background preloader

Basically, it's Data Science

Facebook Twitter

Eclipse. Hide For the Mars release, we are introducing a new Eclipse installer.


This is a new and more efficient way to install Eclipse. It is a proper installer, so no more zip files, with a self extracting download that will lead you through the installation experience. For those not into installers, we still have the packages and zip files available on our download pages. 1. Eclipse is hosted on many mirrors around the world. 2. For Windows users, after the Eclipse Installer executable has finished downloading it should be available in your download directory.

For Mac and Linux users, you will still need to unzip the download to create the Installer. 3. The new Eclipse Installer shows the packages available to Eclipse users. Select and click on the package you want to install. 4. Specify the folder where you want Eclipse to be installed. Select the ‘Install’ button to begin the installation. 5. Once the installation is complete you can now launch Eclipse. Git via Codecademy.

Python via Codecademy. Python via Codeschool. Python via The Great Courses Plus. R: The R Project for Statistical Computing. SAS Tutorials. Scala Programming Language. Sorting algorithms. Foundations of Statistical Natural Language Processing. This is the companion website for the following book.

Foundations of Statistical Natural Language Processing

Chris Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May 1999. Interested in buying the book? Some more information about the book and sample chapters are available. If you are here to look up something that is mentioned in the book, click on the appropriate chapter link below. A list of errata is also available. We'd be pleased to get feedback about how this book works out as a textbook, what is missing, or covered in too much detail, or what is simply wrong. Chapters Other resources Courses using the book Some courses that have used this book are the following (we're happy to be told of others!). UPenn CIS530, UPenn CIS639, Berkeley SIMS 296a-4, BYU CS479, Johns Hopkins: current (Eisner) and previous [lots of great slides by Jan Hajic!]

Canada U Toronto CSC401, Concordia, Dalhousie U CSCI4152 [lots of slides!] Europe Middle East Asia. Statistical Natural Language Processing resource list (via Stanford University) Contents Tools: Machine Translation, POS Taggers, NP chunking, Sequence models, Parsers, Semantic Parsers/SRL, NER, Coreference, Language models, Concordances, Summarization, Other Corpora: Large collections, Particular languages, Treebanks, Discourse, WSD, Literature, Acquisition Dictionaries Lexical/morphological resources Courses, Syllabi, and other Educational Resources Mailing lists.

Statistical Natural Language Processing resource list (via Stanford University)

Statistical Natural Language Processing Resources (continued)