مجید عسگری

The usefulness of using literature in language teaching has always been acknowledged by language teaching researchers who have studied the said issue from different aspects and in various contexts. The present investigation tries to discover if the use of authentic and simplified literary texts could improve learners’ reading achievement, and then check if the impacts of these two types of texts on the learners’ reading were significantly different. Employing a quasi-experimental design, the researchers included ninety high school students in the data collection process. These students were taught in three groups including two experimental and one control, comparing the two types of literary texts with the usual course materials. Using pre-tests and post-tests scores, the learners' performance was compared, then t-test and ANOVA were employed to analyze the significance of the differences found between the means. It appeared that the use of literary texts could assist high school learners improve their performance in reading compared to the use of usual course materials. The findings further confirmed that the use of simplified literary texts could improve the learners’ reading better than the authentic literary texts. The findings are specifically important in teaching reading, which serve as the basic skill for building the required knowledge in EFL contexts. Literature-based language teaching seems to enjoy the power to change and improve language teaching programs through presenting on-demand and productive teaching materials and add to learners’ life awareness besides the language knowledge.

The present investigation attempted to see if using literary texts could lead into any change in learners' interest or perception toward literature. The research also attempted to find students' perception on using literary content in language learning based on learners' opinions. The investigation finally sought to discover the way learners' interest in literature changed as a result of using literary texts as the instructional content. The Research employed a mixed-method design to answer the questions proposed in the study. After analyzing data through t-test, Gain-score comparison and thematic analysis, the results showed that using literary content worked as useful and constructive for learners in language learning and life objectives. The results further confirmed that learners' interest level improved positively due to using simplified literary texts, whereas, the positive role could not be seen in using authentic literary content. Accordingly, content selection from literature along with considering learners' life needs are able to make language learning more attractive to learners. The results also support that developing newer books with literary texts can provide learners with different knowledge like cultural awareness, social knowledge or historical background, which seem to be urgent life needs in today's modern society.

The task of extracting semantic relations from raw data is called relation extraction. One of the most important fields in open information extraction is the automatically extraction of relations in any domain, especially in web mining. There are many works and approaches for relation extraction in English and other languages. Some of these approaches are based on parsing trees. Dependency parsing in the Persian language is difficult and time-consuming, since Persian is a low resource language and has also a dependency grammar and lexical structure, which affects also the speed of relations extraction in Persian. In this paper we will introduce a fast relation extraction method in Persian called RePersian. RePersian is dependent on part-of-speech (POS) tags of a sentence and special relation patterns, which are extracted by analyzing sentence structures in Persian. For finding relation patterns, RePersian searches through POS-tags that are given in regular expression forms. By matching the correct POS pattern to a relation pattern, RePersian extracts the semantic relations in a sentence. We appraise RePersian in two different scenarios on the Dadegan Persian dependency tree dataset. RePersian had on average the precisions 78.05%, 80.4% and 54.85% in finding the first argument on a relation, the second argument and the right relation between them.