Experiential Meaning Breadth Variations of the English-Bahasa Indonesia Alice in Wonderland Movie Texts

Abstract

Research on experiential meaning breadth of bilingual analysis is rarely performed. The research aims at describing (1) the experiential meaning breadth variations of Alice in Wonderland English- Bahasa Indonesia movie texts, (2) the contextual factors that motivate the occurrence of meaning and realization variations, and (3) the contextual effects of the meaning and realization variations in question on the context in reference to the target readers of the texts. The research used descriptive qualitative approach to make the interpretation and the research findings. The data were taken from Alice in Wonderland (2010) movie texts then measured and validated in terms of quantitative research then analyzed through several steps: classifying the data into realized and unrealized expressions, classifying the data into the categories or degrees of variations, classifying the higher degree of each clause compared, giving number of clauses and their process type, contrasting each analysis result, and drawing conclusions. Research results indicate that the most prominent degree of experiential meaning breadth variations falls into the “lowest” category of variation with 57.54%. This means that the Target Text is closely related to the Source Text and/ or the translator of Target Text applied source- based translation as regards experiential meaning breadth complexity. It is also strengthened by the average degree in each of the analysis falling in “very low” category. The contextual factors that motivate the occurrence of the variations consist of inter- textual and situational context (field, mode, and tenor). The contextual effects in reference to the target readers are related to two aspects; the readability and the purpose of creating the texts. The Target Text is readable for the target readers of the text, target readers feel entertained by interpreting the text. The purpose for creating the Target Texts is for education and gives effects for the target readers to enrich their vocabularies and improve their English skills.

Collocation Features in Translated Texts Based on English Analogy Corpus

“…At present, automatic recognition of marked compound sentence categories is mainly based on rule-based methods and machine learning methods. Wang et al [11] combined the syntactic theory of Chinese compound sentences and the theory of relational tagging collocation to automatically identify the relational category of biological non-full-state marked compound sentences; the calculation method of semantic relatedness was used to calculate the relatedness of two words, so as to identify the relational category of compound sentences [12]. Igaab and Abdulhasan [13] used decision tree algorithms to extract features such as lexical properties to identify causal and juxtaposition relations between Chinese sentences.…”