Learning concept embeddings for dataless classification via efficient bag-of-concepts densification

Explicit concept space models have proven efficacy for text representation in many natural language and text mining applications. The idea is to embed textual structures into a semantic space of concepts which captures the main ideas, objects, and the characteristics of these structures. The so-called bag-of-concepts (BoC) representation suffers from data sparsity causing low similarity scores between similar texts due to low concept overlap. To address this problem, we propose two neural embedding models to learn continuous concept vectors. Once they are learned, we propose an efficient vector aggregation method to generate fully continuous BoC representations. We evaluate our concept embedding models on three tasks: (1) measuring entity semantic relatedness and ranking where we achieve 1.6% improvement in correlation scores, (2) dataless concept categorization where we achieve state-of-the-art performance and reduce the categorization error rate by more than 5% compared to five prior word and entity embedding models, and (3) dataless document classification where our models outperform the sparse BoC representations. In addition, by exploiting our efficient linear time vector aggregation method, we achieve better accuracy scores with much less concept dimensions compared to previous BoC densification methods which operate in polynomial time and require hundreds of dimensions in the BoC representation.