Search Results for "entity"

...MAT supports both UI interaction and command-line interaction, and provides various levels of control over the overall annotation process. It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. MAT is for "everything else": all the tools you end up wishing you had.

...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. Version 2.x The CYMRIE pipeline is accessible via a API, standalone GUI and CLI. The CymrIE pipeline has also been adapted for Twitter.

Entity recognition and normalization software for biomedical text

...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.

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.

...It integrates information from various sources. This system has been developed by the Intelligent System Group GSI (http://www.esi.uem.es/gsi/) at UEM University. We do NER (Named Entity Recognition) based on GATE platform. The installation is simple, you can use it as a Web application. It has been tested under apache-tomcat. The original system has been successfully used to carry out active learning activities with medical students. However, it could be interesting in much more knowledge fields.

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. ...