Robust visualization of complex data is critical for the effective use of NLP for event classification, as the volume of data is large and the high-dimensional structure of text makes data challenging to summarize succinctly. In event extraction tasks in particular, visualization can aid in understanding and illustrating the textual relationships from which machine learning tools produce insights. Through our case study which seeks to identify potential triggers of state-led mass killings from news articles using NLP, we demonstrate how visualizations can aid in each stage, from exploratory analysis of raw data, to machine learning training analysis, and finally post-inference validation.
翻译:对复杂数据进行强有力的直观化对于有效利用NLP对事件进行分类至关重要,因为数据量很大,而高维文本结构使数据难以简明扼要地归纳;特别是在提取任务中,直观化有助于理解和说明机器学习工具产生洞察力的文字关系。 我们的案例研究试图从利用NLP的新闻文章中找出国家主导的大规模杀戮的潜在触发因素,通过案例研究,我们展示了视觉化如何在每个阶段,从原始数据的探索性分析,到机器学习培训分析,以及最后的推论后验证,帮助每个阶段,从原始数据的探索性分析,到机器学习培训分析,以及最后的推论后验证。