AnSWR | Software | Statistics and Surveillance | Topics | CDC HIV/AIDS CDC EZ-Text CDC EZ-Text is a software program developed to assist researchers create, manage, and analyze semi-structured qualitative databases. Researchers can design a series of data entry templates tailored to their questionnaire. Download copies of the EZ-Text software and user documentation free of charge. If you have further questions or problems, please send an email message to: eztext@cdc.gov Epi Info Epi Info™ is a public domain suite of interoperable software tools designed for the global community of public health practitioners and researchers.
Faq/A Hackable Access Index | Internet Monitor July 2014 | Robert Faris At the onset of this project, we set out to create an index for countries around the world to complement the research, data, and reports that we produce. Our original intention was that this index would include data organized around three themes: access, openness/content restrictions, and use of the Internet. With considerable hard-earned humility and insights gained by this experience, we start by offering a user-configurable index that captures Internet access from several angles. The access index that we offer is constructed in a two-step process. The adoption sub-index is constructed using four measures, three somewhat different measures of wireline connectivity (none of which is inherently better than the other) and mobile broadband subscription rates: percentage of households with Internet access, percentage of individuals using the Internet, wired Internet subscription rate, and active mobile broadband subscription rate.
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
Grounded Theory Institute - The Grounded Theory Methodology of Barney G. Glaser, Ph.D - Home Using Crowdsourcing for Complex Data Problems: It Can & Should Be Done! | Spare5 Crowdsourcing companies came onto the scene with the goal of helping businesses solve simple problems with massive scale. A decade ago, we couldn’t rely on a computer to, for example, identify duplicate product pages or tell the difference between fruit and faces in an image, so crowdsourcing platforms became a smart way to disperse these easy-but-not-so-easy-a-computer-can-do-it data tasks to a large group of humans — a crowd — to tackle. Of course, today, smart machines are able to handle those tasks — faster and at better scale. But if this old model — anonymous members of a crowd completing rote tasks — is what you think of when you think of crowdsourcing, you’ve missed a major development: intelligent crowdsourcing. The Problem with Anonymity Crowds are largely anonymous. We can look to academia for best practices on how to apply algorithms, workflows, and game theory to improve results, but the problem remains: lack of clear, personal identity means lack of accountability.
LingPipe Home 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.
Open Culture 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
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. 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. Language modelling is typically the training ground of choice when applying RNNs and has succeeded at capturing the imagination, with many getting their first exposure via Andrej’s blog post. and was applied to road-following and pneumonia prediction (Caruana, 1998). . . . . .
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). Our conceptual understanding of how best to represent words and sentences in a way that best captures underlying meanings and relationships is rapidly evolving. Moreover, the NLP community has been putting forward incredibly powerful components that you can freely download and use in your own models and pipelines (It’s been referred to as NLP’s ImageNet moment, referencing how years ago similar developments accelerated the development of machine learning in Computer Vision tasks). (ULM-FiT has nothing to do with Cookie Monster. Example: Sentence Classification