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arXiv 2501.02546cs.CLcs.AI

TreeMatch:一种在特定领域上使用依存知识的完全无监督 WSD 系统

TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain

University of Houston-Downtown(休斯顿市中心大学);University of Massachusetts-Boston(马萨诸塞大学波士顿分校)

Andrew Tran, Chris Bowes, David Brown, Ping Chen, Max Choly, Wei Ding

AI总结:

本文提出完全无监督的 WSD 系统 TreeMatch,利用面向特定领域构建的知识库中的依存知识,并在 SemEval 2010 Task 17 上取得优于最频繁选择基线的精确率。

摘要:

词义消歧(WSD)是计算语言学中的主要挑战之一。TreeMatch 是一个 WSD 系统,最初利用 SemEval 2007 Task 7(粗粒度英语全词任务)的数据开发,后经调整用于 SemEval 2010 Task 17(特定领域的全词词义消歧)。该系统基于完全无监督方法,使用从为该任务构建的特定领域知识库中提取的依存知识。在该任务上评估时,系统精确率高于最频繁选择基线。

英文摘要:

Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All-words Task) that has been adapted for use in SemEval 2010 Task 17 (All-words Word Sense Disambiguation on a Specific Domain). The system is based on a fully unsupervised method using dependency knowledge drawn from a domain specific knowledge base that was built for this task. When evaluated on the task, the system precision performs above the Most Frequent Selection baseline.

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。

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