Information into knowledge.
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models . The terms Viterbi path and Viterbi algorithm are also applied to related dynamic programming algorithms that discover the single most likely explanation for an observation. For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is sometimes called the Viterbi parse .
How Can We Help You? Get the latest version: Free and Paid Licenses/Downloads Learn how to use LingPipe: Tutorials Get expert help using LingPipe: Services Join us on Facebook What is LingPipe? LingPipe is tool kit for processing text using computational linguistics. LingPipe is used to do tasks like: Find the names of people, organizations or locations in news Automatically classify Twitter search results into categories Suggest correct spellings of queries
The Stanford NLP (Natural Language Processing) Group About | Questions | Mailing lists | Download | Extensions | Models | Online demo | Release history | FAQ About Stanford NER (also known as CRFClassifier) is a Java implementation of a Named Entity Recognizer. Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. The software provides a general (arbitrary order) implementation of linear chain Conditional Random Field (CRF) sequence models, coupled with well-engineered feature extractors for Named Entity Recognition. (CRF models were pioneered by Lafferty, McCallum, and Pereira (2001) ; see Sutton and McCallum (2006) for a better introduction.)
- part-of-speech tagging, shallow parsing, and named entity recognition for biomedical text - What's New 20 Oct. 2006 GENIA tagger home page
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