  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.

Computation and Language;Artificial Intelligence;Information Retrieval