Improved syntactic and semantic analysis for natural language processing

本项目旨在提高自然语言句法和语义分析的准确性，以便从文本中自动提取含义。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手动标记的数据。该项目计划开发新的间接监督方法，以克服这种标记的数据瓶颈。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，以用于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。

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.