English learning with Web 2.0 – An investigation into Chinese undergraduates’ technology (non)use and perspectives

Moving beyond the ‘Web 2.0’ and ‘digital native’ rhetoric, this thesis investigates what Chinese undergraduates are actually doing (and not doing) with online tools and applications to learn English outside the classroom and, why they choose to do so. Particular attention is paid to their use and non-use of the social web in their English learning context. A sociocultural framework is adopted to understand learners’ behaviours surrounding digital technology. This theoretical position puts learners at the centre of their English learning and decision-making regarding technology use. It guides the exploration into the contextually mediated choices and practices of English learners in the so-called ‘2.0’ era.\ud \ud Data collection for this mixed sequential study took place during the 2010-2011 academic year. The data consist of a survey of 1,485 undergraduates and semi-structured interviews with 49 participants in two large Chinese universities. The data demonstrate a few embryonic signs of how Chinese undergraduates try to ‘escape’ from their English learning context with online technologies. However, a vast majority of the participants chose to use the web as an instrument to handle their academic duties. When it comes to English learning, their use of Web 2.0 is limited and mostly non-interactive and unspectacular.\ud \ud In light of the above, the thesis goes on to consider a number of contextual factors that appear to constrain participants’ use of technology – not least the discourses of English learning and the cultural artefact of exams. Based on these findings, the thesis provides a framework that challenges existing beliefs about (language) learning with Web 2.0, and that contributes to understandings of how context mediates language learners’ behaviours surrounding digital technologies. The thesis concludes by suggesting ways of maximizing the learning potential of Web 2.0 for English learners at Chinese universities

Pattern memory analysis based on stability theory of cellular neural networks

AbstractIn this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples

SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER

Named Entity Recognition is the task to locate and classify the entities in the text. However, Unlabeled Entity Problem in NER datasets seriously hinders the improvement of NER performance. This paper proposes SCL-RAI to cope with this problem. Firstly, we decrease the distance of span representations with the same label while increasing it for different ones via span-based contrastive learning, which relieves the ambiguity among entities and improves the robustness of the model over unlabeled entities. Then we propose retrieval augmented inference to mitigate the decision boundary shifting problem. Our method significantly outperforms the previous SOTA method by 4.21% and 8.64% F1-score on two real-world datasets.Comment: COLING 202

SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition

Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete annotation noise or indiscriminately handle two types of noise with the same strategy. In this paper, we argue that the different causes of two types of noise bring up the requirement of different strategies in model architecture. Therefore, we propose the SANTA to handle these two types of noise separately with (1) Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate decision boundary shifting problem caused by incomplete annotation and a noise-tolerant loss to improve the robustness. Benefiting from our separate tailored strategies, we confirm in the experiment that the two types of noise are well mitigated. SANTA also achieves a new state-of-the-art on five public datasets.Comment: Findings of ACL202