- Meta-Learning for Keyphrase Extraction - Dec 3, 2021.
This article explores Meta-Learning for Key phrase Extraction, which delves into the how and why of KeyPhrase Extraction (KPE) - extracting phrases/groups of words from a document to best capture and represent its content. The article outline what needs to be done to build a keyphrase extractor that performs well not only on in-domain data, but also in a zero-shot scenario where keyphrases need to be extracted from data that have a different distribution (either a different domain or a different type of documents).
- Sentiment Analysis with KNIME - Nov 29, 2021.
Check out this tutorial on how to approach sentiment classification with supervised machine learning algorithms.
- How to fast-track machine translation projects - Nov 16, 2021.
Data is the lifeblood of any successful machine learning model, and machine translation models are no exception. Without relevant and properly labelled data, even the most sophisticated model will be unable to achieve reliable results.
- Simple Text Scraping, Parsing, and Processing with this Python Library - Oct 29, 2021.
Scraping, parsing, and processing text data from the web can be difficult. But it can also be easy, using Newspaper3k.
- 15 Must-Know Python String Methods - Sep 21, 2021.
It is not always about numbers.
- Text Preprocessing Methods for Deep Learning - Sep 10, 2021.
While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.
- Semantic Search: Measuring Meaning From Jaccard to Bert - Jul 2, 2021.
In this article, we’ll cover a few of the most interesting — and powerful — of these techniques — focusing specifically on semantic search. We’ll learn how they work, what they’re good at, and how we can implement them ourselves.
- How to Train a Joint Entities and Relation Extraction Classifier using BERT Transformer with spaCy 3 - Jun 28, 2021.
A step-by-step guide on how to train a relation extraction classifier using Transformer and spaCy3.
- Applied Language Technology: A No-Nonsense Approach - Jun 25, 2021.
Here is a free entry-level applied natural language processing course that can fit into any beginner's roadmap to understanding NLP. Check it out.
- The Word “WORD” Has 13 Meanings - Jun 22, 2021.
Thoughts around Knowledge Graphs, the semantic nature of language, and the two main types of word ambiguity.
- A Graph-based Text Similarity Method with Named Entity Information in NLP - Jun 16, 2021.
In this article, the author summarizes the 2017 paper "A Graph-based Text Similarity Measure That Employs Named Entity Information" as per their understanding. Better understand the concepts by reading along.
- Topic Modeling with Streamlit - May 26, 2021.
What does it take to create and deploy a topic modeling web application quickly? Read this post to see how the author uses Python NLP packages for topic modeling, Streamlit for the web application framework, and Streamlit Sharing for deployment.
- Machine Translation in a Nutshell - May 17, 2021.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.
- How to Apply Transformers to Any Length of Text - Apr 12, 2021.
Read on to find how to restore the power of NLP for long sequences.
- Automated Text Classification with EvalML - Apr 6, 2021.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
- How to Begin Your NLP Journey - Mar 17, 2021.
In this blog post, learn how to process text using Python.
- Natural Language Processing Pipelines, Explained - Mar 16, 2021.
This article presents a beginner's view of NLP, as well as an explanation of how a typical NLP pipeline might look.
- Getting Started with 5 Essential Natural Language Processing Libraries - Feb 3, 2021.
This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.
- How to Clean Text Data at the Command Line - Dec 16, 2020.
A basic tutorial about cleaning data using command-line tools: tr, grep, sort, uniq, sort, awk, sed, and csvlook.
- Optimizing the Levenshtein Distance for Measuring Text Similarity - Oct 16, 2020.
For speeding up the calculation of the Levenshtein distance, this tutorial works on calculating using a vector rather than a matrix, which saves a lot of time. We’ll be coding in Java for this implementation.
- Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Semantics and Pragmatics - Aug 31, 2020.
Algorithms for text analytics must model how language works to incorporate meaning in language—and so do the people deploying these algorithms. Bender & Lascarides 2019 is an accessible overview of what the field of linguistics can teach NLP about how meaning is encoded in human languages.
- The NLP Model Forge: Generate Model Code On Demand - Aug 24, 2020.
You've seen their Big Bad NLP Database and The Super Duper NLP Repo. Now Quantum Stat is back with its most ambitious NLP product yet: The NLP Model Forge.
- Simple Question Answering (QA) Systems That Use Text Similarity Detection in Python - Apr 7, 2020.
How exactly are smart algorithms able to engage and communicate with us like humans? The answer lies in Question Answering systems that are built on a foundation of Machine Learning and Natural Language Processing. Let's build one here.
- Why you should NOT use MS MARCO to evaluate semantic search - Apr 2, 2020.
If we want to investigate the power and limitations of semantic vectors (pre-trained or not), we should ideally prioritize datasets that are less biased towards term-matching signals. This piece shows that the MS MARCO dataset is more biased towards those signals than we expected and that the same issues are likely present in many other datasets due to similar data collection designs.
- Alternative Data, Text Analytics, and Sentiment Analysis in Trading and Investing - Mar 25, 2020.
Different types of data beyond your typical dollars and cents have been used in the finance industry for many years. By leveraging machine learning, sentiment data is expected to play an increasingly dominant role in the investment industry, and this article highlights some special challenges of its use in trading models.
- How To Build Your Own Feedback Analysis Solution - Mar 12, 2020.
Automating the analysis of customer feedback will sound like a great idea after reading a couple hundred reviews. Building an NLP solution to provide in-depth analysis of what your customers are thinking is a serious undertaking, and this guide helps you scope out the entire project.
- Tokenization and Text Data Preparation with TensorFlow & Keras - Mar 6, 2020.
This article will look at tokenizing and further preparing text data for feeding into a neural network using TensorFlow and Keras preprocessing tools.
- Generating English Pronoun Questions Using Neural Coreference Resolution - Jan 29, 2020.
This post will introduce a practical method for generating English pronoun questions from any story or article. Learn how to take an additional step toward computationally understanding language.
- 10 Python String Processing Tips & Tricks - Jan 20, 2020.
Pursuing a text analytics path but don't know where to start? Try this string processing primer to first gain an understanding of using Python to manipulate and process strings at a basic level.
- Automatic Text Summarization in a Nutshell - Dec 18, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.
- Markov Chains: How to Train Text Generation to Write Like George R. R. Martin - Nov 29, 2019.
Read this article on training Markov chains to generate George R. R. Martin style text.
- Getting Started with Automated Text Summarization - Nov 28, 2019.
This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
- Text Encoding: A Review - Nov 22, 2019.
We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.
- Lemma, Lemma, Red Pyjama: Or, doing words with AI - Oct 10, 2019.
If we want a machine learning model to be able to generalize these forms together, we need to map them to a shared representation. But when are two different words the same for our purposes? It depends.
- An Overview of Topics Extraction in Python with Latent Dirichlet Allocation - Sep 4, 2019.
A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy.
- When Too Likely Human Means Not Human: Detecting Automatically Generated Text - May 23, 2019.
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
- KDnuggets™ News 19:n19, May 15: Data Scientist – Best Job of the Year!; How (not) to use Machine Learning for time series forecasting - May 15, 2019.
"Please, explain." Interpretability of machine learning models; How to fix an Unbalanced Dataset; Data Science Poem; Customer Churn Prediction Using Machine Learning; A Complete Exploratory Data Analysis and Visualization for Text
- Intuit: Sr Manager Data and Analytics – Speech and Text Insights [Mountain View, CA] - May 10, 2019.
Seeking a Sr Manager Data and Analytics for Speech and Text Insights, to oversee the design, development, deployment and management of batch and real-time fraud and credit risk models for both onboarding and monitoring purposes.
- A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights - May 9, 2019.
Visually representing the content of a text document is one of the most important tasks in the field of text mining as a Data Scientist or NLP specialist. However, there are some gaps between visualizing unstructured (text) data and structured data.
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- Unlock and Extract Data from Your PDF Documents - Jan 31, 2019.
Automate and accurately extract data and information locked within PDF documents using PDF Alchemist, increasing productivity and data throughput while reducing costs.
- How to solve 90% of NLP problems: a step-by-step guide - Jan 14, 2019.
Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.
- Comparison of the Text Distance Metrics - Jan 7, 2019.
There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.
- Approaches to Text Summarization: An Overview - Jan 3, 2019.
This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.
- Machine Learning for Text Classification Using SpaCy in Python - Sep 11, 2018.
In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.
- Topic Modeling with LSA, PLSA, LDA & lda2Vec - Aug 30, 2018.
This article is a comprehensive overview of Topic Modeling and its associated techniques.
- Word Vectors in Natural Language Processing: Global Vectors (GloVe) - Aug 29, 2018.
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.
- Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP - Aug 24, 2018.
Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!
- Comparison of the Most Useful Text Processing APIs - Aug 23, 2018.
There is a need to compare different APIs to understand key pros and cons they have and when it is better to use one API instead of the other. Let us proceed with the comparison.
- Named Entity Recognition: A Practitioner’s Guide to NLP - Aug 17, 2018.
Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
- Affordable online news archives for academic research - Aug 10, 2018.
Many researchers need access to multi-year historical repositories of online news articles. We identified three companies that make such access affordable, and spoke with their CEOs.
- Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP - Aug 10, 2018.
Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.
- Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP - Aug 3, 2018.
I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.
- WTF is TF-IDF? - Aug 2, 2018.
Relevant words are not necessarily the most frequent words since stopwords like “the”, “of” or “a” tend to occur very often in many documents.
- Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP - Jul 26, 2018.
Proven and tested hands-on strategies to tackle NLP tasks.
- KDnuggets™ News 18:n24, Jun 20: Data Lakes – The evolution of data processing; Text Generation with RNNs in 4 Lines of Code - Jun 20, 2018.
How to spot a beginner Data Scientist; How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning; Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray; Cartoon: FIFA World Cup Football and Machine Learning
- Getting Started with spaCy for Natural Language Processing - May 2, 2018.
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.
- 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
- Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
- Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
- Text Data Preprocessing: A Walkthrough in Python - Mar 26, 2018.
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools.
- Text Processing in R - Mar 9, 2018.
There are good reasons to want to use R for text processing, namely that we can do it, and that we can fit it in with the rest of our analyses. Furthermore, there is a lot of very active development going on in the R text analysis community right now.
- The Fastest Way to Benefit from Text Analytics, Dec 20 Webinar - Dec 13, 2017.
MeaningCloud Vertical Packs: Voice of the Customer (VoC) and Voice of the Employee (VoE), offer the fastest way to benefit from text analytics.
- A General Approach to Preprocessing Text Data - Dec 1, 2017.
Recently we had a look at a framework for textual data science tasks in their totality. Now we focus on putting together a generalized approach to attacking text data preprocessing, regardless of the specific textual data science task you have in mind.
- A Framework for Approaching Textual Data Science Tasks - Nov 22, 2017.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
- KDnuggets™ News 17:n26, Jul 12: Applying Deep Learning to Real-world Problems; New Poll: Will society be better from increased automation, AI? - Jul 12, 2017.
Also Text Clustering: Get quick insights from Unstructured Data; Using the TensorFlow API: An Introductory Tutorial Series; Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2
- Text Clustering : Quick insights from Unstructured Data, part 2 - Jul 4, 2017.
We will build this in a modular way and also focus on exposing the functionalities as an API so that it can serve as a plug and play model without any disruptions to the existing systems.
- Text Clustering: Get quick insights from Unstructured Data - Jun 28, 2017.
Grouping and clustering free text is an important advance towards making good use of it. We present an algorithm for unsupervised text clustering approach that enables business to programmatically bin this data.
- Text Mining 101: Mining Information From A Resume - May 24, 2017.
We show a framework for mining relevant entities from a text resume, and how to separation parsing logic from entity specification.
- Level-up your analytics with text mining, Apr 27 Webinar - Apr 17, 2017.
MeaningCloud, leader in SaaS semantic analytics, has new RapidMiner extension that offers very powerful and flexible text analytics and the ability to extract the meaning of any unstructured text. Learn more in April 27 webinar.
- WordStat for Stata Now on Macs - Apr 14, 2017.
WordStat is a flexible and easy-to-use text analysis software – whether you need text mining tools for fast extraction of themes and trends, or careful and precise measurement with state-of-the-art quantitative content analysis tools. Now works on Macs.
- Machine Learning Finds “Fake News” with 88% Accuracy - Apr 12, 2017.
In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.
- Text Analytics: A Primer - Mar 14, 2017.
Marketing scientist Kevin Gray asks Professor Bing Liu to give us a quick snapshot of text analytics in this informative interview.
- Making sense of text analytics - Feb 9, 2017.
Gain a deep understanding of tools and techniques of text analytics and sentiment mining from statistical and NLP perspectives. Next course is in NYC, April 27-28.
- What Americans Really Think About Trump’s Immigration Ban and Why - Feb 9, 2017.
What do Americans really think of the President's immigration ban? Text analysis of what people say in their own words reveals more than multiple-choice surveys.
- Provalis Research Releases an Enhanced Qualitative Data Analysis Freeware - Feb 3, 2017.
Upgraded version of the qualitative analysis freeware QDA Miner Lite now includes a document overview, tree-grid display, image rotation and resizing, importing from PowerPoint and more.
- Text Mining Amazon Mobile Phone Reviews: Interesting Insights - Jan 10, 2017.
We analyzed more than 400 thousand reviews of unlocked mobile phones sold on Amazon.com to find out insights with respect to reviews, ratings, price and their relationships.
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects - Jan 9, 2017.
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
- What is emotion analytics and why is it important? - Oct 19, 2016.
In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.
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- The Trump Phenomenon: A Twitter Based Recount - Sep 26, 2016.
This analysis uses Twitter data to perform a sentiment analysis to help determine how people truly feel about Trump. We found that while his fans have supported him throughout his entire campaign, more and more Twitter users have started to grow tired of Trump’s attitude.
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- What Data Scientists Can Learn From Qualitative Research - Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
- Explore your unstructured text data - Jul 13, 2016.
Learn examples of success with text exploration, what engineers and scientists can (and should) do with text data, and the consequences of collecting data and doing nothing with it.
- An Inside Update on Natural Language Processing - Jun 28, 2016.
This article is an interview with computational linguist Jason Baldridge. It's a good read for data scientists, researchers, software developers, and professionals working in media, consumer insights, and market intelligence. It's for anyone who's interested in, or needs to know about, natural language processing (NLP).
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- HPE Haven OnDemand Text Extraction API Cheat Sheet for Developers - Jun 21, 2016.
HPE Haven OnDemand provides a native API based on cURL calls, as well as numerous language-specific APIs, providing maximum flexibility for developers. This cheat sheet will cover the native and Python text extraction APIs.
- Early bird deadline approaching – Chicago’s vortex of 4 analytics events - Apr 15, 2016.
Chicago is whirling together four analytics events, June 20-23, 2016. Don't let the bird rates fly away - register by May 6 for best rates and getting extra saving with code KDN150.
- Ravel: Senior Data Scientist - Apr 6, 2016.
Seeking a Lead or Senior Data Scientist to join our rapidly growing team; A senior member of the Data Science team, you will design systems to tackle tough problems in the legal domain and expand our award-winning products.
- Spring dream offer for PAW Business, PAW Manufacturing, TAW in Chicago – Reg. by Apr 2 - Mar 29, 2016.
Join analytics community in Chicago at PAW Business, Manufacturing, or Text Analytics events this June, and save with code SPRINGDREAM if you register by April 2.
- [Super Early Bird Reminder] Quintuple Analytics Events, Chicago - Mar 7, 2016.
The super early bird deadline ends March 11 for the gathering of 5 analytics events, including PAW Business, PAW Manufacturing, and Text Analytics World, Chicago June 20-23, 2016. Register with code KDN150 and save.
- Text analytics: what makes your phone smarter than survey analysis - Feb 25, 2016.
Text analytics and word prediction has been broadly used for smart phones. Here, we present “next word predictor” (NWP) as an enhancement for existing survey analysis tool kits and use-cases for the same.
- Elementary, My Dear Watson! An Introduction to Text Analytics via Sherlock Holmes - Feb 12, 2016.
Want to learn about the field of text mining, go on an adventure with Sherlock & Watson. Here you will find what are different sub-domains of text mining along with a practical example.
- RapidMiner Webinar: Extracting Insight from Superbowl Sentiments, Feb 16 - Feb 4, 2016.
The webinar explores the power of social content by analyzing data captured from tweets about Super Bowl 50 ads to determine sentiments and predict potential trends in brand adoption.
- Webinar: The Role of Text Mining at Boehringer Ingelheim Pharmaceuticals, Feb 23 - Feb 2, 2016.
Learn how text mining enables life science researchers to quickly analyze massive amounts of literature, conference abstracts, patents and clinical data to help inform and guide R&D.
- Everything You Need to Know about Natural Language Processing - Dec 21, 2015.
Natural language processing (NLP) helps computers understand human speech and language. We define the key NLP concepts and explain how it fits in the bigger picture of Artificial Intelligence.
- WordStat 7.1: Geospatial Intelligence Meets Text Analytics - Dec 16, 2015.
Provalis Research text analysis software now features a geospatial intelligence module which transforms unstructured text data into interactive maps.
- Augmented Intelligence – An Elegant Approach to Optimize Your Decision Making Process - Dec 14, 2015.
Automation is a rising trend in the recent technology boom, but it can impose a level or risk. Harmonizing both human decision and powerful computing abilities will be key, especially for enterprises looking to unlock insights through analytics.
- An Inside View of Language Technologies at Google - Oct 29, 2015.
Learn about language technologies at Google, including projects, technologies, and philosophy, from an interview with a Googler.
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- Upcoming Webcasts on Analytics, Big Data, Data Science – Oct 27 and beyond - Oct 26, 2015.
Amazon QuickSight, Textual Healing, What's new in Statistica 13, Real-time Hadoop and IoT, Ad Hoc Visual Discovery, and more.
- Xerox Research Centre India: Research Scientist/Engineer: Text and Graph Analytics - Oct 5, 2015.
The team is working on challenging research problems with real life relevance pertaining to different business verticals such as Customer Care, Social Media, Healthcare, Transportation and Education.
- Exclusive Interview: Big Data and Data Science at UN - Sep 16, 2015.
We interview the UN Chief Information Technology Officer about how Big Data and Data Science can help solve world's problem. Check Unite Ideas crowdsourcing platform for data analytics challenges where you can help.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Sep 8 and beyond - Sep 7, 2015.
The Future of Data Science, Ensuring Business Value from Analytics, Apache Ignite, Text Analytics, Best Practices of Data Science, Forecasting With Predictive Analytics, and more.
- 11 things to know about Sentiment Analysis - Aug 13, 2015.
Seth Grimes, a text analytics guru, shares 11 key observations on what works, what is past, what is coming, and what to keep in mind while doing sentiment analysis.
- Book: Practical Text Analytics - Aug 11, 2015.
New publication provides guidance on the application of text analytics for marketing professionals who must interpret results and apply them in their campaigns.
- SEC: Operations Research Analyst (Text Analytics) - Aug 4, 2015.
Support big data projects and use cutting-edge analytical tools and methods to inform policy-making and risk assessment activities for U.S. Securities and Exchange Commission.
- Sentiment Analysis Symposium, New York City, July 15-16 - Jun 30, 2015.
2015 Sentiment Analysis Symposium is a dream event for data scientists and analysts working in text, social, and emotion analytics. Get discount with code KDNUGGETS.
- Discover the WHY behind your Customer Scores, June 10 webinar with Seth Grimes - May 28, 2015.
Text Analytics thought leader Seth Grimes and MeaningCloud present a special webinar on ensuring you are getting the most from your customer feedback.
- Boeing: Advanced Information Technologist Text Analytics - Apr 28, 2015.
Join growing Boeing central R&D, Analytics and Simulation organization, and apply advanced data analysis and text analysis algorithms to help build something better for yourself, for our customers and for the world.
- Text Analytics, Text Mining Courses on Statistics.com - Apr 28, 2015.
Text analytics or text mining is the natural extension and essential part of predictive analytics and Data Science - learn key skills with Statistics.com online courses.
- Algorithmia Tested: Human vs Automated Tag Generation - Apr 21, 2015.
Algorithmia, the marketplace for algorithms, can be a platform for hosting APIs to do a plethora of text analytics and information retrieval tasks. Automatic post tagging is done in this case study to demonstrate the effectiveness and ease-of-use of the platform.
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- Math of Ideas: A Word is Worth a Thousand Vectors - Apr 16, 2015.
Word vectors give us a simple and flexible platform for understanding text, there are a few diverse examples that should help build your confidence in developing and deploying NLP systems and what problems they can solve.
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- The State of the Text Analytics Industry – 2015 White Paper - Apr 16, 2015.
This free whitepaper gives the perspectives of industry experts from leading firms on the culture, benefits, challenges, data and technology currently impacting the text analytics market today.
- Text By the Bay conference, San Francisco, Apr 24-25 - Apr 2, 2015.
The inaugural Text By the Bay conference has an amazing program, with speakers from top universities, Big text data powerhouses, Growing global players, Startups, Text/NLP tech providers, and more. KDnuggets discount.
- KDnuggets™ News 15:n10, Apr 1: Computing platforms for data science; Free Data Mining Books; Python vs R - Apr 1, 2015.
Computing Platforms for Analytics, Data Mining, Data Science; More Free Data Mining, Data Science Books and Resources; Text Analytics 2015 - Technology and Market Overview; The Grammar of Data Science: Python vs R.
- Text Analytics 2015 – Technology and Market Overview - Mar 30, 2015.
A leading analyst and expert on text analytics gives an overview of the past year and looks ahead on text analytics technology and market developments.
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- PAW San Francisco: The Pentagon of Data Analytics Events, Mar 29 – Apr 2 - Mar 10, 2015.
In San Francisco this month, you'll find five data analytics events - join your peers by for case studies, profound keynotes, and build strong connections with data analytics professionals. Special KDnuggets discount.
- Prismatic Interest Graph [API]: Organize and Recommend Content - Feb 20, 2015.
Prismatic Interest Graph API provides a set of tools for automatically analyzing unstructured text and annotating it with a variety of tags that are useful for organizing and recommending content.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Feb 17 and beyond - Feb 16, 2015.
Pivotal Update, Moving Targets, Secure Because Math, Data Driven: Creating a Data Culture, Maximize the effectiveness of your text analytics initiatives, Text Mining and Knowledge Graphs in the Cloud.
- Webinar: Drive effective text analytics initiatives, Feb 19 - Feb 16, 2015.
On February 19, join Meta Brown (author of Data Mining for Dummies), Howard Lyeth (Senior Analyst at L.L. Bean), Steven Scarr (CEO of eContext), and Ramkumar Ravichandran (Director of Analytics at Visa) to learn how to maximize the effectiveness of your text analytics.
- Statistics.com Online Data Science Courses and Certificates - Feb 12, 2015.
Accelerate your career and upgrade your skills With Statistics.com training provided by top experts who will answer your questions on a daily basis. Work on practical exercises with real problems, real data and multiple software tools.
- Top /r/MachineLearning posts, Feb 1-7: Music recognition, Text Understanding from scratch - Feb 9, 2015.
Shazam music recognition techniques, deep learning for text understanding, neuroscience history, Neural Turing Machines using Torch, and genetic algorithms are the top topics on Reddit last week.
- Top stories for Jan 25-31: (Deep Learning Deep Flaws) Deep Flaws; Text Analysis 101: Document Classification - Feb 1, 2015.
(Deep Learning Deep Flaws) Deep Flaws; Text Analysis 101: Document Classification; Interview: Anthony Bak, Ayasdi on Managing Data Complexity through Topology.
- PAW: Predictive Analytics World and Text Analytics World, Spring 2015, San Francisco - Jan 27, 2015.
Come to the leading, world-renowned events in predictive analytics and text analytics - coming to San Francisco this spring - and build your skillset and knowledge. Special KDnuggets discount.
- Text Analysis 101: Document Classification - Jan 24, 2015.
Document classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort.
- KDnuggets™ News 15:n01, Jan 7: Clever methods of overfitting; 5 Analytics Rules to cut thru the Hype - Jan 7, 2015.
11 Clever Methods of Overfitting and how to avoid them, Data Mining and Text Analytics of World Cup 2014, iMath Cloud Data Science Platform beta, Platfora CEO on Insightful Analytics for Big Data, and more analytics, big data, data science, and data mining stories.
- PAW: 5 Co-Located Analytics Events in San Francisco, March 29 – Apr 2, 2015 - Jan 7, 2015.
March 29 - Apr 2, 2015 San Francisco hosts PAW for Business, eMetrics Summit, Predictive Analytics World for Workforce, Text Analytics World and the PA Times Executive Breakfast.
- Data Mining and Text Analytics of World Cup 2014 - Jan 3, 2015.
Explore how text analysis techniques to dig into some of the data in a series of blog posts, focusing on matches and their events, tweets languages, tweets volumes for different teams and sentiment analysis.
- Boeing: Advanced Technologist in Text Analytics, level 2/3 - Dec 10, 2014.
Our organization partners with Boeing business units to advance towards widespread application of modeling, simulation, and analytics across the lifecycle of our aerospace and defense products.
- PAW: Unwrap Holiday Cheer for Big Data - Dec 9, 2014.
Get super early bird pricing for PAW and TAW conferences in San Francisco, March 2015, get expert insights at PAW Business, cutting edge keynotes at PAW workforce and more.
- WordStat 7: Shifting Text Analytics Into High Gear - Dec 2, 2014.
WordStat 7 lets users get valuable and actionable insights from text faster, connect unstructured and structured information, and provides better help for the creation and validation of accurate text-categorization dictionaries.
- Top KDnuggets tweets, Nov 24-30: How Word Meanings Change; Data wrangling with R: Things I wish I’d been told - Dec 1, 2014.
Linguistic Mapping Reveals How Word Meanings Sometimes Change Overnight; Practical #DataScience Cookbook, helps data practitioner; Facebook #AI team hires Vladimir Vapnik, father of #SVM; Starting data analysis/wrangling with R: Things I wish I'd been told.
- SAS: Text Analytics Fellowship - Nov 22, 2014.
Open to PhD candidates in CS, related fields in US, this fellowship offers the opportunity to work closely with Text Analytics professionals to develop software. Apply by Jan 23.
- PAW San Francisco – Four Co-located Analytics Events, March 2015 - Nov 4, 2014.
Attend the leading, world-renown events in predictive analytics and text analytics in San Francisco, Spring 2015, and build your skillset and knowledge. Register with Super Early Bird Pricing and KDnuggets discount.