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).

本文介绍了一种新颖的无监督算法，用于文档级别的词义消歧。该方法受到了遗传学领域中用于全基因组测序的Shotgun测序技术的启发，其基于三个主要步骤：在文档中选择较短的上下文窗口，并应用暴力消歧算法生成每个窗口的可能义项列表；基于后缀和前缀匹配将本地义项配置组成长的复合配置；通过投票方案选择每个单词的义项。我们将该算法与其他最先进的无监督词义消歧算法进行了比较，并证明了其更好的性能，有时甚至有很大的差距。

ShotgunWSD: 一种类似 DNA 测序的无监督全局词义消歧算法