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This case study reports a pedagogical approach informed by Concept-Based Language Instruction, which aimed to orient English as a second language learners to a systematic conceptual understanding of rhetorical skills in five-paragraph essay writing. The 12-week pedagogical intervention focused on teaching the organizational structure of the five-paragraph essay and modes of persuasion through SCOBAs (Schemas for a Complete Orienting Basis of Actions) in a test preparation course for the writing tasks in the Test of English as a Foreign Language (TOEFL). Six participants completed three writing assignments and one analytic essay of a writing sample across the 12 weeks and received one-on-one individual tutoring sessions in the last week. The results demonstrate a holistic quality improvement in students’ five-paragraph essays and an observable improvement in the use of rhetorical appeals of Ethos and Logos strategies. In addition, the student-generated SCOBAs showed learners’ abilities to intentionally manipulate the SCOBAs to serve their internalization processes of the target concepts.

Joint Multimodal Aspect-based Sentiment Analysis (JMASA) is a significant task in the research of multimodal fine-grained sentiment analysis, which combines two subtasks: Multimodal Aspect Term Extraction (MATE) and Multimodal Aspect-oriented Sentiment Classification (MASC). Currently, most existing models for JMASA only perform text and image feature encoding from a basic level, but often neglect the in-depth analysis of unimodal intrinsic features, which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features. Given this problem, we propose a Text-Image Feature Fine-grained Learning (TIFFL) model for JMASA. First, we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text, which addresses the context interference problem of identifying different aspect terms. Then, the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive, which addresses the ambiguous semantic extraction problem during image feature learning. Thereby, the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced. Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA, MATE and MASC, thus validating the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

Inductive link prediction (ILP) in knowledge graphs (KGs) aims to predict missing links between entities that were not seen during the training phase. Recent some subgraph-based methods have shown some advancements, but they all overlook the relational semantics between entities during subgraph extraction. To overcome this limitation, we introduce a novel inductive link prediction model named SASILP (Structure and Semantic Inductive Link Prediction), which comprehensively incorporates relational semantics in both subgraph extraction and node initialization processes. The model employs a random walk strategy to calculate the structural scores of neighboring nodes and utilizes an enhanced graph attention network to determine their semantic scores. By integrating both structural and semantic scores, SASILP strategically selects key nodes to form a subgraph. Furthermore, the subgraph is initialized with a node initialization technique that integrates information about neighboring relations. The experiments conducted on benchmark datasets demonstrate that SASILP outperforms state-of-the-art methods on inductive link prediction tasks, and verify the effectiveness of our approach. [ABSTRACT FROM AUTHOR]