计算语义学:如何解决超感的悬念

Aishwarya Asesh
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引用次数: 0

摘要

理解人类语言是一项艰巨的任务,有各种各样的研究领域旨在解释和研究人类语言的原理。语言学、心理学和计算机科学都使用特定领域的工具来描述和建模语言。自然语言处理是一个旨在利用计算机制来处理自然发生的人类语言的领域。建模语法给出了语言结构。使用一般意义类或“超意义”,可以潜在地用语义信息丰富文本。给定一个具有句法信息的句子,以及一组封闭的语义超感觉,能否推导出一个超感觉标记句子?此外,我们可以为多词表达式划分边界吗?本研究的目的是通过对带有Word、词性(POS)、多词表达(MWE)和超意义标记的训练数据进行训练,创建一个多词表达边界和超意义标记的句子。语义标记句可以用于问答系统、信息检索、话语和情感分析等许多任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational Semantics: How to solve the suspense of supersense
Understanding human language is a difficult task, with varied fields of study which aim at explaining and researching the human language principles. Linguistics, Psychology and Computer Science all use domain specific tools to describe and model language. Natural Language Processing is the field which aims at using computational mechanisms to process naturally occurring human language. Modeling syntax gives language structure. Using general sense classes, or "supersenses" one can potentially enrich texts with semantic information. Given a sentence with syntactic information, and a closed set of semantic supersenses, can a supersense tagged sentence be derived? Furthermore, can one demarcate boundaries for multiword expressions? The aim of this research study is to create a multiword expression boundary and supersense labelled sentence by training with Word, part-of-speech (POS), multiword expression (MWE) and supersense tagged training data. The semantically tagged sentences can be used for many tasks such as question answering systems, information retrieval, discourse and sentiment analysis.
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