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In this paper, we present a novel unsupervised algorithm for word sensedisambiguation (WSD) at the document level. Our algorithm is inspired by awidely-used approach in the field of genetics for whole genome sequencing,known as the Shotgun sequencing technique. The proposed WSD algorithm is basedon three main steps. First, a brute-force WSD algorithm is applied to shortcontext windows (up to 10 words) selected from the document in order togenerate a short list of likely sense configurations for each window. In thesecond step, these local sense configurations are assembled into longercomposite configurations based on suffix and prefix matching. The resultedconfigurations are ranked by their length, and the sense of each word is chosenbased on a voting scheme that considers only the top k configurations in whichthe word appears. We compare our algorithm with other state-of-the-artunsupervised WSD algorithms and demonstrate better performance, sometimes by avery large margin. We also show that our algorithm can yield better performancethan the Most Common Sense (MCS) baseline on one data set. Moreover, ouralgorithm has a very small number of parameters, is robust to parameter tuning,and, unlike other bio-inspired methods, it gives a deterministic solution (itdoes not involve random choices).

ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing