On September 12th at 2pm in TASC1 9204 West, Vivian Kou will defend her MSc thesis on the topic of “Speed versus Accuracy in Neural Sequence Tagging for Natural Language Processing”.
Sequence Tagging, including part of speech tagging 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 simple and fast greedy sequence tagging system using different types of feedforward neural net- work models. Then we show the speed and accuracy comparison between Bidirectional LSTMs and feedforward models. Besides the feedforward and the Bidirectional LSTM models, we propose two new models based on Mention2Vec by Stratos (2016): Feedforward-Mention2Vec for Named Entity Recognition and BPE-Mention2Vec for Part-of-Speech Tagging. Feedforward-Mention2Vec predicts named entity boundaries first and then predicts types of named entities. BPE-Mention2Vec uses the Byte Pair Encoding algorithm to segment words in a sequence first and then predicts the Part-of-Speech tags for the subword spans. We carefully design the experiments to demonstrate the speed and accuracy trade- off in different models. The empirical results reveal that feedforward models can achieve comparable accuracy and faster speed than recurrent models for Part-of-Speech tagging, and Feedforward-Mention2Vec is competitive with the fully structured BiLSTM model for Named Entity Recognition while being more scalable in the number of named entity types.
M.Sc. Examining Committee: