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How Can We Help You? Get the latest version: Free and Paid Licenses/DownloadsLearn how to use LingPipe: Tutorials Get expert help using LingPipe: Services Join us on Facebook What is LingPipe? LingPipe is tool kit for processing text using computational linguistics. LingPipe is used to do tasks like: Find the names of people, organizations or locations in newsAutomatically classify Twitter search results into categoriesSuggest correct spellings of queries To get a better idea of the range of possible LingPipe uses, visit our tutorials and sandbox. Architecture LingPipe's architecture is designed to be efficient, scalable, reusable, and robust. Latest Release: LingPipe 4.1.2 Intermediate Release The latest release of LingPipe is LingPipe 4.1.2, which patches some bugs and documentation. Migration from LingPipe 3 to LingPipe 4 LingPipe 4.1.2 is not backward compatible with LingPipe 3.9.3. Programs that compile in LingPipe 3.9.3 without deprecation warnings should compile and run in Lingpipe 4.1.2. Related:  TAL

Цикл надочікувань 2012 — Гартнер визначила рівень зрілості понад 1900 нових технологій | Асоціація підприємств інформаційних технологій України Gartner's 2012 Hype Cycle for Emerging Technologies Identifies "Tipping Point" Technologies That Will Unlock Long-Awaited Technology Scenarios 2012 Hype Cycle Special Report Evaluates the Maturity of More Than 1,900 Technologies Gartner to Host Complimentary Webinar "Emerging Technologies Hype Cycle: What's Hot for 2012 to 2013," Today at 10 a.m. EDT and 1 p.m. STAMFORD, Conn., 16 August, 2012 — Big data, 3D printing, activity streams, Internet TV, Near Field Communication (NFC) payment, cloud computing and media tablets are some of the fastest-moving technologies identified in Gartner Inc.' Figure 1. Gartner analysts said that these technologies have moved noticeably along the Hype Cycle since 2011, while consumerisation is now expected to reach the Plateau of Productivity in two to five years, down from five to 10 years in 2011. "The theme of this year's Hype Cycle is the concept of 'tipping points.' Any Channel, Any Device, Anywhere — Bring Your Own Everything Smarter Things

Stanford Natural Language Processing (NLP) Stanford CoreNLP (Natural Language Processing) est un logiciel d’analyse de texte qui offre de nombreuses fonctionnalités telles que retrouver la racine des mots, étiqueter les mots selon leur type (nom, verbe, personne, localisation, etc.) ou bien trouver des dépendances/relations entre les (groupes de) mots. Dans cet article nous allons dans un premier temps, voir comment leurs outils fonctionnent, puis nous allons utiliser l’API de Stanford (interface qui permet à un développeur d’utiliser un ou plusieurs bouts de code écrit par Stanford) pour pouvoir utiliser leurs différents outils dans un programme Java. Enfin, nous verrons comment créer son propre NER (Named Entity Recognition = outils de reconnaissance d’entité nommée) pour pouvoir détecter des termes. Nous allons nous rendre sur leur site web pour découvrir leurs outils et les tester. Leur premier outil, le « Part of Speech Tagging » permet d’analyser tout le texte et d’annoter chaque mot. Utilisation de leur API Java maxLeft=1

De Marketing agenda in de internettijd.Deel 6. De strainer De Marketing agenda in de internettijd.Deel 6. Een nieuwe marketingcategorie: de strainer De BCG-matrix is na zesendertig jaar eindelijk aan revisie toe. De question mark, star, cash cow en dog hebben er een vriendje bij: de strainer. Dat kan zo niet langer met de oude BCG-matrix. Waarom was de Boston Consulting Group Matrix ook al weer zo handig? Wat is de BCG-matrix ook alweer? Begin jaren zeventig ontwikkelde de Boston Consulting Group een matrix waarin producten of bedrijfseenheden worden beoordeeld op twee kenmerken: het relatieve marktaandeel dat het product heeft verworven ten opzichte van de grootste speler in de markt en het groeipotentieel van de markt voor dat product. Een question mark, ook wel problem child of wild cat, heeft een klein marktaandeel in een groeimarkt. Wat doe je ook alweer met de BCG-matrix? Het ideale ontwikkelingspad voor een product loopt van question mark via star naar cash cow. De ‘strainer’ Die winst kan echter allang zijn verdampt. Euthanasie Wormvormig - index.html Kennisvalorisatie Meer, betere en nieuwe business. Het programma Kennisvalorisatie Rotterdam is een initiatief van het hoger onderwijs in Rotterdam, samen met de gemeente en bedrijven, om ondernemerschap en ondernemerschapsonderwijs te stimuleren en te verbeteren.Meer informatie over het gehele programma is te vinden op de site Kennisvalorisatie is het tot nut en waarde brengen van kennis voor de economie en de maatschappij. Het lectoraat Digital World neemt enkele deelprojecten van dit programma voor zijn rekening. De belangrijkste faciliteiten die we daarin (gaan) bieden zijn de Communities of Practice en het Kennisportal. Hierin komen mensen en kennis bij elkaar komen en worden concrete ondernemersvraagstukken en -problemen opgelost. Missie van het lectoraat in dit project: Learning by doing business! Het lectoraat Digital World initieert projecten rondom Online en Ondernemerschap en voert deze ook uit. Doelstellingen

List of free resources to learn Natural Language Processing - ParallelDots Natural Language Processing (NLP) is the ability of a computer system to understand human language. Natural Langauge Processing is a subset of Artificial Intelligence (AI). There are multiple resources available online which can help you develop expertise in Natural Language Processing. In this blog post, we list resources for the beginners and intermediate level learners. Natural Language Resources for Beginners A beginner can follow two methods i.e. Traditional Machine Learning Traditional machine learning algorithms are complex and often not easy to understand. Speech and Language Processing by Jurafsky and Martin is the popularly acclaimed bible for traditional Natural Language Processing. Deep Learning Deep learning is a subfield of machine learning and is far better than traditional machine learning due to the introduction of Artificial Neural Networks. CS 224n: This is the best course to get started with using Deep Learning for Natural Language Processing. Text Classification

new business A Review of the Neural History of Natural Language Processing This is the first blog post in a two-part series. The series expands on the Frontiers of Natural Language Processing session organized by Herman Kamper and me at the Deep Learning Indaba 2018. Slides of the entire session can be found here. This post will discuss major recent advances in NLP focusing on neural network-based methods. Disclaimer This post tries to condense ~15 years’ worth of work into eight milestones that are the most relevant today and thus omits many relevant and important developments. Table of contents: Language modelling is the task of predicting the next word in a text given the previous words. . This model takes as input vector representations of the n previous words, which are looked up in a table C. and long short-term memory networks (LSTMs; Graves, 2013) for language modelling. This conversely means that many of the most important recent advances in NLP reduce to a form of language modelling. . . . . . RNNs and CNNs both treat the language as a sequence. . .

Hype or worries? The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time Discussions: Hacker News (98 points, 19 comments), Reddit r/MachineLearning (164 points, 20 comments)Translations: Chinese (Simplified), French 1, French 2, Japanese, Korean, Persian, Russian, Spanish 2021 Update: I created this brief and highly accessible video intro to BERT The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural Language Processing or NLP for short). (ULM-FiT has nothing to do with Cookie Monster. One of the latest milestones in this development is the release of BERT, an event described as marking the beginning of a new era in NLP. The two steps of how BERT is developed. There are a number of concepts one needs to be aware of to properly wrap one’s head around what BERT is. Example: Sentence Classification The most straight-forward way to use BERT is to use it to classify a single piece of text. Other examples for such a use-case include: Sentiment analysis Input: Movie/Product review. Model Architecture