Search Results for "entity"

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.

Tool support for creating FMC* diagrams [Block diagrams, Petri nets, Entity-Relationship diagrams (ERD)] in MS-Visio 2000 and newer. Features: stencils, consistency checking, Petri net simulation, exporter e.g. pdf, ... *Fundamental Modeling Concepts A stripped down version of the stencil set is available for TAM (Technical Architecture Modeling of SAP). This set uses UML notation and contains Block, Activity, Sequence, State, Class, and Component diagrams.

...The modules are written in JAVA and ‘wrapped’ for execution under the General Architecture for Text Engineering (GATE) framework. The project also includes CYMRIE an adapted version for Welsh of the GATE - ANNIE Named Entity Recognition (NER) application for a range of entities such as Persons, Organisations, Locations, and date and time expressions.

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.

...GGS grammars can be used to find and annotate sequences of words which respect certain conditions, in a given input. Its purpose is for creating NLP tools like phrase chunkers, named entity finders, pronoun co-reference solvers etc. A grammar is represented by a state machine which can be visualized, edited and applied. A grammar is organized in graphs of nodes. Nodes are used for consuming words from the input, for executing jumps to other graphs in the grammar or for creating annotations etc. GGS has a unique feature: It allows the user to write JavaScript code to be executed for nodes of the grammar. ...

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. ...

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.