英语翻译With an overwhelming volume of news reports currently av

英语翻译
With an overwhelming volume of news reports currently available,there is an increasing need for automatic techniques to analyze and present news to a general reader in a meaningful and efficient manner.Previous research has focused primarily on organizing news stories into a list of clusters by the main topics that they discuss.We believe that viewing a news topic as a simple collection of stories is restrictive and inefficient for a user hoping to understand the information quickly.As a proposed solution to the automatic news organization problem,we introduce incident threading in this thesis.All text that describes the occurrence of a real-world happening is merged into a news incident,and incidents are organized in a network with dependencies of predefined types.In order to simplify the implementation,we start with the common assumption that a news story is coherent in content.In the story threading system,a cluster of news documents discussing the same topic are further grouped into smaller sets,where each represents a separate news event.Binary links are established to reflect the contextual information among those events.Experiments in story threading show promising results.We next describe an enhanced version called relation-oriented story threading that extends the range of the prior work by assigning type labels to the links and describing the relation within each story pair as a competitive process among multiple options.The quality of links is greatly improved with a global optimization process.Our final approach,passage threading,removes the story-coherence assumption by conducting passage-level processing of news.First we develop a new tested for this research and extend the evaluation methods to address new issues.Next,a calibration study demonstrates that an incident network helps reading comprehension with an accuracy of 25-30% in a matrix comparison evaluation.Then a new three-stage algorithm is described that identifies on-subject passages,groups them into incidents,and establishes links between related incidents.Finally,significant improvement over earlier work is observed when the training phase optimizes the harmonic mean of various evaluation measures,and the performance meets the goal in the calibration study.

随着近期有用的新闻报道的容量的涌进,自动技术用来为普通听众有效得分析和显示新闻的需求也大大地增加.先前的调查主要集中在通过讨论的主要标题来分类编制新闻故事.我们认为作为简单的故事收集而看新闻是有局限的,并且对于那些希望更快速的获得信息的观众是低效率的.作为一个对自动新闻编制问题的提议解决方法,在这篇论文中我们介绍了一个线程事件.所有描述真实世界的事件发生出现在一条新闻事件里,并且事件被编制在一个预先确定类型的附属网络里.为了简化这个安装,开始我们假设新闻故事和内容是相关的.在故事中的线程系统,一连串的讨论同一个话题的新闻资料被进一步的分类到一个小体系中,在这个体系中每一条都表达一个分离的新闻事件.二进制的连接被建立用来反映在这些事件中的前后关联的信息.在故事线程中的实验效果非常可观.我们下面描述了一个加强版本,称为关系定向故事线程,这个线程扩大了先前工作的范围,通过分配类型标签到连接和描述每个故事关系为一个在多选择中的竞争过程.我们最后一个方法,段落线程通过指导新闻段落层次的进程来移除新闻连贯的假设.首先我们建立一个新闻用来检测这个研究,并且扩展评估方法来提出新的问题.接着校准学证明一个事件网络在矩阵对比评估中可帮助阅读理解的准确性达到25-30%之间.接着一个新的三步计算法被描述为定义关于问题的段落,把它们分类成事件,并且在相关事件中建立连接.最后,当训练阶段使不同的评估测量的和谐方法完善,并且在校准学中性能与结果吻合的时候,在先前工作中非常重要的进步被观察到.
里面有专业术语最好和老师核对,比如说incident threading,on-subject passages 等.
语法有一些错误
First we develop a new tested for this research 里面的new 要改成news,或者把tested改成test.
the training phase optimizes the harmonic mean 里面的mean改成means比较能讲通,如果坚持用mean 意思就是“不同评估测量的和谐平均值”.
文章结构有些混乱,如果你想进一步提高写作可以给我发信息