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Radical and Stroke-Enhanced Chinese Word Embeddings Based on Neural Networks

机译：基于神经网络的激进和中风增强的中文单词嵌入

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摘要

The internal structural information of words has proven to be very effective for learning Chinese word embeddings. However, most previous attempts made a single form extraction of internal feature to learn representations, ignoring the comprehensive combination of such information. And they focused only on explicit feature of internal structures, even though these structures still have the implicit semantics of words. In this paper, we propose Radical and Stroke-enhanced Word Embeddings (RSWE), a novel method based on neural networks for learning Chinese word embeddings with joint guidance from semantic and morphological internal information. RSWE enables an embedding model to learn simultaneously from (1) implicit semantic information that is exploited from the radicals, and (2) stroke n-grams information that can be explicitly obtained from Chinese words. In the learning process, RSWE uses stroke n-grams to capture the local structural feature of words, and integrates the implicit information exploited from radicals to enhance the semantic of embeddings. Through this combination procedure, semantics of Chinese words are effectively transferred into the learned embeddings. We evaluate the effectiveness of RSWE on word similarity computation, word analogy reasoning, performance over dimensions, performance over learning corpus size, and named entity recognition tasks, the experimental results show that our model outperforms existing state-of-the-art approaches.
机译：言语的内部结构信息已被证明对学习中文单词嵌入来非常有效。但是，最先前的尝试单一的内容提取内部特征以学习表示，忽略这些信息的全面组合。它们只关注内部结构的明确功能，即使这些结构仍然具有隐式的单词语义。本文提出了基于语义和形态内部信息的联合指导，提出了一种基于神经网络的新型方法，提出了激进和中风增强的单词嵌入式（RSWE），这是一种基于神经网络的新方法。 RSWE使嵌入式模型能够同时学习（1）从激进派利用的隐式语义信息，以及（2）笔划N-GRAMS信息可以从中文单词明确地获得。在学习过程中，RSWE使用笔划n-grams来捕获单词的本地结构特征，并集成了从激进的内隐式信息来增强嵌入的语义。通过这种组合程序，中文单词的语义有效地转移到学习的嵌入中。我们评估RSWE对单词相似性计算的有效性，单词类比推理，尺寸的性能，学习语料库大小，并命名实体识别任务，实验结果表明，我们的模型优于现有的最先进的方法。

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  • 来源
    《Neural processing letters》 |2020年第2期|1109-1121|共13页
  • 作者

    Shirui Wang; Wenan Zhou; Qiang Zhou;

  • 作者单位

    Department of Computer Science Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District 100876 Beijing China;

    Department of Computer Science Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District 100876 Beijing China;

    Department of Computer Science Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District 100876 Beijing China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Word embeddings; Internal structure; Neural networks;

    机译：单词嵌入式;内部结构;神经网络;

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