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摘要翻译：
在本文中，我们提出了一种数据密集型的方法来推断句子内部时态关系。时间推理适用于提取或合成时间信息（如摘要、问答）的实际NLP应用。我们的方法通过利用像“after”这样的标记的存在，绕过了手动编码的需要，这些标记公开地发出了时间关系的信号。我们首先证明了在与时间标记相关的主从句上训练的模型在模拟时间推理的伪消歧任务中取得了良好的性能（在测试过程中，时间标记被视为不可见的，模型必须从一组可能的候选标记中选择正确的标记）。其次，我们评估了所提出的方法是否为半自动创建时态注释提供了希望。具体来说，我们使用一个在噪声和近似数据（即主从句）上训练的模型来预测TimeBank中存在的句内关系。TimeBank是一个标注了丰富时态信息的语料库。我们的实验比较了几种在特征空间、语言假设和数据要求方面不同的概率模型。我们根据金本位语料库和人类受试者来评估绩效。
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英文标题：
《Learning Sentence-internal Temporal Relations》
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作者：
M. Lapata, A. Lascarides
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computation and Language 计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects.
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PDF链接：
https://arxiv.org/pdf/1110.1394
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