  As an ubiquitous method in natural language processing, word embeddings are
extensively employed to map semantic properties of words into a dense vector
representation. They capture semantic and syntactic relations among words but
the vectors corresponding to the words are only meaningful relative to each
other. Neither the vector nor its dimensions have any absolute, interpretable
meaning. We introduce an additive modification to the objective function of the
embedding learning algorithm that encourages the embedding vectors of words
that are semantically related to a predefined concept to take larger values
along a specified dimension, while leaving the original semantic learning
mechanism mostly unaffected. In other words, we align words that are already
determined to be related, along predefined concepts. Therefore, we impart
interpretability to the word embedding by assigning meaning to its vector
dimensions. The predefined concepts are derived from an external lexical
resource, which in this paper is chosen as Roget's Thesaurus. We observe that
alignment along the chosen concepts is not limited to words in the Thesaurus
and extends to other related words as well. We quantify the extent of
interpretability and assignment of meaning from our experimental results.
Manual human evaluation results have also been presented to further verify that
the proposed method increases interpretability. We also demonstrate the
preservation of semantic coherence of the resulting vector space by using
word-analogy and word-similarity tests. These tests show that the
interpretability-imparted word embeddings that are obtained by the proposed
framework do not sacrifice performances in common benchmark tests.
