Search Results for "entity"

UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.

AFNER is a C++ named entity recognition system that uses machine learning techniques. It is customisable to various domains. It also allows for multiple and overlapping named entity labels.

BANNER is a named entity recognition system intended primarily for biomedical text. It uses conditional random fields as the primary recognition engine and includes a wide survey of the best techniques described in recent literature.

The BioNLP UIMA Component Repository provides UIMA wrappers for novel and well-known 3rd-party NLP tools used in biomedical text prosessing, such as tokenizers, parsers, named entity taggers, and tools for evaluation.

A PHP script with maps of the World, China, Canada, USA, India, Africa and Europe that allows the user to select the countries, provinces or states by clicking on them or selecting a checkboxes. Selection causes the entity to turn a default color which contrasts with defaults colors of all bordering countries. Thus a patchwork is made to show the history of countries, states or provinces traveled. Added features allow users to download their maps, to blow them up to posters of any size printed with media of any standard size such as A4 or letter size paper; and to get permanent URLs of their made maps in order to come back and further annotate them at a later date.

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. 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. ...

Elysia is an attempt to study the complex process behind decision making in neural systems. An evolved entity approach is used to converge on an 'intelligence' solution through genetic algorithms as well as adaptable models of distributed 'thinking'.

TBLTools is a set of GATE processing resources that implements the Fast Transformation Based Learning Algorithm. You can train it to learn rules for NLP tasks such as Named Entity Recognition and Shallow parsing.

T-Rex (Trainable Relation Extraction) is a highly configurable machine learning-based Information Extraction from Text framework, which includes tools for document classification, entity extraction and relation extraction.

phpTaxonomy is aimed at providing a framework for presenting web-based lists of species and their organisational structure along with text descriptions and images aiding in the identification of these species. Inluding online administration module.