Building Journalistic Knowledge Graphs for Exploratory Text Analysis

该项目是曼彻斯特大学计算机科学系和英国广播公司之间的合作伙伴关系，它侧重于协调多种文本分类和信息提取技术，用于构建知识图（可以支持计算机进行文本理解任务的数据结构），知识图可以支持记者进行复杂的分析任务。该博士生将在自然语言处理（NLP）和知识表示之间的接口工作，提出并评估句子间和句子内的意义表示模型，以满足现实世界记者的分析需求。该项目将探索多种最先进的机器学习技术在话语分析中的应用，如故事/叙事提取、论证挖掘、意见挖掘。目标：-使用信息提取和文本分类方法构建基于知识图的表示。-对BBC语料库上知识图谱提取质量的内在评价。研究问题：-在新闻语料库上使用话语级和句子级表示（潜在的和明确的）是否支持更深层次的文本推理任务？新颖工程：该项目侧重于理解话语级表示（句子之间的关系）如何影响文本推理任务。

DescriptionThis project is a partnership between the Department of Computer Science at the University of Manchester and the BBC and it focuses on the coordination of multiple text classification and information extraction techniques for the construction of knowledge graphs (data structures which can support computers in text understanding tasks) which can support journalists in complex analytical tasks. The PhD student will work at the interface between Natural Language Processing (NLP) and Knowledge Representation, proposing and evaluating inter and intra-sentence meaning representations models to address analytical demands of journalists in the real-world. The project will explore the application of multiple state-of-the-art machine learning techniques for discourse analysis such as story/narrative extraction, argumentation mining, opinion mining. Objectives: - Construction of a knowledge graph-based representation using information extraction and text classification methods.- Intrinsic evaluation of the quality of the knowledge graph extraction on the BBC corpus.- Extrinsic evaluation using question answering and textual entailment in a journalistic settingResearch questions:- Can the use of discourse-level and sentence-level representations (latent and explicit) over a journalistic corpus, support deeper textual inference tasks?Novel engineering:- The project focuses on understanding how discourse-level representations (relationships between sentences) can impact text inference tasks.