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首页> 外文会议>International Seminar on Annotating, Extracting and Reasoning about Time and Events; 20050410-15; Dagstuhl Castle(DE) >Arguments in TimeML: Events and Entities
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Arguments in TimeML: Events and Entities

机译：TimeML中的参数：事件和实体

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摘要

TimeML is a specification language for the annotation of events and temporal expressions in natural language text. In addition, the language introduces three relational tags linking temporal objects and events to one another. These links impose both aspectual and temporal ordering over time objects, as well as mark up subordination contexts introduced by modality, evidentiality, and factivity. Given the richness of this specification, the TimeML working group decided not to include the arguments of events within the language specification itself. Full reasoning and inference over natural language texts clearly requires knowledge of events along with their participants. In this paper, we define the appropriate role of argumenthood within event markup and propose that TimeML should make a basic distinction between arguments that are events and those that are entities. We first review how TimeML treats event arguments in subordinating and aspectual contexts, creating event-event relations between predicate and argument. As it turns out, these constructions cover a large number of the argument types selected for by event predicates. We suggest that TimeML be enriched slightly to include causal predicates, such as lead to, since these also involve event-event relations. As such, causal relationships will be a relation type for the new Discourse Link that will also encode other discourse relations such as elaboration. We propose that all other verbal arguments be ignored by the specification, and any predicate-argument binding of participants to an event should be performed by independent means. In fact, except for the event-denoting arguments handled by the extension to TimeML proposed here, almost full temporal ordering of the events in a text can be computed without argument identification.
机译：TimeML是一种规范语言，用于注释自然语言文本中的事件和时间表达。另外，该语言引入了三个关系标签，将时间对象和事件相互链接。这些链接强加了时间对象的时态和时间顺序，并标记了情态，证据和真实性引入的从属上下文。考虑到该规范的丰富性，TimeML工作组决定在语言规范本身中不包括事件的参数。对自然语言文本进行充分的推理和推论显然需要事件及其参与者的知识。在本文中，我们定义了事件标记在事件标记中的适当作用，并建议TimeML应该对作为事件的参数和作为实体的参数进行基本区分。我们首先回顾一下TimeML如何在从属和方面上下文中处理事件参数，并在谓词和参数之间创建事件-事件关系。事实证明，这些构造涵盖了由事件谓词选择的大量参数类型。我们建议对TimeML进行略微丰富，以包含因果谓语（例如，导致），因为它们也涉及事件-事件关系。这样，因果关系将成为新“话语链接”的一种关系类型，该链接还将对其他话语关系（例如阐述）进行编码。我们建议规范忽略所有其他口头论据，并且参与者与事件的任何谓词-论据绑定都应通过独立的方式执行。实际上，除了此处提出的TimeML扩展所处理的表示事件的参数外，无需参数识别即可计算文本中事件的几乎全部时间顺序。

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