ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
This addresses the problem of disambiguating word meanings in text for natural language processing applications, presenting an incremental improvement with a deterministic and parameter-robust approach.
The paper tackles word sense disambiguation at the document level with an unsupervised algorithm inspired by DNA shotgun sequencing, achieving better performance than other state-of-the-art unsupervised methods, sometimes by a large margin, and outperforming the Most Common Sense baseline on one dataset.
In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).