Variational Bayesian Semi-Supervised Keyword Extraction

The expansion of textual data, stemming from various sources such as online product reviews and scholarly publications on scientific discoveries, has created a significant demand for the extraction of succinct yet comprehensive information. While many methods have been proposed for automatic keyword extraction in unsupervised and fully supervised settings, effectively leveraging a partial list of known keywords, such as author-specified keywords or Twitter hashtags, remains under-explored. This work aims to enhance both the effectiveness and scalability of semi-supervised keyword extraction. We propose a novel variational Bayesian semi-supervised (VBSS) method that builds upon recent Bayesian advancement in the field, replacing computationally expensive posterior sampling with variational inference and data augmentation. This leads to closed-form updates and substantial speedups, particularly for long texts. Our numerical results show that the VBSS method not only improves performance on longer texts but also offers better control over false discovery rates compared to state-of-the-art keyword extraction techniques.