Target Word Masking for Location Metonymy Resolution

Proceedings of the 28th International Conference on Computational Linguistics, pages 3696–3707 Barcelona, Spain (Online), December 8-13, 2020 3696 Target Word Masking for Location Metonymy Resolution Haonan Li♠ Maria Vasardani♦ Martin Tomko♥ Timothy Baldwin♠ ♠ School of Computing and Information Systems, The University of Melbourne ♥ Department of Infrastructure Engineering, The University of Melbourne ♦ Department of Geospatial Science, RMIT University haonanl5@student.unimelb.edu.au, maria.vasardani2@rmit.edu.au tomkom@unimelb.edu.au, tb@ldwin.net Abstract Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that our approach achieves the state-of-the-art on 5 datasets, surpassing conventional BERT models and benchmarks by a large margin. We also show that our approach generalises well to unseen data. 1 Introduction Metonymy is a widespread linguistic phenomenon, in which a thing or concept is referred to by the name of something closely associated with it. It is an instance of figurative language that can be easily understand by humans through association, but is hard for machines to interpret. For example, in They read Shakespeare, it is “the works of Shakespeare” that we are referring to, not the playwright himself. Existing named entity recognition (NER) and word sense disambiguation (WSD) systems have no explicit metonymy detection. This is an issue as named entities and other lexical items are often used metonymically. For instance, Germany in the context of Germany lost in the semi-final refers to “the national German sports team”, different to the context I live in Germany which the term is used literally. NER systems generally tag both as location without recognition of the metonymic usage in the first, and WSD systems are tied to word sense inventories and generally don’t handle metonyms and other sense extensions well (Markert and Nissim, 2009). Intuitively, metonym resolution should improve NER and WSD, something we explore in this paper. Metonymy resolution is the task of determining whether a potentially metonymic word (“PMW”) in a given context is used metonymically or not. It has been shown to be an important component of many NLP tasks, including machine translation (Kamei and Wakao, 1992), question answering (Stallard, 1993), anaphora resolution (Markert and Hahn, 2002), geographical information retrieval (Leveling and Hartrumpf, 2008), and geo-tagging (Monteiro et al., 2016; Gritta et al., 2018). Conventional approaches to metonymy resolution have made extensive use of taggers, parsers, lexicons, and corpus-derived or hand-crafted features (Nissim and Markert, 2003; Farkas et al., 2007; Nastase et al., 2012). These either rely on NLP pre-processors that potentially introduce errors, or require external domain-specific resources. Recently, deep contextualised word embeddings (Peters et al., 2018) and pre-trained language models (Devlin et al., 2019) have been shown to benefit many NLP tasks, and part of our interest in this work is how to best apply these approaches to metonymy resolution. While we include experiments for other types of metonymy, a particular focus of this …

Content truncated to 500 words