Search Results for "tiny-core-plus"

OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention…), health care (early diagnosis, microarray analysis…) and engineering (performance optimization, predictive maitenance…). OpenNN does not deal with computer vision or natural language processing. The main advantage of OpenNN is its...

Natural intelligence is the utilization of naturally occurring logic. This naturally occurring logic provides concrete clues for organizing natural objects, like: - Grouping objects that belong together, - Separating objects that don't belong together, - Archiving objects that have become less important. Natural language and spatial information are sources of natural intelligence: - Natural language is providing concrete logic for organizing knowledge objects, - Spatial...

OpenPR stands for Open Pattern Recognition project and is intended to be an open source library for algorithms of image processing, computer vision, natural language processing, pattern recognition, machine learning and the related fields.

...A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. In general, emphasis is given in improving the efficiency of the algorithms in shared-memory models via java threads, since multi-core machines are so wide-spread today.

This repository contains lecture notes, slides, assignments, and code for a university-level Natural Language Processing course. It spans core NLP topics such as language modeling, sequence tagging, parsing, semantics, and discourse, alongside modern machine learning methods used to solve them. Students work through programming exercises and problem sets that build intuition for both classical algorithms (like HMMs and CRFs) and neural approaches (like word embeddings and sequence models). ...

...Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.

Web application to make user-friendly requests on large XML database. Tools to XML-ize large bodies of semi-formal texts (like floras). Computer-assisted specimen identification. Uses natural language processing, 2D/3D images analysis and generation.

Maximum entropy is a powerful method for constructing statistical models of classification tasks, such as part of speech tagging in Natural Language Processing. Several example applications using maxent can be found in the OpenNLP Tools Library.

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.

MeCab is a fast and customizable Japanese morphological analyzer. MeCab is designed for generic purpose and applied to variety of NLP tasks, such as Kana-Kanji conversion. MeCab provides parameter estimation functionalities based on CRFs and HMM

QuickAI (pronounced, "quickeye", or just "Quick" for short) is a return to the fundamental goals of creating an artificial intelligence. The priorities are to implement core models of knowledge and knowing, a reasoning engine, and a simple interface.

The program provides Java interface (to C++ Lemmatizer via XML-RPC) in order to perform lemmatizing in Russian, English, and German (lemma is the canonical form of a lexeme in Natural Language Processing). RussianPOSTagger could work as a module of GATE.

Grok is a library of natural language processing components, including support for parsing with categorial grammars and various preprocessing tasks such as part-of-speech tagging, sentence detection, and tokenization.