Replication Data for: Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction

Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability to automatically build a domain-specific dictionary. As an illustrative use case of our approach, we create an objective dictionary that can infer conflict intensity from text data. We train the neural networks on a corpus of conflict reports and match them with conflict event data. This corpus consists of over 14,000 expert-written International Crisis Group (ICG) CrisisWatch reports between 2003 and 2021. Sensitivity analysis is used to extract the weighted words from the neural network to build the dictionary. In order to evaluate our approach, we compare our results to state-of-the-art deep learning language models, text-scaling methods, as well as standard, non-specialized, and conflict event dictionary approaches. We are able to show that our approach outperforms other approaches while retaining interpretability.近年来，自然语言处理（Natural Language Processing，NLP）方法的相关进展已显著提升了其性能表现。然而，复杂度更高的自然语言处理模型往往更难实现可解释性，且计算成本高昂。为此，我们提出了一种词典构建方法，能够在模型复杂度与可解释性之间实现审慎的权衡。该方法将深度神经网络架构与提升模型可解释性的技术相结合，可自动构建面向特定领域的词典。为展示该方法的应用场景，我们构建了一款可从文本数据中推断冲突强度的客观词典。我们基于冲突报告语料库训练神经网络，并将其与冲突事件数据进行匹配。该语料库包含2003年至2021年间由专家撰写的14000余篇国际危机组织（International Crisis Group，ICG）《危机观察》（CrisisWatch）报告。我们通过敏感性分析从神经网络中提取带权重的词汇，以此构建该词典。为评估所提方法的性能，我们将其结果与当前最优的深度学习语言模型、文本缩放方法，以及标准非专用词典与冲突事件词典等各类方法进行了对比。实验结果表明，我们的方法在保留可解释性的同时，性能优于其他同类方法。