A procedure is proposed for tagging part of speech using a hybrid system that consists of a statistical based rule finder and a genetic algorithm which decides how to use those rules. This procedure will try to improve upon an already very good method of part of speech tagging.

We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging is improved from 90% to 93% accuracy (m...

Sequence Tagging, including part of speech tagging, chunking and named entity recognition, is an important task in NLP. Recurrent neural network models such as Bidirectional LSTMs have produced impressive results on sequence tagging. In this work, we first present a Bidirectional LSTM neural network model for sequence tagging tasks. Then we show a simple and fast greedy sequence tagging system ...

Part-of-speech tagging involves assigning the correct tag from a specified tagset to each item in a text corpus. Approaches to part-of-speech tagging can be grouped according to the type of knowledge representation they employ and whether those representations are hand-crafted or generated using machine learning. Numerical representations require machine learning as they cannot be easily unders...