RGCL at SemEval-2020 Task 6: Neural Approaches to DefinitionExtraction

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 717–723 Barcelona, Spain (Online), December 12, 2020. 717 RGCL at SemEval-2020 Task 6: Neural Approaches to Definition Extraction Tharindu Ranasinghe1, Alistair Plum1, Constantin Or˘asan2, Ruslan Mitkov1 1Research Group in Computational Linguistics, University of Wolverhampton, UK 2Centre for Translation Studies, University of Surrey, UK {tharindu.ranasinghe, a.j.plum, r.mitkov}@wlv.ac.uk c.orasan@surrey.ac.uk Abstract This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of- the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection. 1 Introduction Definition Extraction refers to the task in Natural Language Processing (NLP) of detecting and extracting a term and its definition in different types of text. A common use of automatic definition extraction is to help building dictionaries (Kobyli´nski and Przepi´orkowski, 2008), but it can be employed for many other applications. For example, ontology building can benefit from methods that extract definitions (Hearst, 1992; Malais´e et al., 2007), whilst the fields of definition extraction and information extraction can employ similar methodologies. It is therefore normal that there is growing interest in the task of definition extraction. This paper describes our system that participated in two of the three subtasks of Task 6 at SemEval 2020 (DeftEval), a shared task focused on definition extraction from a specialised corpus. Our method employs state-of-the-art neural architectures in combination with automatic methods which extend and clean the provided dataset. The remaining parts of this paper are structured as follows. First, we present related work in the area of definition extraction and the related field of relation extraction (Section 2). The three subtasks and the dataset provided by the task organisers are described in Section 3. Next, we describe our system (Section 4), followed by the results of the evaluation (Section 5) and a final conclusion (Section 6). 2 Related Work The first efforts related to definition extraction happened in the field of hypernym extraction, where relations that usually indicate a definition were also dealt with. This includes the X is a type of Y relation, such as salmon is a type of fish, where salmon is a hyponym of fish. Notable work includes Hearst (1992), who automatically extracts hyponyms from large amounts of unstructured text using lexico-syntactic patterns. Inspired by this approach, Malais´e et al. (2004) describe a similar method to mine definitions in French, which are then classified in terms of their semantic relations, limited to the hypernymy - synonymy relation. The approach is also used for building ontologies (Malais´e et al., 2007). The importance of the semantic relations between words for pattern-based approaches to definition extraction is highlighted in (Sierra et al., 2008). Here, the authors describe and explain definitional verbal patterns in Spanish, which they also propose to use for mining definitions. The proposed system is further presented in Alarc´on et al. (2009) and …

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