ! :) I hope this video helps :) , don't forget to checkout my other videos in this playlist, they are really worth watching. Like,comment,rate video :) Any suggestions are always welcome! Thank you very much for watching have a great day ! http://www.youtube.com/playlist?list=PLB3i9IKhwBX9WIPAr0Jw75cb_VxGF8ftf Support my page :) : www.fb.com/Dosomethingbesomeone I have created this page to spread smile on others face :) I hope you like it !
Views: 350947 sampath ramkumar
SEMANTIXS: System for Extraction of doMAin-specific iNformation from Text Including compleX Structures Refer the following links for more information on the project and related thesis -- - http://www.cs.iastate.edu/~semantix/ - http://sourceforge.net/projects/semantixs/
Views: 1821 semantixs
CAT or Coding Analysis Toolkit is a web-based suite of CAQDAS tools. It is free and open source software, and is developed by the Qualitative Data Analysis Program of the University of Pittsburgh CAT is able to import Atlas.ti data, but also has an internal coding module. It was designed to use keystrokes and automation as opposed to mouse clicks, to speed up CAQDAS tasks.
Views: 176 WikiTubia
Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 94818 Well Academy
If you have questions or comments on the contents of this video, please email us at [email protected] There has been considerable change in the relationship between customers and companies. With the ease of online access, customers are now empowered to know their vendors and control their relationships with them including the freedom to switch vendors easily. Companies have the ability to know their customers and market to them on a personalized basis using data mining and predictive analytics technologies. Customer analytics unlock insights that enable companies to add new customers and grow their business as well as improve their understanding of what their customers want. It uncovers hidden insights in customer data to enable the creation of personalized experiences that win more business while reducing costs and increasing customer loyalty. Customer analytics solutions using IBM technology can help your organization: •Identify and target the best customers for marketing programs •Predict which customers will respond to your offers •Improve sales forecasting and minimize sales cycles •Predict which customers are at risk of leaving and retain them •Maximize customer lifetime value through personalized up-sell and cross-sell This webinar illustrates how to extend your organization's ability to make smarter decisions using Predictive Analytics. The webinar also includes a demonstration on the of SPSS Modeler to build a model for a specific use case.
Views: 14480 LPA Software Solutions
This is a quick demo of how to use LingPipe NLP API to tokenize, sentence-split, and part-of-speech discharge summaries (and probably other medical natural language text).
Views: 2580 Sam M
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 105668 Siraj Raval
Exaptive uses natural language processing to identify higher level topics within raw text documents. In this video, we take a look at how the process leads to explorable results. TRANSCRIPT: Exaptive makes it easier to extract meaningful insight out of raw text. In some cases Exaptive makes it possible when the barrier was too high before. Sources change quickly. So do the questions you want to ask. And not every question can be anticipated ahead of time. The best way to get insight from raw text is to be able to explore and pivot quickly. In this example, we used raw text from online discussion forums. This chart represents one forum over time. The analyst first drills down by selecting a geographic area. Then selecting communications over a certain period of time after seeing a quantitative view of how much activity is taking place. By building a network diagram, the analyst can see who is talking, and who is listening. And then the analyst can view different periods of time during the discussion to see how the network evolves. The topics of conversations can also be added to the network. The topics are an aggregation of the raw text from the discussion forums, showing who is talking about what and when. A different visualization can provide a view of how these topics occur, and recur, over time. In this example, voting became a topic September through November. And crime was a recurring topic in the winter and at the height of summer. The category - the word ‘crime’ - was not an explicit topic of conversation. The raw text - the words used - were processed and aggregated into those topic categories to draw attention to the raw data most relevant to the analysis. Findings can then be easily shared for further collaboration. In this example, it would be via PDF. All these components, whether visual or behind the scenes, are independent, reusable components in the Exaptive platform that can be leveraged with different data to provide the insight you need, as you need it. www.exaptive.com
Views: 42 Exaptive