Improved syntactic and semantic analysis for natural language processing

改进自然语言处理的句法和语义分析

• 批准号: DP160102156
• 负责人: Prof Mark Johnson
• 金额: $ 27.19万
• 依托单位: Macquarie University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-06-30 至 2020-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160102156
• 关键词: Improved; syntactic; semantic; analysis; natural

This project aims to improve the accuracy of syntactic and semantic analysis of natural language for automatic extraction of meaning from text. Many data mining and information extraction applications rely on syntactic and semantic analysis. Current analysis approaches are limited because they require expensive manually-labelled data. The project plans to develop new indirectly-supervised approaches to overcome this labelled data bottleneck. By integrating information from large text corpora and structured databases, the project aims to minimise the reliance on manually-labelled data for training natural language processing systems. Automatic methods for syntactic and semantic analysis would have a wide range of applications in extracting information from large collections of unstructured data, such as hospital patient records or social media.

本项目旨在提高自然语言句法和语义分析的准确性，实现文本意义的自动提取。许多数据挖掘和信息提取应用依赖于句法和语义分析。目前的分析方法是有限的，因为它们需要昂贵的人工标记数据。该项目计划开发新的间接监督方法来克服这种标记数据瓶颈。通过整合来自大型文本语料库和结构化数据库的信息，该项目旨在最大限度地减少对人工标记数据的依赖，以训练自然语言处理系统。句法和语义分析的自动方法在从大量非结构化数据（如医院患者记录或社交媒体）中提取信息方面具有广泛的应用。