Kou, Xinxin - Speed versus accuracy in neural sequence tagging for natural language processing...

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This thesis has been submitted to the Library for purposes of graduation, but needs to be audited for technical details related to publication in order to be approved for inclusion in the Library collection.
Term: 
Fall 2017
Degree: 
M.Sc.
Degree type: 
Thesis
Department: 
School of Computing Science
Faculty: 
Applied Sciences
Senior supervisor: 
Anoop Sarkar
Thesis title: 
Speed versus accuracy in neural sequence tagging for natural language processing
Given Names: 
Xinxin
Surname: 
Kou
Abstract: 
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 using a feedforward neural network. We compare the speed and accuracy between the Bidirectional LSTM model and the greedy feedforward model. In addition, we propose two new models based on Mention2Vec by Stratos (2016): Feedforward-Mention2Vec for named entity recognition and chunking, and BPE-Mention2Vec for part-of-speech tagging. Feedforward-Mention2Vec predicts tag boundaries and corresponding types separately. BPE-Mention2Vec uses the Byte Pair Encoding algorithm to segment words 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 the greedy feedforward model can achieve comparable accuracy and faster speed than recurrent models for sequence 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.
Keywords: 
Natural Language Processing; Sequence Tagging; Neural Networks
Total pages: 
47