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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

The Stanford NLP (Natural Language Processing) Group About | Getting started | Questions | Mailing lists | Download | Extensions | Models | Online demo | Release history | FAQ About Stanford NER is a Java implementation of a Named Entity Recognizer. Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. It comes with well-engineered feature extractors for Named Entity Recognition, and many options for defining feature extractors. Included with the download are good named entity recognizers for English, particularly for the 3 classes (PERSON, ORGANIZATION, LOCATION), and we also make available on this page various other models for different languages and circumstances, including models trained on just the CoNLL 2003 English training data. Stanford NER is also known as CRFClassifier. The CRF code is by Jenny Finkel. Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Getting started This NER system requires Java 1.8 or later.

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

Recherche Ebook 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

GENIA tagger home page - part-of-speech tagging, shallow parsing, and named entity recognition for biomedical text - What's New 20 Oct. 2006 A demo page is available. 6 Oct. 2006 Version 3.0: The tagger now performs named entity recognition. Overview The GENIA tagger analyzes English sentences and outputs the base forms, part-of-speech tags, chunk tags, and named entity tags. How to use the tagger You need gcc to build the tagger. 1. Apr. 16 2007 geniatagger-3.0.1.tar.gz (source package for Unix) 2. > tar xvzf geniatagger.tar.gz 3. > cd geniatagger/ > make 4. Prepare a text file containing one sentence per line, then > . The tagger outputs the base forms, part-of-speech (POS) tags, chunk tags, and named entity (NE) tags in the following tab-separated format. word1 base1 POStag1 chunktag1 NEtag1 word2 base2 POStag2 chunktag2 NEtag2 : : : : : Chunks are represented in the IOB2 format (B for BEGIN, I for INSIDE, and O for OUTSIDE). Example Part-of-Speech Tagging Performance Chunking Performance (to be evaluated) References [1] S.

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

Le Choix de Mlle Eddie - Webzine d'une Musicophage - Garanti san - index.html Viterbi algorithm The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models. The terms Viterbi path and Viterbi algorithm are also applied to related dynamic programming algorithms that discover the single most likely explanation for an observation. For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is sometimes called the Viterbi parse. Example[edit] Consider a primitive clinic in a village. Suppose a patient comes to the clinic each day and tells the doctor how she feels. The doctor knows the villager's general health condition, and what symptoms patients complain of with or without fever on average. In the running example, the forward/Viterbi algorithm is used as follows: to state . . if

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