Textual materials represent a rich source of information for improving the decision-making of people, businesses and organizations. However, for natural language processing (NLP), it is difficult to correctly infer the meaning of narrative content in the presence of negations. The reason is that negations can be formulated both explicitly (e.g., by negation words such as “not”) or implicitly (e.g., by expressions that invert meanings such as “forbid”) and that their use is further domain-specific. Hence, NLP requires a dynamic learning framework for detecting negations and, to this end, we develop a reinforcement learning framework for this task. Formally, our approach takes document-level labels (e.g., sentiment scores) as input and then learns a negation policy based on the document-level labels. In this sense, our approach replicates human perceptions as provided by the document-level labels and achieves a superior prediction performance. Furthermore, it benefits from weak supervision; meaning that the need for granular and thus expensive word-level annotations, as in prior literature, is replaced by document-level annotations. In addition, we propose an approach to interpretability: by evaluating the state-action table, we yield a novel form of statistical inference that allows us to test which linguistic cues act as negations.

文字材料代表了丰富的信息资源，可用于改善人员，企业和组织的决策。但是，对于自然语言处理（NLP），在存在否定的情况下很难正确地推断叙事内容的含义。原因是，可以明确地（例如，通过诸如“ not”之类的否定词）或（例如，通过颠倒诸如“ forbid”之类的含义的表达）隐式地表达否定，并且它们的使用还具有特定于领域的含义。因此，NLP需要动态的学习框架来检测否定，为此，我们为此任务开发了强化学习框架。形式上，我们的方法将文档级别的标签（例如，情感分数）作为输入，然后基于文档级别的标签学习否定策略。在这个意义上说，我们的方法复制了文档级标签所提供的人类感知，并实现了卓越的预测性能。此外，它得益于监管不力；这意味着，像在先文献中一样，对细粒度且因此昂贵的单词级注释的需求已由文档级注释取代。此外，我们提出了一种可解释性的方法：通过评估状态-动作表，我们产生了一种新的统计推断形式，该统计推断使我们能够测试哪些语言线索充当否定词。