named-entity-recognition

• The objective of this project was to perform supervised NER for Twitter data using Viterbi decoding, CRF and feature engineering.
• Designed dozens of features using lexicons, POS tags, and N-Grams
• Utilized BIO-encoded data to train the Perceptron of the Conditional Random Field (CRF) tagger
• Implemented Viterbi Decoding using Dynamic Programming for the sequence tagging part of CRF tagger.
• Used CONLL evaluation to evaluate the model and reported F1 scores.