土耳其语的抽象意义表征

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Natural Language Engineering Pub Date : 2022-04-28 DOI:10.1017/s1351324922000183

Elif Oral, Ali Acar, Gülşen Eryiğit

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引用次数: 4

摘要

摘要意义表示（AMR）是一种基于图的句子级意义表示方法，近年来得到了广泛的应用。AMR是一种基于知识的意义表示，严重依赖于用于链接谓词框架的框架语义和用于链接命名实体概念的实体知识库（如DBpedia）。尽管它最初是为英语设计的，但通过定义特定语言的差异和表示，它可以适应非英语语言。本文介绍了第一个土耳其语的AMR表示框架，与英语相比，由于其类型差异，这对AMR提出了不同的挑战；粘性的，自由的组成顺序，形态高度丰富，导致句子中较少的词表形式。针对这些特点提出的解决方案有望指导其他类似语言的研究，并加快跨语言通用AMR框架的构建。除了这一主要贡献外，本文还介绍了第一个700句的AMR语料库的构建，第一个用于半自动注释的AMR解析器（即基于树到图的AMR语法分析器），以及对引入的土耳其语资源的评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Abstract meaning representation of Turkish

Abstract meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish.

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来源期刊

Natural Language Engineering COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

5.90

自引率

12.00%

发文量

审稿时长

>12 weeks

期刊介绍： Natural Language Engineering meets the needs of professionals and researchers working in all areas of computerised language processing, whether from the perspective of theoretical or descriptive linguistics, lexicology, computer science or engineering. Its aim is to bridge the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use. As well as publishing research articles on a broad range of topics - from text analysis, machine translation, information retrieval and speech analysis and generation to integrated systems and multi modal interfaces - it also publishes special issues on specific areas and technologies within these topics, an industry watch column and book reviews.