Digital social media platforms frequently contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. While cognitive-behavioral fixation has been extensively studied in psychology, methods for computationally detecting and evaluating such fixation remain underexplored. To address this gap, we propose a novel framework for assessing cognitive-behavioral fixation by analyzing users’ multimodal social media engagement patterns. Specifically, we introduce a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach, laying the groundwork for scalable computational analysis of cognitive fixation. All code in this project is publicly available for research purposes at https://github.com/Liskie/cognitive-fixation-evaluation.

“This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotationquality and reducing costs. Unlike previous studies targeting simpler tasks, this study con-tends with the complexities of label interdependencies in sequence labeling. The proposedalgorithm utilizes a Combinatorial Multi-Armed Bandit (CMAB) approach for worker selec-tion, and a cost-effective human feedback mechanism. The challenge of dealing with imbal-anced and small-scale datasets, which hinders offline simulation of worker selection, is tack-led using an innovative data augmentation method termed shifting, expanding, and shrink-ing (SES). Rigorous testing on CoNLL 2003 NER and Chinese OEI datasets showcased thealgorithm’s efficiency, with an increase in F1 score up to 100.04% of the expert-only base-line, alongside cost savings up to 65.97%. The paper also encompasses a dataset-independenttest emulating annotation evaluation through a Bernoulli distribution, which still led to animpressive 97.56% F1 score of the expert baseline and 59.88% cost savings. Furthermore,our approach can be seamlessly integrated into Reinforcement Learning from Human Feed-back (RLHF) systems, offering a cost-effective solution for obtaining human feedback. All re-sources, including source code and datasets, are available to the broader research community athttps://github.com/blcuicall/nlp-crowdsourcing.”

“基于自然语言生成技术的聊天机器人ChatGPT能够快速生成回答,但目前尚未对机器作答所使用的语言与人类真实语言在哪些方面存在差异进行充分研究。本研究提取并计算159个语言特征在人类和ChatGPT对中文开放域问题作答文本中的分布,使用随机森林、逻辑回归和支持向量机(SVM)三种机器学习算法训练人工智能探测器,并评估模型性能。实验结果表明,随机森林和SVM均能达到较高的分类准确率。通过对比分析,研究揭示了两种文本在描述性特征、字词常用度、字词多样性、句法复杂性、语篇凝聚力五个维度上语言表现的优势和不足。结果显示,两种文本之间的差异主要集中在描述性特征、字词常用度、字词多样性三个维度。”

This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French. We propose a transformer-based multitasking framework to explore the task. The framework integrates multiple embedding architectures through the cross-attention mechanism, and captures the structure of glosses through a masking language model objective. Additionally, we also investigate a simple but effective model ensembling strategy to further improve the robustness. The evaluation results show the effectiveness of our solution. We release our code at: https://github.com/blcuicall/SemEval2022-Task1-DM.

The construct of linguistic complexity has been widely used in language learning research. Several text analysis tools have been created to automatically analyze linguistic complexity. However, the indexes supported by several existing Chinese text analysis tools are limited and different because of different research purposes. CTAP is an open-source linguistic complexity measurement extraction tool, which prompts any research purposes. Although it was originally developed for English, the Unstructured Information Management (UIMA) framework it used allows the integration of other languages. In this study, we integrated the Chinese component into CTAP, describing the index sets it incorporated and comparing it with three linguistic complexity tools for Chinese. The index set includes four levels of 196 linguistic complexity indexes: character level, word level, sentence level, and discourse level. So far, CTAP has implemented automatic calculation of complexity characteristics for four languages, aiming to help linguists without NLP background study language complexity.