Introducing Inter-Relatedness between Wikipedia Articles in Explicit Semantic Analysis
This work aims to improve the quality of text representations for tasks that rely on Explicit Semantic Analysis, which could benefit researchers and applications in natural language processing.
This paper proposes a method to improve Explicit Semantic Analysis (ESA) by incorporating inter-relatedness between Wikipedia articles. By retrofitting ESA vectors with knowledge from an undirected graph of Wikipedia articles, the authors demonstrate decent improvements in performance measures, including Spearman's Rank correlation coefficient, on several smaller Wikipedia corpus subsets.
Explicit Semantic Analysis (ESA) is a technique used to represent a piece of text as a vector in the space of concepts, such as Articles found in Wikipedia. We propose a methodology to incorporate knowledge of Inter-relatedness between Wikipedia Articles to the vectors obtained from ESA using a technique called Retrofitting to improve the performance of subsequent tasks that use ESA to form vector embeddings. Especially we use an undirected Graph to represent this knowledge with nodes as Articles and edges as inter relations between two Articles. Here, we also emphasize how the ESA step could be seen as a predominantly bottom-up approach using a corpus to come up with vector representations and the incorporation of top-down knowledge which is the relations between Articles to further improve it. We test our hypothesis on several smaller subsets of the Wikipedia corpus and show that our proposed methodology leads to decent improvements in performance measures including Spearman's Rank correlation coefficient in most cases.