Discourse parsing is an important research area in natural language processing(NLP),which aims to parse the discourse structure of coherent sentences.In this survey,we introduce several different kinds of discourse pa...Discourse parsing is an important research area in natural language processing(NLP),which aims to parse the discourse structure of coherent sentences.In this survey,we introduce several different kinds of discourse parsing tasks,mainly including RST-style discourse parsing,PDTB-style discourse parsing,and discourse parsing for multiparty dialogue.For these tasks,we introduce the classical and recent existing methods,especially neural network approaches.After that,we describe the applications of discourse parsing for other NLP tasks,such as machine reading comprehension and sentiment analysis.Finally,we discuss the future trends of the task.展开更多
文摘由于传统课程数据库检索系统查全效果较差,同时受到噪声影响,导致检索精准度较低,不能满足用户对Stac(Statistical Analysis)课程数据库检索的需求。为此,提出基于场景理论的Stac课程数据库自动检索系统设计。在场景理论下,对数据库自动检索系统进行总体设计,添加分词模块,采用组合型歧义统计方式,区分Stac课程数据库中同义或多义词;使用网络蜘蛛寻找网页链接地址,读取内容,进行全部目标地址检索;当采集量达到一定规模时,调用数个独立的搜索引擎,相互合作,以此建立索引库,根据Stac课程资源数据规范标准进行数据采集,利用索引引擎,将采集结果全部输入到系统中。通过辨认情景特点,建立光盘数据库,设计检索流程,严密监视各个机器行为,避免噪声干扰,经过中心DB Server(Data Base Senver)处理,将地址列表合并,形成新资源列表,供用户检索。由实验结果可知,该系统检索精准度最高可达到98%,为多图像检索提供系统支持。
基金The research in this article is supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project(2018AA0101901)the National Key Research and Development Project(2018YFB1005103)+2 种基金the National Natural Science Foundation of China(Grant Nos.61772156 and 61976073)Shenzhen Foundational Research Funding(JCYJ20200109113441941)the Foundation of Heilongjiang Province(F2018013).
文摘Discourse parsing is an important research area in natural language processing(NLP),which aims to parse the discourse structure of coherent sentences.In this survey,we introduce several different kinds of discourse parsing tasks,mainly including RST-style discourse parsing,PDTB-style discourse parsing,and discourse parsing for multiparty dialogue.For these tasks,we introduce the classical and recent existing methods,especially neural network approaches.After that,we describe the applications of discourse parsing for other NLP tasks,such as machine reading comprehension and sentiment analysis.Finally,we discuss the future trends of the task.
