Karakter seviyesinde etiketleme

I describe and evaluate a language-independent character-level tagger for sequence labeling problems: Named Entity Recognition (NER), Part-of-Speech (POS) tagging and Chunking. Instead of words, a sentence is represented as a sequence of charac- ters. The model consists of stacked bidirectional LSTMs which input characters and output tag probabilities for each character. These probabilities are then converted to consistent word level phrase tags using a Viterbi decoder. The model uses only labeled data and does not rely on hand-engineered features or other external resources like syntactic taggers or Gazetteers. The model is able to achieve close to state-of-the-art NER performance in seven languages, performs as well as or better than previous work in four languages for POS tagging and yields competitive results for English Chunking dataset.