使用交错编码器网络（InterlaCed Encoder NETworks，ICE-NET）区分反义词与同义词
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)
AI总结：
针对反义词和同义词因分布语境相似而难以区分的问题，本文提出 ICE-NET，通过建模词对的关系特有属性提升分类性能，并在基准数据集上取得最高 1.8% 的相对 F1 提升。
摘要：
反义词与同义词区分是词汇语义分析和自动化词汇资源构建中的一项核心挑战。这些词对具有相似的分布语境，因此更难加以区分。这方面的主流研究试图捕捉关系词对的属性，即对称性、传递性和跨传递性。然而，现有研究无法恰当地建模关系特有属性，限制了其最终性能。本文提出用于反义词与同义词区分的交错编码器网络（InterlaCed Encoder NETworks，即 ICE-NET），旨在捕捉并建模反义词和同义词词对的关系特有属性，从而以性能增强的方式完成分类任务。使用基准数据集进行的实验评估表明，ICE-NET 在 F1-measure 上以最高达 1.8% 的相对得分优于现有研究。我们在 https://github.com/asif6827/ICENET 发布 ICE-NET 的代码。
英文摘要：
Antonyms vs synonyms distinction is a core challenge in lexico-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context which makes it harder to distinguish them. Leading research in this regard attempts to capture the properties of the relation pairs, i.e., symmetry, transitivity, and trans-transitivity. However, the inability of existing research to appropriately model the relation-specific properties limits their end performance. In this paper, we propose InterlaCed Encoder NETworks (i.e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner. Experimental evaluation using the benchmark datasets shows that ICE-NET outperforms the existing research by a relative score of upto 1.8% in F1-measure. We release the codes for ICE-NET at https://github.com/asif6827/ICENET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
