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Natural Language Toolkit — NLTK 3.0 documentation

Natural Language Toolkit — NLTK 3.0 documentation
NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike. NLTK is available for Windows, Mac OS X, and Linux. Best of all, NLTK is a free, open source, community-driven project. NLTK has been called “a wonderful tool for teaching, and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

http://www.nltk.org/

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Book Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper Note-Taking in Graduate School Justin Dunnavant is a PhD student in Anthropology at the University of Florida. You can find him on Twitter @archfieldnotes or at his blog AfricanaArch. As graduate students, we are confronted with the daunting task of collecting, consolidating, and absorbing large amounts of information. Few universities actually take the time to train you on how do this, and as result taking notes and keeping track of them can quickly become overwhelming. There are dozens of books that discuss the importance of note taking and offer different methods and strategies to become an effective note-taker. Rather than covering the full gamut of note-taking tactics, I’m just going to outline the different ways I have adapted to taking notes over the years.

Dependency Parsing: Recent Advances (Artificial Intelligence) Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for machine learning systems for a wide variety of applications from Parsing to Machine Translation (Quirk et al., 2005). Dependency representation is preferred for many languages because linguistic and semantic information is easier to retrieve from the more direct dependency representation. Dependencies are relations that are defined on words or smaller units where the sentences are divided into its elements called heads and their arguments, e.g. verbs and objects. Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation or syntactic structure. Parsing is usually considered as the first step of Natural Language Processing (NLP).

Python Runtime Environment - Google App Engine - Google Code Welcome to Google App Engine for Python! With App Engine, you can build web applications using the Python programming language, and take advantage of the many libraries, tools and frameworks for Python that professional developers use to build world-class web applications. Your Python application runs on Google's scalable infrastructure, and uses large-scale persistent storage and services. 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? The top integrated development environments for Python Python is everywhere. These days, it seems it powers everything from major websites to desktop utilities to enterprise software. Python has been used to write all, or parts of, popular software projects like dnf/yum, OpenStack, OpenShot, Blender, Calibre, and even the original BitTorrent client. It also happens to be one of my favorite programming languages. Personally, Python has been my go-to language through the years for everything from class projects in college to tiny scripts to help me automate recurring tasks.

Jisc digital capability codesign challenge blog It’s been a hectic, interesting and sometimes information-overloaded couple of months on the Digital Capabilities frameworks project. Lou McGill and I have reviewed over 60 existing frameworks for describing the digital capabilities of staff, from professional frameworks which might only touch on digital practice, to frameworks from the IT industry, digital media, and business innovation. We’ve looked at a host of publications and web sites. And I’ve carried out interviews with dozens of people who are doing work in this area, whether they are based in professional bodies or in universities and colleges, or in industry and the professions outside of education. One of the surprising things to emerge from this process, as Sarah Davies has outlined, is the affection people feel for some of the work Jisc has already done in this area.

Ralph Debusmann - Extensible Dependency Grammar (XDG) Extensible Dependency Grammar (XDG) is a general framework for dependency grammar, with multiple levels of linguistic representations called dimensions, e.g. grammatical function, word order, predicate-argument structure, scope structure, information structure and prosodic structure. It is articulated around a graph description language for multi-dimensional attributed labeled graphs. An XDG grammar is a constraint that describes the valid linguistic signs as n-dimensional attributed labeled graphs, i.e. n-tuples of graphs sharing the same set of attributed nodes, but having different sets of labeled edges. Design Patterns in Python Alex Martelli is a leading light of the Python programming language community. He is a leader in the development of the language, author of Python in a Nutshell and has written extensively on Python in other books and articles. Last week he spoke to the SDForum Software Architecture and Modeling SIG on "Design Patterns in Python". Alex has posted the presentation slides here. Design patterns are a useful concept in programming.

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