Maritime English belongs to the domain of English for specific purposes (ESP); it is the lingua franca for people engaged in international maritime transportation, whose throughput accounts for more than 80% of the goods for world trade. Hundreds of thousands of seafarers of different countries speaking different tongues work in this industry communicating in one language—English, among themselves, between labor and management, from ship to ship and between sea and shore. Quite often the captain, other senior officers and crews of one ocean-going ship are from several countries and English is the only language spoken both as a working language and for everyday conversation.
Researchers have studied the English words either from the word learning strategy, or word teaching method, or the misuse of words. However, most of these studies are based on the word-level instead of the more up-to-date and varied collostruction-level.
Based on corpus linguistics, the thesis is aimed at investigating the collexeme features of maritime English. According to the data obtained by using computer program and analysis tools, this study is expected to answer the following five questions: First, What are the high-frequency sea-related words of a Maritime English Corpus (MEC)? Second, what are the statistical results when realizing three different approaches, FYE, DELTA P and LOG, to collostructional analysis? Third, what are the relationships among the three different approaches to collostructional analysis? Fourth, is there any directionality of the DELTA P for collostructional analysis? Fifth, how do we explain the similarities and differences of near-synonyms in the MEC by using collostructional analysis?
To answer these research questions. First, by setting customary selection criteria of high-frequency sea-related words and referring to BNC Baby, I extracted 12 representative words from the compiled corpus, MEC, which is a 1,446,650 -word corpus including safety at sea, shipping news, navigational and marine engineering technology, laws, rules and regulations and documents on all the related areas of maritime transportation.
Keyword analysis is a good way to analyze representative words in a specialized corpus. After cutting off by frequency and sorting by keyness, 861 keywords are extracted for the MEC. The selected keywords are subdivided into three categories according to the UCREL semantic analysis system (USAS), namely: Means of Water Transport (M4) words, Geographical (W3) words and Overlapped Sea-related Semantic Domain (W3/M4) words. The chosen 12 words are Ship (M4), Vessel (M4), Port (M4), Harbour (M4), Sea (W3/M4), Global (W3), Worldwide (W3), Ocean (W3/M4), Coast (W3/M4), Shore (W3/M4), Marine (W3/M4) and Maritime (W3/M4). Three different association measures are adopted and directionality is considered to further analyze these words in collostructional analysis.
Since FYE is the first association measure for collostructional analysis, I use it as independent variable and DPW2C, LOG as dependent variables respectively. After conveying the collostructional analysis of 13 representative words, the different measures return different values. The relationships of FYE and DPW2C values and FYE and LOG values can be described with the Menzerath-Altmann model, which can be presented into the following model:
Where y is the (mean) size of the immediate constituents, x is the size of the construct, and a, b and c are parameters which seem to depend mainly on the level of the units under investigation (Köhler 2012). With y being the DPW2C or LOG and x FYE, a, b and c are parameters. The result is excellent for all collostructional results for the 12 representative words.
In addition, nearly all measures that have been used are bidirectional, or symmetric. However, Ellis (2007) and Ellis & Ferreira-Junior (2009) pointed out that associations are not necessarily reciprocal in strength. More technically, bidirectional/symmetric association measures conflate two probabilities that are in fact very different: P (W|C) is not the same as P (C|W). To measure how difference they are in MEC, we have defined P (W|C) as Delta P word to construction, namely DPW2C; P (C|W) as Delta P construction to word, namely DPC2W to investigate their relationships and distribution of collexemes.
Last but not least, to further understand the collostructional feature of sea-related near-synonyms in MEC, There are collexemes shared by two near-synonyms. To identify the detailed difference is of vital importance to understand the subtle usage difference of two near-synonyms. For example: There are 174 shared collexemes in collostruction “A+A/N” of Ship and Vessel. Their DPW2C ranks vary significantly, for example: As a shared collexeme, Industry ranks 1 with Ship, while ranks 320 with Vessel, which indicates we should consider Ship more important than Vessel when collocating before Industry. The detailed ranking differences are listed in appendix.
Collostructional analysis is a novel method for a nominal structure, especially for “A/N + N” structure, which is a significant grammatical structure in maritime English, compared to general English. The results includes directionality and statistical methods, which proves to have advantages to traditional frequency-based collocation analysis. For example: in the top 10 collostruction and collocation results of Ship, the results are quite different between collostructional analysis and collocation analysis, Web ranks 4, Safety ranks 5, Owner ranks 7 and Construction ranks 8 in collostruction results, while Web ranks 5, Safety ranks 4, Owner ranks 8 and Construction ranks 9 in collostruction results. Operator and Design are in top 10 collostruction results, while Port and Cargo are in top 10 collocation results. The detailed ranking differences are listed in appendix.
The idea of using collostructional analysis to investigate high-frequency sea-related words can be presented. From the perspective of traditional methods, we can only see the relationship between words to words, or structures to structures. With collostructional analysis, we can see more than words and structures. We also see their inner relationships which contribute to the study of maritime English.