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Document-level event extraction aims to extract event-related information from an unstructured document composed of multiple sentences. Existing approaches are not effective due to the challenge of event arguments that are scattered across multi-sentences and they pay more attention to the coreference relationship between entity mentions. However, it is an extremely common phenomenon that there are a large number of crossing sentences pronouns that referring to entity mentions. These pronouns also contain rich semantic information related to events in the document. Therefore, there is still a challenge that how to effectively construct the mention–pronoun coreference relationship and better learn the rich semantic entities representations for DEE. Aiming at the above problems, we propose a novel document-level multi-task learning approach based on coreference-aware dynamic heterogeneous graph network for event extraction, named DMCGEE. Specifically, first, an information enhancement extractor module is constructed to effectively capture multi-types of semantic association information for mentions representations. Second, a mention–pronoun coreference resolution method is proposed to capture mention–pronoun coreference resolution pairs, and a coreference-aware dynamic heterogeneous graph network is constructed to help sentences and mentions representations to focus on the effective global related information, thereby improving the performance of DMCGEE. Experiments show that DMCGEE outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue from a single perspective, disregarding the impact of the other part. Therefore, we propose a multiple information-aware recurrent reasoning network (MIRER). Firstly, the sequence information is smoothly sent to multiple local information layers for fine-grained feature extraction through a BiLSTM-connected hybrid CNN group method. Secondly, to obtain global semantic features that are speaker-, context-, and temporal-sensitive, we design a speaker-aware temporal reasoning heterogeneous graph to characterize interactions between utterances spoken by different speakers, incorporating different types of nodes and meta-relations with node-edge-type-dependent parameters. We also design a dual-task temporal reasoning heterogeneous graph to realize the semantic-level and prediction-level self-interaction and interaction, and we constantly revise and improve the label in the process of dual-task recurrent reasoning. MIRER fully integrates context-level features, fine-grained features, and global semantic features, including speaker, context, and temporal sensitivity, to better simulate conversation scenarios. We validated the method on two public dialogue datasets, Mastodon and DailyDialog, and the experimental results show that MIRER outperforms various existing baseline models. [ABSTRACT FROM AUTHOR]

Knowledge representation of scientific paper data is a problem to be solved,and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem.This paper proposes an unsupervised cluster-level scientific paper heterogeneous graph node representation learning method(UCHL),aiming at obtaining the representation of nodes (authors,institutions,papers,etc.) in the heterogeneous graph of scientific papers.Based on the heterogeneous graph representation,this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes,that is,the relationship between paper and paper.Experiments results show that the proposed method achieves excellent performance on multiple evaluation metrics on real scientific paper datasets.

The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue from a single perspective, disregarding the impact of the other part. Therefore, we propose a multiple information-aware recurrent reasoning network (MIRER). Firstly, the sequence information is smoothly sent to multiple local information layers for fine-grained feature extraction through a BiLSTM-connected hybrid CNN group method. Secondly, to obtain global semantic features that are speaker-, context-, and temporal-sensitive, we design a speaker-aware temporal reasoning heterogeneous graph to characterize interactions between utterances spoken by different speakers, incorporating different types of nodes and meta-relations with node-edge-type-dependent parameters. We also design a dual-task temporal reasoning heterogeneous graph to realize the semantic-level and prediction-level self-interaction and interaction, and we constantly revise and improve the label in the process of dual-task recurrent reasoning. MIRER fully integrates context-level features, fine-grained features, and global semantic features, including speaker, context, and temporal sensitivity, to better simulate conversation scenarios. We validated the method on two public dialogue datasets, Mastodon and DailyDialog, and the experimental results show that MIRER outperforms various existing baseline models.

Fashion compatibility prediction aims to provide a compatibility score for a set of fashion combinations, making an effort to meet people's needs for clothing matching in daily life. One difficulty of this problem is that whether the fashion items are compatible depends on a variety of attribute factors, such as design style, material, color. Existing works mostly use the attributes as the item feature representations or integrate attribute features into the compatibility modeling process as auxiliary information in a simple way. These works ignore the fine-grained attributes that can play a role as bridges in establishing the potential compatibility relationship between fashion items. To better take advantage of fashion attributes information, we propose an Attribute-aware Heterogeneous Graph Network (AHGN), which combines the fashion items and their attributes as a heterogeneous graph to integrate the heterogeneous information in the fashion compatibility problem. While fusing the fine-grained attribute information into the item nodes representation, the attributes act as bridges to transfer the characteristics of potential compatible fashion items into the target item node. Previous methods of calculating outfit score by item pairs without considering the effect of outfit compositions. An outfit-based item pairs scoring method is proposed. The feature representation of item pairs is adjusted by integrating the overall outfit information to consider the influence of the outfit environment on the item pairs. We conduct experiments on three tasks (fill-in-the-blank, compatibility prediction, and fashion item retrieval) in two real-world fashion datasets: Fashion32 and IQON3000. Experimental results demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Name disambiguation has long been a significant issue in many fields, such as literature management and social analysis. In recent years, methods based on graph networks have performed well in name disambiguation, but these works have rarely used heterogeneous graphs to capture relationships between nodes. Heterogeneous graphs can extract more comprehensive relationship information so that more accurate node embedding can be learned. Therefore, a Dual-Channel Heterogeneous Graph Network is proposed to solve the name disambiguation problem. We use the heterogeneous graph network to capture various node information to ensure that our method can learn more accurate data structure information. In addition, we use fastText to extract the semantic information of the data. Then, a clustering method based on DBSCAN is used to classify academic papers by different authors into different clusters. In many experiments based on real datasets, our method achieved high accuracy, which proves its effectiveness.

Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of disease have revealed that circRNA can be considered as a potential biomarker. Therefore, understanding the association of circRNA and diseases can help to forecast some disorders of life activities. However, traditional biological experimental methods are time-consuming. The most common method for circRNA-disease association prediction on the basis of machine learning can avoid this, which relies on diverse data. Nevertheless, topological information of circRNA and disease usually is not involved in these methods. Moreover, circRNAs can be associated with diseases through miRNAs. With these considerations, we proposed a novel method, named THGNCDA, to predict the association between circRNAs and diseases. Specifically, for a certain pair of circRNA and disease, we employ a graph neural network with attention to learn the importance of its each neighbor. In addition, we use a multilayer convolutional neural network to explore the relationship of a circRNA-disease pair based on their attributes. When calculating embeddings, we introduce the information of miRNAs. The results of experiments show that THGNCDA outperformed the SOTA methods. In addition, it can be observed that our method gives a better recall rate. To confirm the significance of attention, we conducted extensive ablation studies. Case studies on Urinary Bladder and Prostatic Neoplasms further show THGNCDA's ability in discovering known relationships between circRNA candidates and diseases., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Name disambiguation has long been a significant issue in many fields, such as literature management and social analysis. In recent years, methods based on graph networks have performed well in name disambiguation, but these works have rarely used heterogeneous graphs to capture relationships between nodes. Heterogeneous graphs can extract more comprehensive relationship information so that more accurate node embedding can be learned. Therefore, a Dual-Channel Heterogeneous Graph Network is proposed to solve the name disambiguation problem. We use the heterogeneous graph network to capture various node information to ensure that our method can learn more accurate data structure information. In addition, we use fastText to extract the semantic information of the data. Then, a clustering method based on DBSCAN is used to classify academic papers by different authors into different clusters. In many experiments based on real datasets, our method achieved high accuracy, which proves its effectiveness. [ABSTRACT FROM AUTHOR]