域内嵌入用于生成意大利语言概念的复杂描述
1Department of Politics and Communication Science, University of Salerno, via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy. amaisto@unisa.it.
Cognitive processing
|October 21, 2024
概括
我们创建了一个新的分布式语义资源,使用语言数据连接连续的词向量与离散的语义理论. 该资源有助于生成合理的语义描述和概念分类.
科学领域:
- 计算语言学 计算语言学
- 词典写作 词典写作 词典写作
- 自然语言处理自然语言处理.
背景情况:
- 弥合连续分布式语义向量和离散语义理论之间的差距是一个重大挑战.
- 现有的方法通常依赖神经解码来将词嵌入转化为离散属性.
- 需要采用替代策略,利用语言数据进行语义表示.
研究的目的:
- 提出一个以语言和词汇信息丰富的分布式语义资源.
- 从分布向量连接连续的语义值与语义理论中的离散描述.
- 为了促进对概念的推理性语义描述的生成.
主要方法:
- 使用意大利名词和动词根据语义特征和类进行分类,开发了特定领域的共同发生矩阵.
- 基于特定词汇领域相关的定义词汇集计算的同时出现值.
- 创建了一个资源,包括21个域特定矩阵,一个全面矩阵和一个图形用户界面.
主要成果:
- 通过两个实验证明了资源的实用性,取得了有希望的结果.
- 成功执行了动物名词的自动分类.
- 有效地利用开发的资源提取动物特征.
结论:
- 拟议的分布式语义资源有效地弥合了分布式和离散语义表示之间的差距.
- 该资源促进了合理的语义描述生成和概念分类.
- 语言数据集成为语义分析提供了一个可行的神经解码替代方案.
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