# Learning Concept Embeddings for Efficient Bag-of-Concepts Densification

## TL;DR

This paper introduces neural embedding models for concepts to densify Bag-of-Concepts representations, improving text similarity, categorization, and classification tasks with efficient continuous vector aggregation.

## Contribution

The paper presents novel neural embedding models and an efficient aggregation method to enhance Bag-of-Concepts representations for various NLP tasks.

## Key findings

- Achieved 1.6% improvement in semantic relatedness correlation
- Reduced categorization error rate by over 5%
- Outperformed sparse BoC in document classification

## Abstract

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.