A recent approach for modelling this type of data is

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Unformatted text preview: they do not take into account the predicted labels of the surrounding words. This can be done using probabilistic models of sequences of labels and features. Frequently used is the hidden Markov model (HMM), which is based on the conditional distributions of current labels L( j) given the previous label L( j−1) and the distribution of the 46 LDV-FORUM A Brief Survey of Text Mining current word t( j) given the current and the previous labels L( j) , L( j−1) . L ( j ) ∼ p ( L ( j ) | L ( j −1) ) t ( j ) ∼ p ( t ( j ) | L ( j ) , L ( j −1) ) (18) A training set of words and their correct labels is required. For the observed words the algorithm takes into account all possible sequences of labels and computes their probabilities. An efficient learning method that exploits the sequential structure is the Viterbi algorithm (Rabiner 1989). Hidden Markov models were successfully used for named entity extraction, e.g. in the Identifinder system (Bikel et al. 1999). 3.3.3 Conditional Random Fields Hidden Markov models require the conditional independence of features of different words given the labels. This is quite restrictive as we would like to include features which correspond to several words simultaneously. A recent approach f...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.

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