摘要 :
Author study is conducted in the field of research on discourse markers that have attracted many scholars' attention both at home and abroad in recent years.
The authorfirst review the previous researches on discourse markers...
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Author study is conducted in the field of research on discourse markers that have attracted many scholars' attention both at home and abroad in recent years.
The authorfirst review the previous researches on discourse markers, particularly the four approaches: Coherence Approach, Grammatical-Pragmatic Approach, Relevance Approach, and Sociolinguistic Approach. Based on Fraser's (1996, 1998) model and Liao Qiuzhong's(1986) model for analyzing discourse markers, I then describe the classification of discourse markers in both Chinese and English.
Next Author discuss the functions of oh and its translation into Chinese. Eleven Chinese novels and nine English novels and movie scripts are examined. Relevance Theory provides us a new perspective to understand such functions served by oh as recognition marker, receipt marker, answer marker, emotion marker and repair marker.Besides Relevance Theory informs us that translation should be optimally relevant to target readers or audience without imposing unnecessary processing effort burden upon them. In analyzing the translation of oh as emotion marker into Chinese, I point out that its translation should not be the same always employing the Chinese expression , but has to be different in different contexts. And oh may not be translated at all in some circumstances.
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摘要 :
Much has been written about humor and even sarcasm automatic recognition on Twitter. Nevertheless，the task of classifying humorous tweets according to the type of humor has not been confronted so far，as far as we know.
This...
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Much has been written about humor and even sarcasm automatic recognition on Twitter. Nevertheless，the task of classifying humorous tweets according to the type of humor has not been confronted so far，as far as we know.
This research is aimed at applying semi-supervised classification algorithms and other NLP algorithms to the challenging task of automatically identifying the type of humor appearing in messages on Twitter.
The different methods，algorithms，tools and classifiers used are discussed，as well as the specific difficulty encountered due to the very subjective nature of humor and the informal language applied in tweets.
It is shown that the discussed methods improve the accuracy of classification by up to 5%above the baseline which is ZeroR，the algorithm that classifies all instances to the majority class.
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