领域感知词嵌入的生成方法研究

Dominic Seyler, Chengxiang Zhai
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引用次数: 2

摘要

词嵌入是许多文本数据应用程序的基本组件。在大多数工作中,在一般文本语料库上训练的“开箱即用”嵌入被使用,但是当应用于特定领域的设置时,它们可能不太有效。因此,如何创建“领域感知”的词嵌入是一个有趣的开放性研究问题。本文研究了基于通用文本语料库和特定文本语料库的三种领域感知词嵌入方法,包括嵌入向量的拼接、文本数据的加权融合和对齐嵌入向量的插值。尽管所研究的策略是为特定于领域的任务量身定制的,但它们足够通用,可以应用于任何领域,而不是特定于单个任务。实验结果表明,三种方法均能取得较好的效果,但插值方法的效果始终最好。
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A Study of Methods for the Generation of Domain-Aware Word Embeddings
Word embeddings are essential components for many text data applications. In most work, "out-of-the-box" embeddings trained on general text corpora are used, but they can be less effective when applied to domain-specific settings. Thus, how to create "domain-aware" word embeddings is an interesting open research question. In this paper, we study three methods for creating domain-aware word embeddings based on both general and domain-specific text corpora, including concatenation of embedding vectors, weighted fusion of text data, and interpolation of aligned embedding vectors. Even though the investigated strategies are tailored for domain-specific tasks, they are general enough to be applied to any domain and are not specific to a single task. Experimental results show that all three methods can work well, however, the interpolation method consistently works best.
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