an be read as a transliteration.

Features with non-binary value are later trans-formed to binary features.Structural Features: A set of binary features: wis the first word in the article; w is the first word in the sentence; w is the last word in the sen-tence. Lexicon Features: w itself is used as a feature. This set contains one feature for each distinct

The Maximum Entropy (ME) probabilistic mod-eling technique has proved to be well adapted to cases where the model includes a large number of features. As apposed to the HMM, a ME model treats each feature separately. It gives each feature a weight according to its impact on the name class prediction.