Automatic Labeling for Hierarchical Topics with NETL
Rinto Kozono, Ryosuke Saga · 2020
Hierarchical topic model is the method used in considering topics with hierarchical relationships. Neural embedding topic labelling (NETL) is a method utilized to label topics with neural embedding, even though it labels topics without topic relationships. The labels of hierarchical topics should have hierarchical relationship with other labels. This study proposes a method for labeling hierarchical topics with hierarchical relationships, and uses NETL to generate candidate labels for bottom topics. Moreover, our proposed method calculates how small the overlap of the candidate labels compared with other sibling topics. To label the upper topics, our method adds the label of the bottom topics and generate labels in the same way as the bottom topics recursively. Our method succeeded label hierarchical topics with labels which is more qualitative labels to consider hierarchical relationship of topics.