CAREER: Developing an Underspecified Representation for Temporal Information in Text

尽管最近在自然语言的自动化处理方面取得了进展，但在许多情况下，接近一般人类水平的文本理解仍然具有挑战性。 这个CAREER项目解决了这样一个挑战，自动提取和理解自然语言文本叙述中描述的事件的顺序和时间。 它开发了一个计算框架，用于表示和提取文本中传达的时间信息，最终目标是从文本中实现逼真的时间推理。 它还从事学生参与计算机科学研究的早期，特别是旨在吸引女学生追求计算机科学和相关areas.As不同于现有的方法，拟议的研究假设欠规范是一个不可分割的属性的时间表示，支持的概念粗粒度的事件集群。 它利用叙事文本的微观结构，通过识别事件集群及其叙事锚，连同默认的事件时间和持续时间，用于组织未指定的时间轴的表示。 它还通过开发三个关键组成部分来解决目前最先进技术中的知识差距。 首先，一个新的代表性计划，以方便简单，直观的选择，最大限度地减少认知努力，减少注释错误的注释。 问答作为目标应用程序，重点是阅读理解问题，这些问题需要时间推理，而不仅仅是理解简单的事实。 第二，新的注释一致性的内在评价方法被用来解决与现有的评价方法，这往往会产生不同的结果，这取决于特定的策略，通过时间关系图上的传递闭包推断出的时间关系。 所提出的方法依赖于表示时间关系的偏序集，并使用这些偏序的线性扩展，以评估注释者之间的协议和系统性能。 最后，一类新的基于神经网络的模型进行了探讨，旨在恢复偏序图锚定事件集群。 这些模型使用外部记忆组件的累积话语表示，并允许联合训练识别coreferent事件提到，分组恢复的事件大致同时的事件集群，并建立它们之间的类型链接。 该模型结合了默认事件顺序和时间的表示，以及事件的参数结构和时间表达式的定量推理。

Despite the recent advances in automated processing of natural language, approaching general human-level understanding of text in many cases still remains challenging. This CAREER project addresses one such challenge, automatically extracting and understanding the order and timing of events described in natural language text narratives. It develops a computational framework for representing and extracting temporal information conveyed in text, with the end goal to enable realistic temporal reasoning from text. It also engages student involvement in computer science research early on, and in particular is designed to attract female students to pursue careers in computer science and related areas.As distinct from existing approaches, the proposed research assumes underspecification to be an integral property of temporal representation, supported by the notion of a coarse-grained event cluster. It takes advantage of the micro-structure of narrative text by identifying event clusters and their narrative anchors which, together with default event times and durations, serve to organize the underspecified representation of the timeline. It also addresses knowledge gaps in the current state-of-the-art in by developing three key components. First, a novel representational scheme is employed to facilitate simple, intuitive choices for the annotators that minimize cognitive effort and reduce annotation error. Question answering serves as the target application, with a focus on reading-comprehension questions that require temporal reasoning beyond understanding simple factoids. Second, novel methods for intrinsic evaluation of annotation consistency are used to address problems with existing evaluation methods, which often produce varied results depending on specific strategies for crediting temporal relations inferred through transitive closure over the temporal relation graph. The proposed methods rely on representing temporal relations as partially ordered sets and use linear extensions of these partial orders in order to evaluate inter-annotator agreement and system performance. Finally, a new class of neural network-based models is explored that aim to recover partial order graphs over anchored event clusters. These models use external memory components for cumulative discourse representation, and allow joint training for identifying coreferent event mentions, grouping the recovered events into roughly-simultaneous event clusters, and establishing typed links between them. The models incorporate a representation for default event order and timing, as well as argument structure for events and quantitative inference over temporal expressions.