AUC oriented Bidirectional LSTM-CRF Models to Identify Algorithms Described in an Abstract

• Main Authors: Brian Chen, 陳柏穎
• Other Authors: 林守德
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/p3grat

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === In this thesis, we attempt to identify algorithms mentioned in the paper abstract. We further want to discriminate the algorithm proposed in this paper from algorithms only mentioned or compared, since we are more interested in the former. We model this task as a sequential labeled task and propose to use a state-of-the-art deep learning model LSTM-CRF as our solution. However, the data or labels are generally imbalanced since not all the sentence in the abstract is describing its algorithm. That is, the ratio between different labels is skewed. As a result, it is not suitable to use traditional LSTM-CRF model since it only optimizes accuracy. Instead, it is more reasonable to optimize AUC in imbalanced data because it can deal with skewed labels and perform better in predicting rare labels. Our experiment shows that the proposed AUC-optimized LSTM-CRF outperforms the traditional LSTM-CRF. We also show the ranking of algorithms used currently, and find the trend of different algorithms used in recent years. Moreover, we are able to discover some new algorithms that do not exist in our training data.