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NLTK Home (Natural Language Toolkit)

NLTK Home (Natural Language Toolkit)
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/

Terminology Extraction Introduction Terminology is the sum of the terms which identify a specific topic. Extracting terminology is the process of extracting terminology from a text. 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).

12 open source tools for natural language processing Natural language processing (NLP), the technology that powers all the chatbots, voice assistants, predictive text, and other speech/text applications that permeate our lives, has evolved significantly in the last few years. There are a wide variety of open source NLP tools out there, so I decided to survey the landscape to help you plan your next voice- or text-based application. For this review, I focused on tools that use languages I'm familiar with, even though I'm not familiar with all the tools.

Welcome — Theano 0.9.0 documentation Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.dynamic C code generation – Evaluate expressions faster.extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007.

Pattern Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization. The module is free, well-document and bundled with 50+ examples and 350+ unit tests.

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. 5. Data Structures This chapter describes some things you’ve learned about already in more detail, and adds some new things as well. 5.1. More on Lists The list data type has some more methods.

100 days of web mining In this experiment, we collected Google News stories at regular 1-hour intervals between November 22, 2010, and March 8, 2011, resulting in a set of 6,405 news stories. We grouped these per day and then determined the top daily keywords using tf-idf, a measurement of a word's uniqueness or importance. For example: if the word news is mentioned every day, it is not particularly unique at any single given day. To set up the experiment we used the Pattern web mining module for Python.The basic script is simple enough: Your code will probably have some preprocessing steps to save and load the mined news updates. In the image below, important words (i.e., events) that occured across multiple days are highlighted (we took a word's document frequency as an indication).

GAJIT This page is for a mini-project I undertook when I had a spare moment or two to port the C++ based genetic algorithm library GAGS to Java. GAJIT (Genetic Algorithm Java Implementation Toolkit) is a rather contrived name for a simple set of classes that I wrote to experiment with genetic algorithms. I became interested in genetic algorithms and wanted to experiment with them.

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