  Methods for learning word representations using large text corpora have
received much attention lately due to their impressive performance in numerous
natural language processing (NLP) tasks such as, semantic similarity
measurement, and word analogy detection. Despite their success, these
data-driven word representation learning methods do not consider the rich
semantic relational structure between words in a co-occurring context. On the
other hand, already much manual effort has gone into the construction of
semantic lexicons such as the WordNet that represent the meanings of words by
defining the various relationships that exist among the words in a language. We
consider the question, can we improve the word representations learnt using a
corpora by integrating the knowledge from semantic lexicons?. For this purpose,
we propose a joint word representation learning method that simultaneously
predicts the co-occurrences of two words in a sentence subject to the
relational constrains given by the semantic lexicon. We use relations that
exist between words in the lexicon to regularize the word representations
learnt from the corpus. Our proposed method statistically significantly
outperforms previously proposed methods for incorporating semantic lexicons
into word representations on several benchmark datasets for semantic similarity
and word analogy.
