Using corpora to inform teaching practice in German Studies

Abstract

Learner corpus research seeks to describe and thereby better understand learner acquisition, thus informing better teaching practice and creating an important bond between corpus linguistics and second/foreign language (L2) research. While much research exists for the study of L2 English, there is little research for the study of L2 German. This study explores the implementation of a corpus-based writing course in German studies at Rhodes University in South Africa with students at third-year level who were learning to write longer texts that present and defend an opinion. In order to evaluate the success of the writing course in teaching specific academic collocations, a diachronic collection of learner writing was undertaken. Written assignments were used to create a learner corpus, RUDaF (Rhodes University Deutsch als Fremdsprache). The creation of a learner corpus of German before and during the writing course served as a reflective tool to aid in the evaluation and improvement of corpus-based teaching materials. This is exemplified through the students' use of three specific opinion-related everydayacademic German collocations before and after the writing course. The learner corpus revealed a marked improvement in the use of taught collocations before and during the writing course. These methods provide insights for other language educators who wish to implement corpus-based methods for improving writing in a second or foreign language, and for all educators wanting to assess improvements in student writing.

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

“…Reference [18] compared various deep learning models on 30,000 German news corpus, in which a two-layer stacked LSTM network successfully overcame the shortcomings of gradient explosion and gradient disappearance of ordinary RNNs with strong sequence memory capability and obtained the best accuracy of 86.3 percent. Reference [19] used character-level embedded CNNs to extract local features of text and expand the training corpus by replacing synonyms for restaurant and product German reviews and improved the accuracy by 2.4%. Although all the above studies attempted to build a deep model with multiple hidden layers, the network structure was relatively simple and homogeneous, which not only failed to effectively combine local and sequence features of the text to extract deeper sentiment information but also was limited by the black-box nature of the deep learning model, which made it difficult to make full use of the characteristics of the German language and common sense sentiment.…”

“…In German tweets, there are also a variety of swear words and slang words, which, together with many nondirective expressions, increase the granularity of the sentiment factor affecting the sentence, and traditional sentiment dictionaries alone cannot meet the demand. In this paper, we refer to the literature [18,19] and construct a dictionary of expletive slang containing three types of words or phrases, including words that compare each other to animals or filthy and useless objects (e.g., Deine Mutter ist tot), expletives related to sexuality or sexual organs (e.g., Fick dich! ), and various words used for insults, curses, or blasphemy (e.g., Du bist ein Narr).…”