Academic journal article International Journal of English Linguistics

Using Sketch Engine to Investigate Synonymous Verbs

Academic journal article International Journal of English Linguistics

Using Sketch Engine to Investigate Synonymous Verbs

Article excerpt


Synonymy is an important yet intricate linguistic feature in the field of lexical semantics. Using the 100 million-word British National Corpus (BNC) as data and the software Sketch Engine (SkE) as analyzing tool, this study examines the usage differences between raise and increase, two synonymous verbs notorious for their complex semantic and syntactic usage patterns. In addition to examining the collocates of the verbs, the study also investigated the syntactic patterns that the verbs typically occupy in the sentence structure and their functional implications. The data analysis yields an informative delineation of the internal semantic structure of the synonym set. The results also show the need for the corpus approach to go beyond collocational analysis in the study of synonymous verbs. The limitations of using SkE to extract and disambiguate synonyms are also addressed. This paper ends by discussing the pedagogical implications that this research may have when the results are introduced into the classroom.

Keywords: synonymy, lexical semantics, collocation, colligation, BNC, Sketch Engine

1. Introduction

Synonymy, or semantic equivalence, is an important yet intricate linguistic feature in the field of lexical semantics. Synonyms are not completely interchangeable; rather, they differ in shades of meaning and vary in their connotations, implications, and register (DiMarco et al., 1993). Any natural language consists of a considerable number of synonymous words. Due to historical reasons, English is particular rich in synonyms, which enables English speakers "to convey meanings more precisely and effectively for the right audience and context" (Liu & Espino, 2012, p. 198), but also constitute a thorny area for EFL (English as a Foreign Language) learners because of their subtle nuances and variations in meaning and usage.

It thus comes no surprise that an important aspect of English linguistics is to find the proper measures of automatically identifying and extracting synonyms (Peirsman, Geeraerts, & Speelman, 2015) and of distinguishing one word from its synonyms or near-synonyms (Hanks, 1996; Biber et al., 1998; Gries, 2001; Xiao & McEnery, 2006; Divjak, 2006; Gries & Otani, 2010; Liu, 2010). Although the two orientations of researching synonyms are equally important, I will in this paper focus more attention on the second one. The main purposes of this study are methodological, in that I would like to discover what the relative strengths and weaknesses of using Sketch Engine to research synonyms are, and what their relative scope of applicability is.

The rest of this paper is structured as follows. In the next section, I will give an overview of related work by introducing corpus studies of lexical semantics in the first place, and then discussing corpus-based automatic extraction and discrimination of synonymous words. Section 3 will present corpus data and tools used in this study. The results of this study are presented and discussed in Section 4, where I show the success of Sketch Engine in researching synonyms. The final section summarizes major findings and pointers for future research.

2. Related Work

2.1 Corpus Studies of Lexical Semantics

In the field of lexical semantics, there are a number of closely related key issues such as "How do we know what words mean? What evidence do we have? Is this evidence observable and objective? How can large text collections (corpora) be used to study what words mean?" (Stubbs, 2001, p. 4). For centuries, researchers, language teachers, and dictionary makers have used both their own intuitions and also attested uses of words, often in the form of thousands of quotations from printed books. However, it is only since the mid-1980s that corpus methods have been able to provide evidence about word meaning by searching across large text collections.

The approach of using corpus evidence to study meaning of words or phrases is often labeled as corpus semantics or empirical semantics, and the most active and influential scholars are called neo-Firthian corpus linguists. …

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