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DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 1 Probabilistic Retrieval Model   Basic
 
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https://www.coursera.org/learn/text-retrieval
Views: 984 Ryo Eng
Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Lecture 47 — Information Extraction - Natural Language Processing | Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 3 Query Likelihood Retrieval Function
 
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https://www.coursera.org/learn/text-retrieval
Views: 149 Ryo Eng
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: 366140 sentdex
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 8704 The Audiopedia
Lecture 61 — Information Retrieval | NLP | University of Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Information extraction made easy by Text Mining Solutions
 
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Information extraction brought to you by Text Mining Solutions we explain the process of text mining in 3 easy to understand steps. 1. Organise your input documents. 2. Processing your documents. 3. Analyse your results. This video is perfect for anyone new to Text Mining. For more information go to http://www.textminingsolutions.co.uk Follow Text Mining Solutions on: Facebook: https://www.facebook.com/TextMiningSolutions?fref=ts Twitter: https://twitter.com/Txt_Mining LinkedIn: https://www.linkedin.com/company/text-mining-solutions Music: http://www.purple-planet.com
Views: 463 TxtMining
Tutorial - Natural Language Processing for Music Information Retrieval. Information Extraction
 
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January 30th 2017 https://www.upf.edu/web/mdm-dtic/tutorial-natural-language-processing-for-music-information-retrieval In this tutorial, we focus on linguistic, semantic and statistical-­based approaches to extract and formalize knowledge about music from naturally occurring text. We propose to provide the audience with a preliminary introduction to NLP, covering its main tasks along with the state­-of-­the-­art and most recent developments. In addition, we will showcase the main challenges that the music domain poses to the different NLP tasks, and the already developed methodologies for leveraging them in MIR and musicological applications. Sergio Oramas, Music Technology Group http://mtg.upf.edu/ Luis Espinosa, Natural Language Processing Group https://www.upf.edu/web/taln María de Maeztu Strategic Research Program on Data-driven Knowledge Extraction https://www.upf.edu/web/mdm-dtic/
Cosine Similarity and IDF Modified Cosine Similarity
 
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This video tutorial explains the cosine similarity and IDF-Modified cosine similarity with very simple examples (related to Text-Mining/IR/NLP). It also demonstrates the Java implementation of cosine similarity. The source code can be downloaded from:- 1. Cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/cosine_similarity 2. IDF-Modified cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/idf_modified_cosine_similarity
Views: 7948 Dr. Niraj Kumar
Neural Models for Information Retrieval
 
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In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modeling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text. In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task. See more at https://www.microsoft.com/en-us/research/video/neural-models-information-retrieval-video/
Views: 2392 Microsoft Research
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 13881 Linguamatics
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 5 Statistical Language Model Part 2
 
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https://www.coursera.org/learn/text-retrieval
Views: 64 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   1 1 1 Course Welcome Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 589 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 4 Statistical Language Model Part 1
 
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https://www.coursera.org/learn/text-retrieval
Views: 80 Ryo Eng
Data Mining-Structured Data, Unstructured data and Information Retrieval
 
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Structured Data, Unstructured data and Information Retrieval
Views: 1251 John Paul
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 81082 Francisco Iacobelli
Weighting by Term Frequency - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 12247 Udacity
INFORMATION RETRIEVAL TECHNIQUES IN HINDI
 
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Find the notes of INFORMATION RETRIEVAL on this link - https://viden.io/knowledge/information-retrieval?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 5619 LearnEveryone
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 2 Feedback in Vector Space Model   Rocc
 
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https://www.coursera.org/learn/text-retrieval
Views: 84 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 2 4 Implementation of TR Systems
 
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https://www.coursera.org/learn/text-retrieval
Views: 109 Ryo Eng
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9272 Stat Pharm
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 6558 LearnEveryone
The Future of Information Retrieval Part 1
 
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Interviews with prominent experts in the field of information retrieval, internet search and text mining. Interviewer: Peter Kawinek www.matrixware.com
Views: 6927 matrixware
Webinar on Information retrieval from unstructured text at scale using advanced deep learning
 
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This webinar talks about converting unstructured e-commerce text into structured format by leveraging multi-task deep learning. The existing problem: e-sellers upload text information in unstructured or semi-structured format. E-commerce engines use naive text search techniques as a result of which search engine performance suffers because of less number irrelevant results. Can we convert unstructured text into a structured (key:value) format with high precision and high recall? Challenges: Presence of multiple products in text description (e.g., blue top goes well with black jeans, which is the primary product?), semantic dis-ambiguity (e.g., 'Blue' as brand vs color, top as filler vs fashion category), context spread across long sentences (e.g., this is stunning red color top, ....., this sleeveless piece is a gem, ....) scale: more than 4 m e-fashion listings crawled, parsed and converted into structured format. Let us dive into the solutions with this webinar. Agenda Motivate the problem by examples Key challenges and requirements to solve the problem Bidirectional LSTM based deep network Case Study and deployment challenges
Views: 473 Analytics Vidhya
The Future of Information Retrieval Part 2
 
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Interviews with prominent experts in the field of information retrieval, internet search and text mining. Interviewer: Peter Kawinek www.matrixware.com
Views: 2951 matrixware
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 1 Evaluation of TR Systems
 
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https://www.coursera.org/learn/text-retrieval
Views: 83 Ryo Eng
Chenyan Xiong: "Text Representation, Retrieval, and Understanding with Knowledge Graphs"
 
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Chenyan Xiong Title: "Text Representation, Retrieval, and Understanding with Knowledge Graphs" Abstract: Search engines and other information systems have started to evolve from retrieving documents to providing more intelligent information access. However, the evolution is still in its infancy due to computers’ limited ability in representing and understanding human language. This talk will present my work addressing these challenges with knowledge graphs. The first part is about utilizing entities from knowledge graphs to improve search. I will discuss how we build better text representations with entities and how the entity-based text representations improve text retrieval. The second part is about better text understanding through modeling entity salience (importance), as well as how the improved text understanding helps search under both feature-based and neural ranking settings. This talk concludes with future directions towards the next generation of intelligent information systems.
TF-IDF for Machine Learning
 
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Quick overview of TF-IDF Some references if you want to learn more: Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf Scikit's implementation: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer Scikit's code example for feature extraction: http://scikit-learn.org/stable/modules/feature_extraction.html Stanford notes: http://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html
Views: 20254 RevMachineLearning
FOM Text Mining Kurs - Teil 3 Information Retrieval und Markup Matcher
 
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Diese Video Reihe beschäftigt sich mit dem Thema Text Mining auf mit der SAS 9.3 Software und gibt eine Hilfestellung zu einer Vorlesungsreihe an der FOM - der Hochschule für Ökonomie und Management.
Views: 520 Andreas Jürgens
Text mining Lecture 7
 
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Text Mining Lecture 7 Topic: Natural Language Processing in Accounting, Auditing and Finance: A synthesis of the Literature with a Roadmap for Future Research 01:33 Major Contribution of the Paper 02:56 Introduction 03:47 Objective 04:17 Literature Selection & Assessment 08:43 Analysis of Sample size N 14:11 NLP in Accounting , Auditing and Finance 16:48 Knowledge Organization, Categorization, and Retrieval 17:49 Taxonomy & Thesauri Generation 18:30 Information Retrieval 20:23 Fraud Prediction and Detection 21:57 Predicting Stock Prices and Market Activity 23:36 Firm- Specific Predicitions 24:23 Predictive Value of Annual Reports and Disclosures 25:27 Predictive of Web Content 29:56 Natural Language Processing & Readability Studies Topic: Detecting deceptive discussion in conference calls 36:29 Motivation 38:47 Literature review on linguistic features 44:29 Development of word lists to measure deception 1:02:53 Data 1:04:30 Parsing method for conference calls 1:10:29 Results for CFO 1:13:01 Similarities in Linguistic cues 1:15:01 Coding 1:23:02 Software Repository for Accounting and Finance
Information Retrieval Probabilistic Model
 
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Information Retrieval and Text Mining
Views: 30 ogsconnect
DATA MINING   2 Text Retrieval and Search Engines   1 1 2 Course Introduction Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 544 Ryo Eng
Information Retrieval WS 17/18, Lecture 10: Latent Semantic Indexing
 
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This is the recording of Lecture 10 from the course "Information Retrieval", held on 9th January 2018 by Prof. Dr. Hannah Bast at the University of Freiburg, Germany. The discussed topics are: Latent Semantic Indexing, Matrix Factorization, Singular Value Decomposition (SVD), Eigenvector Decomposition (EVD). Link to the Wiki of the course: https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718 Link to the homepage of our chair: https://ad.informatik.uni-freiburg.de/
Views: 725 AD Lectures
Information Retrieval and Extraction.
 
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Mining Name Entity From Wikipedia In many search domains, both contents and searches are frequently tied to named entities such as a person, a company or similar.One challenge from an information retrieval point of view is that a single entity can have more than one way of referring to it. In this project we describe how to use Wikipedia contents to automatically generate a dictionary of named entities and synonyms that are all referring to the same entity. Contact:- Nikhil Barote([email protected]) kunj Thakker([email protected]) shivani Poddar([email protected]) Ankit Sharma([email protected]).
Views: 511 Ankit Sharma
Evaluation 6: precision and recall
 
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Precision and recall are the two fundamental measures of search effectiveness. We discuss their building blocks (true/false positives/negatives), give a probabilistic interpretation, and provide an intuitive explanation of what they reflect. We also discuss why you should never report just the recall, or just the precision of a system.
Views: 16903 Victor Lavrenko
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 5 Web Indexing
 
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https://www.coursera.org/learn/text-retrieval
Views: 89 Ryo Eng
WDM 2: Structured Data, Unstructured data and Information Retrieval
 
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What is an IR System For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 11206 Oresoft LWC
DATA MINING   2 Text Retrieval and Search Engines   Lesson 6 3 Learning to Rank Part 3
 
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https://www.coursera.org/learn/text-retrieval
Views: 14 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 2 Evaluation of TR Systems   Basic Meas
 
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https://www.coursera.org/learn/text-retrieval
Views: 45 Ryo Eng
IU X-Informatics Unit 21:Web Search and Text Mining 6:Information Retrieval Technology
 
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Lesson Overview: The detailed technology of IR and web search is based on the important bag of words model. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.
IU X-Informatics Unit 21:Web Search and Text Mining 2: Web and Document/Text Search-The Problem I
 
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Lesson Overview: This lesson starts with the web with its size, shape (coming from the mutual linkage of pages by URL's) and universal power laws for number of pages with particular number of URL's linking out or in to page. The way Web search is performed by users is discussed. Information retrieval is introduced and a possible DIKW mapping for web search is given. A comparison is given between semantic searches as in databases and the full text search that is base of Web search. The ACM classification illustrates potential complexity of ontologies. Some differences between web search and information retrieval are given. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.