The exponential growth of information on the Internet is a big challenge for
information retrieval systems towards generating relevant results. Novel
approaches are required to reformat or expand user queries to generate a
satisfactory response and increase recall and precision. Query expansion (QE)
is a technique to broaden users' queries by introducing additional tokens or
phrases based on some semantic similarity metrics. The tradeoff is the added
computational complexity to find semantically similar words and a possible
increase in noise in information retrieval. Despite several research efforts on
this topic, QE has not yet been explored enough and more work is needed on
similarity matching and composition of query terms with an objective to
retrieve a small set of most appropriate responses. QE should be scalable,
fast, and robust in handling complex queries with a good response time and
noise ceiling. In this paper, we propose Xu, an automated QE technique, using
high dimensional clustering of word vectors and Datamuse API, an open source
query engine to find semantically similar words. We implemented Xu as a command
line tool and evaluated its performances using datasets containing news
articles and human-generated QEs. The evaluation results show that Xu was
better than Datamuse by achieving about 88% accuracy with reference to the
human-generated QE.Comment: Accepted to IEEE COMPSAC 201