Search Results for "machine learning python" - Page 2

Redundancy due to cut-paste operations in text creates bias in machine learning for NLP. This module takes a directory and produces a subset of the files in that directory (in a list) with an upper bound on similarity between two files.

CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. ...

Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%.

An agent-based situated language learning simulation that focuses on lexical learning and grounding, featuring a unigram syntax structure and a CFG-based semantic grammar. Created as a MSc thesis project, using python.

This is a PHP-5 library for language detection.

CRF decoder is the simplified version of CRF++, only for decoding the sequential data. It removes the training component and its correspondent codes from CRF++, which makes CRF decoder more reabable and understandable for freshman.

Sanchay is a collection of tools and APIs for language researchers. It has some implementations of NLP algorithms, some flexible APIs, several user friendly annotation interfaces and Sanchay Query Language for language resources.

Maui is a multi-purpose automatic topic indexing algorithm. Given a document, Maui automatically identifies its topics. Depending on the task topics are tags, keywords, keyphrases, vocabulary terms, descriptors or Wikipedia titles.

A unique natural-language processing software, called Discovery, created on the CA Visual Objects/Vulcan.NET environment, which also has potential for effective "shallow approach" machine translation.

Moved to Github: http://github.com/tremby/py-translate

Using Machine Learning, AraRooter finds the three-lettered root of any Arabic lemma with around 84% accuracy.

Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.