Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features. However, these are also some obstacles imposed by using those machine learning methods, in need of a large amount of manually labeled data. To avoid such limitations, we could replace human annotation with distant supervision, however there remain a technical challenge on the error label issue caused by ignoring the entities that are not included in the vocabulary, which should be addressed to achieve the effective NER model. Then, we propose a novel back-labeling approach and integrate it into a tagging scheme, especially, we apply this scheme to handle the NER task in traditional Chinese medicine (TCM) field. In addition, we discuss how to use distant supervision methods to achieve better performance of the NER model. We conduct some experiments and verify that our scheme can effectively improve the entity recognition on the basis of distant supervision.

Background: Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective: Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervised learning methods. Therefore, we utilized distantly supervised named entity recognition (NER) to respond to the challenge. Methods: We propose a span-level distantly supervised NER approach to extract TCM medical entity. It utilizes the pretrained language model and a simple multilayer neural network as classifier to detect and classify entity. We also designed a negative sampling strategy for the span-level model. The strategy randomly selects negative samples in every epoch and filters the possible false-negative samples periodically. It reduces the bad influence from the false-negative samples. Results: We compare our methods with other baseline methods to illustrate the effectiveness of our method on a gold-standard data set. The F1 score of our method is 77.34 and it remarkably outperforms the other baselines. Conclusions: We developed a distantly supervised NER approach to extract medical entity from TCM clinical records. We estimated our approach on a TCM clinical record data set. Our experimental results indicate that the proposed approach achieves a better performance than other baselines.

Traditional dense video captioning predominantly focuses on edited exocentric footage. These videos are filmed from an external perspective and generally feature distinct transitions between different events, as exemplified in edited instructional videos. However, such videos do not genuinely reflect the way we perceive our real lives. Instead, we observe the world from an egocentric viewpoint and witness only continuous unedited footage. To facilitate further research, we introduce a new topic: Egocentric Vehicle Dense Video Captioning, in classic vehicle driving scenarios. This is a multi-modal, multi-task subject endeavor for a comprehensive understanding of untrimmed, egocentric driving videos. It consists of three sub-tasks that concentrate on event localization, captioning, and vehicle state estimation separately. To accomplish these tasks, it is necessary to deal with at least three challenges: extracting ego-motion relevant information, describing driving behavior and analyzing the underlying rationale, as well as resolving the boundary ambiguity problem. In response, we devise corresponding solutions, including a vehicle ego-motion learning strategy and a novel adjacent contrastive learning strategy, which effectively address the aforementioned issues. We validate our method by conducting extensive experiments on the BDD-X dataset, all of which show promising results and achieve new state-of-the-art performance on most metrics, which proves the effect of our approach.

The social relation is a fundamental attribute of human beings in daily life. The ability of humans to form large organizations and institutions stems directly from our complex social networks. Therefore, understanding social relationships in the context of multimedia is crucial for building domain-specific or general artificial intelligence systems. The key to reason social relations lies in understanding the human interactions between individuals through multimodal representations such as action and utterance. However, due to video editing techniques and various narrative sequences in videos, two individuals with social relationships may not appear together in the same frame or clip. Additionally, social relations may manifest in different levels of granularity in video expressions. Previous research has not effectively addressed these challenges. Therefore, this paper proposes a Multi-Granularity Relation Graph Aggregation Framework (MGRG) to enhance the inference ability for social relation reasoning in multimedia content, like video. Different from existing methods, our method considers the paradigm of jointly inferring the relations by constructing a social relation graph. We design a hierarchical multimodal relation graph illustrating the exchange of information between individuals' roles, capturing the complex interactions at multi-levels of granularity from fine to coarse. In MGRG, we propose two aggregation modules to cluster multimodal features in different granularity layer relation graph, considering temporal aspects and importance. Experimental results show that our method generates a logical and coherent social relation graph and improves the performance in accuracy.