Corpus-based Extraction And Analysis Of Spoken English Expressions In Movies And TV Series

With the enlargement of exchange of culture in China, more and more English original movies and TV series have been introduced into China. They have been quite popular with Chinese audiences and widely used in the English teaching and learning.Since language in English movies and TV series is a kind of style between spoken English and written English, and closer to spoken English, it is characterized by typical spoken English and full of considerable spoken English expressions, but the major concern in my study is how to refine and analyze them efficiently.This study aims at refining the spoken English expressions in English movies and TV series and analyzing them from different perspectives such as collocation, semantic meaning and pragmatic functions. First, some English movies and TV series which are popular with Chinese audiences are collected in the corpus of English Movies and TV series scripts (CEMTVS), which contains1,437,396words. And out of the need for this research, the raw text in CEMTVS is modified in two ways, that is to say, it is respectively annotated with part-of-speech tagging (POS tagging) and pragmatic functions. Second, the methodology of corpus is used to refine and analyze the following spoken English expressions:1) individual-word-based extraction and analysis namely, the common nouns, common verbs, common adjectives, common adverbs, common interjections;2) chunk-based extraction and analysis, namely, chunks without or with a definite word are extracted as well as the non-sequence chunks;3) annotation-based extraction and analysis, namely the expressions used to fulfill different pragmatic functions;4) intuition-based extraction and analysis. Meanwhile corpus tools are used in this study are AntConc3.2.1w and TreeTagger.Through this study, the major findings are as follows:1) the noun thing is in collocation with another noun in front to express the thing of the same or similar category; the verb get is used in structures get__to do and get_doing, which is common in spoken English to express the meaning make somebody to do something; the adjective good comes out frequently in good for__, meaning it is good or great for__all is common as an adverb to take place of completely or very; well as an exclamation is used frequently to express different kinds of feelings.2) in many cases,/mean is used as a discourse marker and I mean is likely to co-occur with another discourse marker well or you know; a good many examples of kind of and sort of are used as the vagueness and when kind of, sort of refer to a particular variety or type, we can find some informal examples; the non-sequence chunk__n’t mean to__, but__is frequently used in conversation to express politeness or show the respect to the listener hoping the listener not getting you misunderstood.3) Hi there, What’s up, how’s it going?, How (are) you doing? and so on are used to greet somebody; some expressions are used to remind somebody or draw the listener’s attention like look, listen, you know what, guess what,(I’ll/I) tell you what, for the record, for your information (often abbreviated to FYI); some expressions are used to express the speaker’s subjective opinions and judgment over something like I take it, it (just) seems to me,1think/believe/figure/guess/suppose/assume, with all due respect.4) usually the English tag question is followed after the declarative sentence, and the basic sentence patterns are:positive statement+negative tag question; negative statement+positive tag question, but sometimes the declarative sentence and tag question are both used in a positive way and tag questions can also be put behind imperative sentences; when expressing our desire, we can use expressions like want, would like or feel like; when expressing the subjective feelings, we usually use the word feel and the subject of feel can be a nonliving substance, besides, feel like can be used to express the subjective feelings and the structures that it is used in are usually divided into two kinds:feel like+NP, feel(s) like+(that) clause.