30-03-2017, 11:26 AM
Stanford NER is a Java implementation of a Named Entity Recognizer. Named Entity Recognition (NER) labels the sequences of words in a text that are names of things, such as person and company names or names of genes and proteins. It comes with well-designed feature extractors for Named Entity Recognition, and many options for defining feature extractors. Included in the download are recognized recognitions of English, particularly for the 3 classes (PERSON, ORGANIZATION, LOCATION), and we also make available on this page several other models for different languages and circumstances, including models trained in CoNLL 2003 Training data in English.
Stanford NER is also known as CRFClassifier. The software provides a general implementation of random-sequence (random order) linear chain (CRF) models. That is, by training your own models in tagged data, you can use this code to build sequence models for NER or any other task. Lafferty, McCallum and Pereira (2001), see Sutton and McCallum (2006) or Sutton and McCallum (2010) for more understandable presentations.).
Stanford NER is also known as CRFClassifier. The software provides a general implementation of random-sequence (random order) linear chain (CRF) models. That is, by training your own models in tagged data, you can use this code to build sequence models for NER or any other task. Lafferty, McCallum and Pereira (2001), see Sutton and McCallum (2006) or Sutton and McCallum (2010) for more understandable presentations.).