3 Matching Annotations
  1. Apr 2018
    1. an be read as a transliteration.

      how do you do this???

    2. 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

      NER MEMM Features

    3. 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.