Multi-modal imbalanced cross-source entity alignment aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs) that encompass diverse data sources with imbalanced modality, which poses significant challenges due to the non-uniform distribution of information across different modalities. Existing methods encounter major limitations in aligning entities across MMKGs, where missing data and modality-specific inconsistencies thus create information gaps. These gaps, stemming from disparities in neighborhood structure and attribute availability, result in reduced alignment performance. To address these challenges, we propose a novel multi-modal fact knowledge generation framework to advance imbalanced cross-source entity alignment. Utilizing large language models (LLMs) for comprehensive knowledge completion, our framework enriches MMKGs by synthesizing missing neighboring entities and relational attributes, enabling precise one-to-one similarity comparisons across all relations and attributes. Specifically, neighbor entity completion generates probable neighboring entities to fill structural gaps, while attribute completion synthesizes missing relational attributes to improve alignment. The facts evaluation module assesses generated triples, ensuring that only high-quality information supports the alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms strong competitors, achieving superior entity alignment performance.

Satellite-acquired optical remote sensing imagery is extensively applied in time-critical applications like traffic surveillance and evaluation of natural disasters. However, clouds, as a common atmospheric phenomenon, frequently obscure observation. Current approaches aim to restore visibility in cloud-obscured regions, yet they typically fall short in the presence of dense cloud cover, which are exceedingly prevalent in remote sensing imagery. Alternative approaches rely on the satellite revisit cycle, frequently surpassing ten days, a duration impractical for genuine application scenarios due to target changes and bandwidth limitations. To address these issues, this paper proposes SCo-Cloud, a novel satellite constellation collaboration framework for cloud-aware onboard-computed imaging and transmission, which consists of Center-Sat and Edge-Sats. We propose onboard thin cloud removal and re-imaging region location models to locate the impact of clouds. We further design a novel multi-satellite scheduling strategy to eliminate clouds. The models above are integrated within the Center-Sat, with the nearby Edge-Sats collaborating in tandem to execute re-imaging assignments. Furthermore, to facilitate in-depth research, we have meticulously developed a cloud-covered target detection dataset. Comprehensive experiments have conclusively demonstrated that SCo-Cloud effectively surpasses the limitations inherent in current approaches, providing accurate and timely responses within the domain of Earth observation.

Text-Attributed Heterogeneous Graphs (TAHGs) integrate topological relationships with rich textual node attributes, offering expressive representations for complex multi-faceted data. While recent methods jointly leverage textual and structural information, they still face two critical limitations: (i) existing approaches are constrained to neighborhood modeling, failing to capture semantic dependencies in higher-order topologies; (ii) current techniques exhibit inadequate unified alignment strategies, limiting dynamic interaction between cross modalities. To address these challenges, we propose SATH, a self-supervised information aggregation model for TAHGs, designed to effectively leverage textual and structural information within TAHGs. SATH aggregates higher-order neighbor textual attributes through comparative learning, and dynamically aligns these attributes to higher-order topologies through a unified strategy. This approach integrates both types of information effectively, enhancing the expressiveness and discriminative capability of the learned node representations in downstream tasks. Extensive experiments on real-world datasets demonstrate that SATH significantly outperforms baseline models while eliminating the need for manual meta-path design or text feature concatenation. It also improves efficiency and scalability on large-scale TAHGs, achieving superior representation quality in TAHG-based tasks.

Graph contrastive learning (GCL) enhances the self-supervised learning capacity for graph representation learning. Nevertheless, the previous research has neglected to consider one fundamental nature of GCL -- graph contrastive learning operates as a one-shot learner, guided by the widely utilized noise contrastive estimation (e.g., the InfoNCE loss). Theoretically, to initially investigate the factors that contribute to the one-shot learner essence, we analyze the InfoNCE-based objective and derive its equivalent form of the softmax-based cross-entropy function. It is concluded that the InfoNCE-based GCL is determined to be a (2n-1)-way 1-shot classifier (n is the number of nodes). In this particular context, each sample is indicative of a unique ideational class, and each class has only one sample. Consequently, the one-shot learning nature of GCL leads to the issue of the limited self-supervised signal. To further address the above issue, we propose a One-Shot Learner in Graph Contrastive Learning (OS-GCL). Firstly, we estimate the potential probability distributions of the deterministic node features and discrete graph topology. Secondly, we develop a probabilistic message-passing mechanism to propagate probability (of feature) on probability (of topology). Thirdly, we propose the ProbNCE loss functions to contrast distributions. Extensive experimental results demonstrate the superiority of OS-GCL. To the best of our knowledge, this is the first study to examine the one-shot learning essence and the limited self-supervised signal issue of GCL.

Multi-modal relation extraction (MMRE) is a challenging task that seeks to identify relationships between entities with textual and visual attributes. However, existing methods struggle to handle the complexities posed by multiple entity pairs within a single sentence that share similar contextual information (e.g., identical text and image content). These scenarios amplify the difficulty of distinguishing relationships and hinder accurate extraction. To address these limitations, we propose the variational multi-modal hypergraph attention network (VM-HAN), a novel and robust framework for MMRE. Unlike previous approaches, VM-HAN constructs a multi-modal hypergraph for each sentence-image pair, explicitly modeling high-order intra-/inter-modal correlations among different entity pairs in the same context. This design enables a more detailed and nuanced understanding of entity relationships by capturing intricate cross-modal interactions that are often overlooked. Additionally, we introduce the variational hypergraph attention network (V-HAN). This variational attention mechanism dynamically refines the hypergraph structure, enabling the model to effectively handle the inherent ambiguity and complexity of multi-modal data. Comprehensive experiments on benchmark MMRE datasets demonstrate that VM-HAN achieves state-of-the-art performance, significantly surpassing existing methods in both accuracy and efficiency.

Satellite technology has significantly influenced our daily lives, manifested in applications such as navigation and communication. With its development, a vast amount of multimodal satellite commonsense data has been generated, thus leading to an urgent demand for conversation about satellite data. However, existing large language models suffer from prevalent hallucinations and poor comprehensibility on multimodal satellite data due to their high professional content threshold and partial information opacity. To address these issues, we propose a multimodal satellite knowledge retrieval-augmented iterative alignment framework (Sat-RIA) for satellite commonsense conversation. We first construct multi-view retrieval expert knowledge to reduce hallucinations and enhance the interpretability of responses, which incorporates the satellite expert database, satellite rule, satellite image database, and a satellite knowledge graph. We next design commonsense conversation instructions to make the answers more legible and understandable. Furthermore, the retrieval-augmented iterative alignment module refines response precision by aligning outputs with task-specific standards through multi-stage evaluations. Finally, we construct satellite multi-turn dialogue and visual question-answer datasets for a more comprehensive evaluation of satellite commonsense conversation. Experimental results demonstrate that Sat-RIA outperforms existing large language models and provides more comprehensible answers with fewer hallucinations.

Aspect sentiment triplet extraction (ASTE) aims to extract triplets composed of aspect terms, opinion terms, and sentiment polarities from given sentences. The table tagging method is a popular approach to addressing this task, which encodes a sentence into a 2-dimensional table, allowing for the tagging of relations between any two words. Previous efforts have focused on designing various downstream relation learning modules to better capture interactions between tokens in the table, revealing that a stronger capability in relation capture can lead to greater improvements in the model. Motivated by this, we attempt to directly utilize transformer layers as downstream relation learning modules. Due to the powerful semantic modeling capability of transformers, it is foreseeable that this will lead to excellent improvement. However, owing to the quadratic relation between the length of the table and the length of the input sentence sequence, using transformers directly faces two challenges: overly long table sequences and unfair local attention interaction. To address these challenges, we propose a novel Table-Transformer (T-T) for the tagging-based ASTE method. Specifically, we introduce a stripe attention mechanism with a loop-shift strategy to tackle these challenges. The former modifies the global attention mechanism to only attend to a 2-dimensional local attention window, while the latter facilitates interaction between different attention windows. Extensive and comprehensive experiments demonstrate that the T-T, as a downstream relation learning module, achieves state-of-the-art performance with lower computational costs.

The proliferation of satellite-related commonsense data on the internet. Traditional analytical methods are challenging to integrate and effectively uncover implicit knowledge within them. However, current deep learning and LLM-based approaches often struggle with errors and hallucinations when performing multi-hop reasoning in domain-specific contexts. To address these limitations, we propose a novel multi-hop reasoning framework for implicit commonsense mining. This framework aims to uncover the underlying meta-knowledge behind reasoning problems, thereby providing enhanced interpretability of the reasoning process. Specifically, we design an extraction-retrieval-principle multi-step reasoning method that generates different levels of meta-knowledge in stages to support the reasoning process effectively. We further design the mixture of expert knowledge graph construction to construct a satellite knowledge graph that supports multi-hop reasoning. Experimental results demonstrate that our approach outperforms baselines on satellite knowledge graph reasoning.

Infrared and visible image fusion (IVIF) aims to extract fine details from visible images and complementary information from infrared images. Most existing methods directly extract relevant and complementary features from each modality using neural networks, often overlooking the guidance process and the distinct frequency-domain characteristics of these features. To address this, we propose HRFusion-a novel frequency-domain framework that extracts complementary features from hybrid features using prior-constrained relevant features, effectively enhancing complementary information and reducing redundancy. In HRFusion, hybrid and relevant features are robustly extracted to guide the subsequent fusion stage. By leveraging frequency differences between complementary and relevant features, we introduce the Enhanced Complementary Frequency Network (ECFNet), which uses optimized Variational Mode Decomposition (VMD) to effectively separate and process these signals for fusion. The overall architecture is built with the proposed DTBlock, which captures both global and local features. Extensive experiments show that our method achieves state-of-the-art performance on the TNO, MSRS, M3FD, and Harvard Brain datasets, outperforming recent approaches. Code is available at https://github.com/liuuuuu777/HRFusion.

Monocular depth estimation stands as a fundamental pursuit in computer vision. Recently, some methods have attempted to introduce the text-to-image diffusion model into the domain of monocular depth estimation and achieved impressive results. However, these methods typically employ pre-defined templates as text prompts to guide the learning of denoising networks, resulting in limited flexibility and scalability. In this paper, we propose OGDepth, a diffusion-based monocular depth estimation network with object prompts generated by taking advantage of the object detection information from the scene. Specifically, we design an Object Prompt Module (OPM) to encode the object detection information into prompts that are more closely aligned with the image content, offering richer contextual information while circumventing the monotony and redundancy inherent in template-generated prompts. Moreover, we employ bounding box information for each object to filter and localize objects, enabling the model to grasp relative positional information within the scene. This facilitates the creation of a more precise depth map. Additionally, we design a Global-Local Interaction Decoder (GLID) to facilitate the mutual exchange of features at different scales, enabling efficient feature fusion. Our approach underwent rigorous experiments across multiple datasets, with results showcasing its state-of-the-art performance. Notably, on the KITTI dataset, our model achieves an RMSE of 1.967 and a REL of 0.047, and both metrics are the best among all compared methods. On the NYU Depth V2 dataset, our method achieves an RMSE score of 0.221, representing a notable 12.9% enhancement compared to the baseline method (VPD).

Knowledge graph completion (KGC) , which aims at inferring the missing fact triples, has shown an essential role in constructing a complete knowledge graph to enhance downstream applications. However, most KGC techniques require a large number of labeled training instances, and the performance drops dramatically when only a few triples are available. The primary challenge lies in the insufficient information that the few-shot annotated triples provided. Recently, several works have utilized multimodal entity contexts to enrich the entity representation, but their performance remains constrained by (1) overlooking the challenges of modality heterogeneity, (2) introducing the redundant multimodal noise of entities that is irrelevant to the corresponding relation, and (3) the difficulty in learning relation representation with only a few labeled cases. To address the above issues, we propose a novel Relation-enhanced Hierarchical Multimodal Framework (R-HMF) for the few-shot KGC. Specifically, to take the modality heterogeneity into account, we first conduct the modality-specific few-shot relation learning to capture the correlation between entities and relations within each modality. Subsequently, a multimodal fact assessment module is designed to validate the correctness of given triples by considering the entity contexts in a joint multimodal representation space. Notably, to avoid the involvement of redundant multimodal noise of entities, adaptive entity features are extracted to fit for different relations. In addition, to strengthen the relation representation in the few-shot setting, we also take full advantage of the large language models (LLMs) to generate the corresponding relation names and descriptions, which will be utilized to align different modalities as well. Extensive experimental results on two multimodal knowledge graph datasets, MM-FB15K237 and MM-DBpedia, show that our framework achieves better performance than previous state-of-the-art methods by improving 3.23% Hits@10 score under the 1-shot setting and 6.45% Hits@10 score under the 5-shot setting on average.

Graph contrastive learning (GCL) has demonstrated remarkable efficacy in graph representation learning. However, previous studies have overlooked the inherent conflict that arises when employing graph neural networks (GNNs) as encoders for node-level contrastive learning. This conflict pertains to the partial incongruity between the feature aggregation mechanism of graph neural networks and the embedding distinction characteristic of contrastive learning. Theoretically, to investigate the location and extent of the conflict, we analyze the participation of message-passing from the gradient perspective of InfoNCE loss. Different from contrastive learning in other domains, the conflict in GCL arises due to the presence of certain samples that contribute to both the gradients of positive and negative simultaneously under the manner of message passing, which are opposite optimization directions. To further address the conflict issue, we propose a practical framework called ReGCL, which utilizes theoretical findings of GCL gradients to effectively improve graph contrastive learning. Specifically, two gradient-based strategies are devised in terms of both message passing and loss function to mitigate the conflict. Firstly, a gradient-guided structure learning method is proposed in order to acquire a structure that is adapted to contrastive learning principles. Secondly, a gradient-weighted InfoNCE loss function is designed to reduce the impact of false negative samples with high probabilities, specifically from the standpoint of the graph encoder. Extensive experiments demonstrate the superiority of the proposed method in comparison to state-of-the-art baselines across various node classification benchmarks.

Cross-domain Aspect Sentiment Triplet Extraction (ASTE) aims to extract fine-grained sentiment elements from target domain sentences by leveraging the knowledge acquired from the source domain. Due to the absence of labeled data in the target domain, recent studies tend to rely on pre-trained language models to generate large amounts of synthetic data for training purposes. However, these approaches entail additional computational costs associated with the generation process. Different from them, we discover a striking resemblance between table-filling methods in ASTE and two-stage Object Detection (OD) in computer vision, which inspires us to revisit the cross-domain ASTE task and approach it from an OD standpoint. This allows the model to benefit from the OD extraction paradigm and region-level alignment. Building upon this premise, we propose a novel method named Table-Filling via Mean Teacher (TFMT). Specifically, the table-filling methods encode the sentence into a 2D table to detect word relations, while TFMT treats the table as a feature map and utilizes a region consistency to enhance the quality of those generated pseudo labels. Additionally, considering the existence of the domain gap, a cross-domain consistency based on Maximum Mean Discrepancy is designed to alleviate domain shift problems. Our method achieves state-of-the-art performance with minimal parameters and computational costs, making it a strong baseline for cross-domain ASTE.

Social event detection refers to extracting relevant message clusters from social media data streams to represent specific events in the real world. Social event detection is important in numerous areas, such as opinion analysis, social safety, and decision-making. Most current methods are supervised and require access to large amounts of data. These methods need prior knowledge of the events and carry a high risk of leaking sensitive information in the messages, making them less applicable in open-world settings. Therefore, conducting unsupervised detection while fully utilizing the rich information in the messages and protecting data privacy remains a significant challenge. To this end, we propose a novel social event detection framework, ADP-SEMEvent, an unsupervised social event detection method that prioritizes privacy. Specifically, ADP-SEMEvent is divided into two stages, i.e., the construction stage of the private message graph and the clustering stage of the private message graph. In the first stage, an adaptive differential privacy approach is used to construct a private message graph. In this process, our method can adaptively apply differential privacy based on the events occurring each day in an open environment to maximize the use of the privacy budget. In the second stage, to address the reduction in data utility caused by noise, a novel 2-dimensional structural entropy minimization algorithm based on optimal subgraphs is used to detect events in the message graph. The highlight of this process is unsupervised and does not compromise differential privacy. Extensive experiments on two public datasets demonstrate that ADP-SEMEvent can achieve detection performance comparable to state-of-the-art methods while maintaining reasonable privacy budget parameters.

Cross-lingual event detection (CLED) is a challenging information extraction task in which a model is trained in one language and evaluated in another. Most recent methods attack CLED by aligning source and target language representations based on fine-tuning multilingual pre-trained language models. However, they need to modify all the model parameters and store a complete copy for each source-target language pair, which is resource-intensive and requires significant memory. In contrast, prefix-tuning is a more lightweight alternative, but it relies solely on the labeled source language data during training, limiting its performance. To address the above problems, we propose a novel framework for CLED with Language-agnostic Prototypical Prefix-Learning (L-APPLE), which can integrate language-agnostic event information with prefix-tuning. In detail, inspired by vanilla prompt methods, L-APPLE divides the prefix into two parts: one optimized as continuous word embeddings while the other generated with cross-lingual aligned event prototypes. Meanwhile, we employ language alignment with contrastive learning to acquire cross-lingual aligned event prototypes, and finally, parameters are optimized using both task and alignment loss. The evaluation of public CLED benchmarks demonstrates that L-APPLE achieves significant improvements in CLED with only less than 0.1% of the parameters optimized compared to previous fine-tuning methods.

Multimodal Named Entity Recognition (MNER) models typically require a significant volume of labeled data for effective training to extract relations between entities. In real-world scenarios, we frequently encounter unseen relation types. Nevertheless, existing methods are predominantly tailored for complete datasets and are not equipped to handle these new relation types. In this paper, we introduce the Few-shot Multimodal Named Entity Recognition (FMNER) task to address these novel relation types. FMNER trains in the source domain (seen types) and tests in the target domain (unseen types) with different distributions. Due to limited available resources for sampling, each sampling instance yields different content, resulting in data bias and alignment problems of multimodal units (image patches and words). To alleviate the above challenge, we propose a novel Multimodal causal Intervention graphs (MOUSING) model for FMNER. Specifically, we begin by constructing a multimodal graph that incorporates fine-grained information from multiple modalities. Subsequently, we introduce the Multimodal Causal Intervention Strategy to update the multimodal graph. It aims to decrease spurious correlations and emphasize accurate correlations between multimodal units, resulting in effectively aligned multimodal representations. Extensive experiments on two multimodal named entity recognition datasets demonstrate the superior performance of our model in the few-shot setting.

Social media has become ubiquitous for connecting with others, staying updated with news, expressing opinions, and finding entertainment. However, understanding the intention behind social media posts remains challenging due to the implicit and commonsense nature of these intentions, the need for cross-modality understanding of both text and images, and the presence of noisy information such as hashtags, misspelled words, and complicated abbreviations. To address these challenges, we present MIKO, a Multimodal Intention Knowledge DistillatiOn framework that collaboratively leverages a Large Language Model (LLM) and a Multimodal Large Language Model (MLLM) to uncover users' intentions. Specifically, our approach uses an MLLM to interpret the image, an LLM to extract key information from the text, and another LLM to generate intentions. By applying MIKO to publicly available social media datasets, we construct an intention knowledge base featuring 1,372K intentions rooted in 137,287 posts. Moreover, We conduct a two-stage annotation to verify the quality of the generated knowledge and benchmark the performance of widely used LLMs for intention generation, and further apply MIKO to a sarcasm detection dataset and distill a student model to demonstrate the downstream benefits of applying intention knowledge.

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs.

Text-video retrieval is a challenging task that aims to identify relevant videos given textual queries. Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content. Previous works primarily focus on aligning the query and the video by finely aggregating word-frame matching signals. Inspired by the human cognitive process of modularly judging the relevance between text and video, the judgment needs high-order matching signal due to the consecutive and complex nature of video contents. In this paper, we propose chunk-level text-video matching, where the query chunks are extracted to describe a specific retrieval unit, and the video chunks are segmented into distinct clips from videos. We formulate the chunk-level matching as n-ary correlations modeling between words of the query and frames of the video and introduce a multi-modal hypergraph for n-ary correlation modeling. By representing textual units and video frames as nodes and using hyperedges to depict their relationships, a multi-modal hypergraph is constructed. In this way, the query and the video can be aligned in a high-order semantic space. In addition, to enhance the model's generalization ability, the extracted features are fed into a variational inference component for computation, obtaining the variational representation under the Gaussian distribution. The incorporation of hypergraphs and variational inference allows our model to capture complex, n-ary interactions among textual and visual contents. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the text-video retrieval task.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors.

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level event graph to associate sentence nodes of different types and adopting a document-awared graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a new schema of argument roles, with a type-awared parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks, especially at least 2.51% and 1.12% improvement of the event trigger identification and argument role classification sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods.

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED , leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE . In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN.

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of topology-imbalance from a global view of the supervision information distribution in terms of under-reaching and over-squashing, which motivates two quantitative metrics as measurements. In light of our analysis, we propose a novel position-aware graph structure learning framework named PASTEL, which directly optimizes the information propagation path and solves the topology-imbalance issue in essence. Our key insight is to enhance the connectivity of nodes within the same class for more supervision information, thereby relieving the under-reaching and over-squashing phenomena. Specifically, we design an anchor-based position encoding mechanism, which better incorporates relative topology position and enhances the intra-class inductive bias by maximizing the label influence. We further propose a class-wise conflict measure as the edge weights, which benefits the separation of different node classes. Extensive experiments demonstrate the superior potential and adaptability of PASTEL in enhancing GNNs' power in different data annotation scenarios

Event extraction is a ftask for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument’s role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification.

Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.