Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks

Theerapat Lapjaturapit, Kobkrit Viriyayudhakom, T. Theeramunkong
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引用次数: 11

Abstract

Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.
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基于双向LSTM神经网络的多候选分词
大多数现有的分词方法输出一个单一的分词解。本文提供了在考虑多个解决方案时的分词性能分析。针对这一研究,应用了一个具有多个阈值的深度神经网络来生成多个候选的分割。作为实验平台,我们将双向长短期记忆(BiLSTM)单元用于深度神经网络的11个上下文。作为绩效指标,有三个衡量标准;召回率、精度和f-measure都是根据边界水平和词水平评估的不同阈值绘制的。实验结果表明,多候选词分词可以在保证分词精度的前提下提高查全率。
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