论文标题
探索术语的语义能力
Exploring Semantic Capacity of Terms
论文作者
论文摘要
我们介绍和研究术语的语义能力。例如，人工智能的语义能力高于线性回归的语义能力，因为人工智能具有更广泛的含义范围。了解术语的语义能力将有助于自然语言处理中的许多下游任务。为此，我们提出了一个两步模型来研究术语的语义能力，该模型将大量文本语料库作为输入，并可以评估术语的语义能力，如果文本语料库可以提供足够的术语相关信息。在三个领域进行的广泛实验表明，与精心设计的基准和人类水平评估相比，我们的模型的有效性和合理性。
We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope. Understanding semantic capacity of terms will help many downstream tasks in natural language processing. For this purpose, we propose a two-step model to investigate semantic capacity of terms, which takes a large text corpus as input and can evaluate semantic capacity of terms if the text corpus can provide enough co-occurrence information of terms. Extensive experiments in three fields demonstrate the effectiveness and rationality of our model compared with well-designed baselines and human-level evaluations.
