One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

Proceedings of the 19th Conference on Computational Language Learning, pages 338–344, Beijing, China, July 30-31, 2015. c⃝2015 Association for Computational Linguistics One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction Kaveh Taghipour Department of Computer Science National University of Singapore 13 Computing Drive Singapore 117417 kaveh@comp.nus.edu.sg Hwee Tou Ng Department of Computer Science National University of Singapore 13 Computing Drive Singapore 117417 nght@comp.nus.edu.sg Abstract Supervised word sense disambiguation (WSD) systems are usually the best per- forming systems when evaluated on stan- dard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and an- notate a large number of samples and re- lease them for public use, and also to eval- uate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD sys- tem trained on our dataset achieves state- of-the-art results in standard disambigua- tion tasks and a recent word sense induc- tion task, outperforming several task sub- missions and strong baselines. 1 Introduction Identifying the meaning of a word automatically has been an interesting research topic for a few decades. The approaches used to solve this prob- lem can be roughly categorized into two main classes: Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) (Navigli, 2009). For word sense disambiguation, some systems are based on supervised machine learning algorithms (Lee et al., 2004; Zhong and Ng, 2010), while oth- ers use ontologies and other structured knowledge sources (Ponzetto and Navigli, 2010; Agirre et al., 2014; Moro et al., 2014). There are several sense-annotated datasets for WSD (Miller et al., 1993; Ng and Lee, 1996; Pas- sonneau et al., 2012). However, these datasets either include few samples per word sense or only cover a small set of polysemous words. To overcome these limitations, automatic meth- ods have been developed for annotating training samples. For example, Ng et al. (2003), Chan and Ng (2005), and Zhong and Ng (2009) used Chinese-English parallel corpora to extract sam- ples for training their supervised WSD system. Diab (2004) proposed an unsupervised bootstrap- ping method to automatically generate a sense- annotated dataset. Another example of auto- matically created datasets is the semi-supervised method used in (K¨ubler and Zhekova, 2009), which employed a supervised classifier to label in- stances. The two main contributions of this paper are as follows. First, we employ the same method used in (Ng et al., 2003; Chan and Ng, 2005) to semi-automatically annotate one million training samples based on the WordNet sense inventory (Miller, 1995) and release the annotated corpus for public use. To our knowledge, this annotated set of sense-tagged samples is the largest publicly avail- able dataset for word sense disambiguation. Sec- ond, we train an open source supervised WSD sys- tem, IMS (Zhong and Ng, 2010), using our data and evaluate it against standard WSD …

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