词性（POS）标签的一部分在自然语言处理（NLP）中起重要作用。它的应用程序可以在许多NLP任务中找到，例如命名实体识别，句法解析，依赖性解析和文本块。在本文进行的调查中，我们利用了两个广泛使用的工具包的技术，即Clearnlp和Stanford Pos Tagger，以及为越南人开发了两个新的POS标签，然后将它们与三个著名的越南标签者进行比较，即vntagger和rdrpostagger。我们进行系统的比较，以找出具有最佳性能的标签器。我们还设计了一个新功能集来衡量统计标签者的性能。我们由Stanford Tagger和新功能集构建的新标签者可以在标记准确性方面胜过所有其他当前的越南标签。此外，我们还分析了某些功能对统计标签者的性能的感情。最后，实验结果还表明，基于转换的标签器Rdrpostagger的运行速度明显快于任何其他统计标签器。

从Bosselut等人从彗星方法开始。（2019），直接从预训练的语言模型产生勤杂朗语言，最近受到了重大关注。令人惊讶的是，目前没有这种方式没有产生的勤义知识的物化资源是公开的。本文填补了这种差距，并利用了物化资源在精确和召回方面对这种方法的潜力进行了详细分析。此外，我们确定了常见问题的情况，并通过物化资源启用了概述使用案例。我们认为，这些资源的可用性对于领域的进步是重要的，因为它可以实现所产生的知识，进一步分析其优势和劣势。

关于概念及其属性的常识知识（CSK）有助于AI应用程序。诸如ConceptNet之类的先前作品已经编译了大型CSK集合。但是，它们的表现力限制在主题性 - 预处理（SPO）的三联元中，对p和o的s和字符串的简单概念。与先前的作品相比，CSK断言具有精致的表现力和更好的精度和回忆。 Ascent ++通过用子组和方面捕获复合概念，以及用语义方面的主张来捕获复合概念。后者对于表达断言和进一步预选赛的时间和空间有效性至关重要。此外，Ascent ++将开放信息提取（OpenIE）与典型性和显着性分数的明智清洁和排名相结合。对于高覆盖范围，我们的方法挖掘到具有广泛的Web内容的大规模爬网C4中。通过人类判断的评估显示了上升++ Kb的卓越质量，以及对QA支持任务的外部评估强调了Ascent ++的好处。可以在https://ascentpp.mpi-inf.mpg.de/上访问Web界面，数据和代码。

关于概念及其属性的常识知识（CSK）对AI应用程序（例如强大的聊天机器人）有用。诸如ConceptNet，Tuplekb和其他人之类的先前作品汇编了大型CSK集合，但在其表现力上限制了主题性主体对象（SPO）三倍（SPO）三元组，其中s和p和Onolithic的简单概念是P和O。这些项目都优先考虑精确精度。或召回，但几乎不能调和这些互补目标。本文介绍了一种称为Ascent的方法，以自动建立一个大规模的CSK断言的知识库（KB），具有高级表现力，并且比先前的作品更好，并且具有更好的精度和回忆。通过捕获子组和方面的复合概念，以及通过语义方面的主张来捕获复合概念，超越了三倍。后者对于表达断言和进一步预选赛的时间和空间有效性很重要。 Ascent使用语言模型将开放信息提取与明智的清洁结合在一起。内在评估显示了上升KB的较高规模和质量，QA支持任务的外部评估强调了上升的好处。可以在https://ascent.mpi-inf.mpg.de/上找到Web界面，数据和代码。

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.