Expanding a dictionary of pre-selected keywords is crucial for tasks in information retrieval, such as database query and online data collection. Here we propose Local Graph-based Dictionary Expansion (LGDE), a method that uses tools from manifold learning and network science for the data-driven discovery of keywords starting from a seed dictionary. At the heart of LGDE lies the creation of a word similarity graph derived from word embeddings and the application of local community detection based on graph diffusion to discover semantic neighbourhoods of pre-defined seed keywords. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings and can capture word similarities based on paths of semantic association. We validate our method on a corpus of hate speech-related posts from Reddit and Gab and show that LGDE enriches the list of keywords and achieves significantly better performance than threshold methods based on direct word similarities. We further demonstrate the potential of our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on data collected and analysed by domain experts by expanding a conspiracy-related dictionary.

使用局部基于图的词典扩展方法，结合流形学习和网络科学的工具，通过基于词嵌入的词相似性图和基于图扩散的局部社区检测，发现预定义种子关键词的语义邻域进行关键词发现。在Reddit和Gab的仇恨言论帖子语料库上验证了该方法相比于基于直接词相似性的阈值方法在关键词扩展方面的优越性，并通过扩展阴谋相关词典的实际用例，通过领域专家收集和分析的数据来定量评估了LGDE方法的潜力。

LGDE：局部基于图的字典扩展