標(biāo)題: Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings [打印本頁] 作者: interleukins 時(shí)間: 2025-3-21 18:25
書目名稱Database Systems for Advanced Applications影響因子(影響力)
書目名稱Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Database Systems for Advanced Applications被引頻次
書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名
書目名稱Database Systems for Advanced Applications年度引用
書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名
書目名稱Database Systems for Advanced Applications讀者反饋
書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名
作者: 不可知論 時(shí)間: 2025-3-21 21:13
Information Networks Based Multi-semantic Data Embedding for Entity Resolutions. Recently, deep learning techniques have been substantially applied to entity resolution. We focus on entity resolution with graph based multi-semantic data embedding. In ER, data with attributes cannot be well represented by common word embeddings from natural language processing. In this work, d作者: Insatiable 時(shí)間: 2025-3-22 02:40 作者: innate 時(shí)間: 2025-3-22 06:17
Empowering Transformer with Hybrid Matching Knowledge for Entity Matchingoken-level pairwise interactions within the input sequence. In this paper, we propose a novel entity matching framework named GTA. GTA enhances Transformer for relational data representation by injecting additional hybrid matching knowledge. The hybrid matching knowledge is obtained via graph contra作者: regale 時(shí)間: 2025-3-22 12:21 作者: collagen 時(shí)間: 2025-3-22 15:39
Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper, we propose a Story Heterogeneous Graph Network (SHGN) to explicitly model both the informa作者: collagen 時(shí)間: 2025-3-22 18:16 作者: 萬靈丹 時(shí)間: 2025-3-22 23:44 作者: prolate 時(shí)間: 2025-3-23 02:13 作者: 外觀 時(shí)間: 2025-3-23 06:23
Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Grapthe internal and external influence at either element level or fact level. However, the multi-granularity information is essential for TKG modeling and the connection in between is also under-explored. In this paper, we propose the method that .ligning-internal .egularity and external .nfluence of .作者: interlude 時(shí)間: 2025-3-23 09:53 作者: osteocytes 時(shí)間: 2025-3-23 17:49
HRG: A Hybrid Retrieval and Generation Model in Multi-turn Dialogues difficult to maintain the context consistency by using only a few previous turns of the dialogue indiscriminately. Except for the context information, we can retrieve additional candidates from historical contexts, according to semantic similarity. Therefore, in this paper, we integrate the histor作者: crucial 時(shí)間: 2025-3-23 21:55
: A Faithful Contrastive Framework for?Response Generation in?TableQA Systems. Due to the complex syntax of SQL and matching issues with table content, this task is prone to produce factual errors. In this paper, we propose ., a .ithfu. .trastive generation framework to improve the factual correctness of generated responses. . forces the generation model to identify examples作者: EXPEL 時(shí)間: 2025-3-24 01:16 作者: Aspiration 時(shí)間: 2025-3-24 03:06
SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classificationsification are primarily based on proposing new architectures and fine-tuning them on pre-trained Convolutional Neural Networks. Recently, learning transferable visual models from natural language supervision has shown great success in zero-shot settings due to the easily accessible web-scale traini作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-24 10:34
Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel .ulti-.ask .earning framework with .hared .ulti-channel .nteractions (MTL-SMI), which is composed of 作者: 陶器 時(shí)間: 2025-3-24 11:00
Knowing What I Don’t Know: A Generation Assisted Rejection Framework in Knowledge Base Question Answty, some recent research works introduce external text for KBQA. However, such external information is not always readily available. We argue that it is critical for a KBQA system to know whether it lacks the knowledge to answer a given question. In this paper, we present a novel .neration .ssisted 作者: Choreography 時(shí)間: 2025-3-24 17:49 作者: patella 時(shí)間: 2025-3-24 18:59 作者: GONG 時(shí)間: 2025-3-25 00:39 作者: bypass 時(shí)間: 2025-3-25 05:56
Open-Domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusionnsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory modu作者: lanugo 時(shí)間: 2025-3-25 11:29 作者: lobster 時(shí)間: 2025-3-25 13:49 作者: 迎合 時(shí)間: 2025-3-25 19:02
Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Grapxternal random perturbation. Finally, according to the above obtained multi-granular information of rich features, ARIM-TE conducts alignment for them in both structure and semantics. Experimental results show that ARIM-TE outperforms current state-of-the-art KGE models on several TKG link predictio作者: reserve 時(shí)間: 2025-3-25 21:52 作者: 被詛咒的人 時(shí)間: 2025-3-26 01:49 作者: homocysteine 時(shí)間: 2025-3-26 04:47
SimEmotion: A Simple Knowledgeable Prompt Tuning Method for Image Emotion Classificationnd . are introduced to enrich text semantics, forming knowledgeable prompts and avoiding considerable bias introduced by fixed designed prompts, further improving the model’s ability to distinguish emotion categories. Evaluations on four widely-used affective datasets, namely, Flickr and Instagram (作者: HALO 時(shí)間: 2025-3-26 10:26 作者: 起草 時(shí)間: 2025-3-26 13:27
Hanging on to the Imperial Pastand images and generate texts. It also involves cross-modal learning to enhance interactions between images and texts. The experiments verify our method in appropriateness, informativeness, and emotion consistency.作者: 幸福愉悅感 時(shí)間: 2025-3-26 19:36
https://doi.org/10.1007/978-3-031-35411-3ension. Moreover, we design two auxiliary tasks to implicitly capture the sentiment trend and key events lie in the context. The auxiliary tasks are jointly optimized with the primary story ending generation task in a multi-task learning strategy. Extensive experiments on the ROCStories Corpus show 作者: 行乞 時(shí)間: 2025-3-26 21:10 作者: commensurate 時(shí)間: 2025-3-27 03:40 作者: 一瞥 時(shí)間: 2025-3-27 09:20 作者: 音樂會(huì) 時(shí)間: 2025-3-27 10:12
Jeremy Kearney,Catherine Donovanxternal random perturbation. Finally, according to the above obtained multi-granular information of rich features, ARIM-TE conducts alignment for them in both structure and semantics. Experimental results show that ARIM-TE outperforms current state-of-the-art KGE models on several TKG link predictio作者: 能夠支付 時(shí)間: 2025-3-27 14:23 作者: inspired 時(shí)間: 2025-3-27 21:21 作者: 可轉(zhuǎn)變 時(shí)間: 2025-3-27 22:23
Vladimir I. Danilov,Alexander I. Sotskovnd . are introduced to enrich text semantics, forming knowledgeable prompts and avoiding considerable bias introduced by fixed designed prompts, further improving the model’s ability to distinguish emotion categories. Evaluations on four widely-used affective datasets, namely, Flickr and Instagram (作者: ARY 時(shí)間: 2025-3-28 05:48 作者: 學(xué)術(shù)討論會(huì) 時(shí)間: 2025-3-28 10:03
Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki作者: Alienated 時(shí)間: 2025-3-28 13:15
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263395.jpg作者: 輕快走過 時(shí)間: 2025-3-28 16:20 作者: Admire 時(shí)間: 2025-3-28 21:50
https://doi.org/10.1007/978-3-031-00129-1artificial intelligence; computational linguistics; computer hardware; computer networks; computer syste作者: 反抗者 時(shí)間: 2025-3-28 23:49
978-3-031-00128-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: ineffectual 時(shí)間: 2025-3-29 06:20
Hanging on to the Imperial Pastnd visual signals. Image-grounded emotional response generation (IgERG) tasks requires chatbots to generate a response with the understanding of both textual contexts and speakers’ emotions in visual signals. Pre-training models enhance many NLP and CV tasks and image-text pre-training also helps mu作者: 切掉 時(shí)間: 2025-3-29 10:48
Jacob Davidsen,Paul McIlvenny,Thomas Rybergs. Recently, deep learning techniques have been substantially applied to entity resolution. We focus on entity resolution with graph based multi-semantic data embedding. In ER, data with attributes cannot be well represented by common word embeddings from natural language processing. In this work, d作者: Ergots 時(shí)間: 2025-3-29 15:19
https://doi.org/10.1007/978-3-031-35411-3y totally change the underlying logic. Currently, existing datasets for MWP task contain limited samples which are key for neural models to learn to disambiguate different kinds of local variances in questions and solve the questions correctly. In this paper, we propose a set of novel data augmentat作者: 樂意 時(shí)間: 2025-3-29 16:39 作者: Modicum 時(shí)間: 2025-3-29 19:58
Jacob Davidsen,Paul McIlvenny,Thomas Rybergs the volume of the databases has been increased considerably over the past few decades. Making a good use of a survey paper of the research topic can vastly lower the difficulty but there may be no survey paper in some emerging research topics due to the rapid development. In this work, we propose 作者: 嫻熟 時(shí)間: 2025-3-29 23:54
https://doi.org/10.1007/978-3-031-35411-3 challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper, we propose a Story Heterogeneous Graph Network (SHGN) to explicitly model both the informa作者: 世俗 時(shí)間: 2025-3-30 06:28
Wolfgang A. Halang,Alexander D. Stoyenkoes based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of作者: 愉快嗎 時(shí)間: 2025-3-30 09:45 作者: custody 時(shí)間: 2025-3-30 13:41
Haijun Zeng,Zhisheng Li,Zhuo Zhangdifficult to identify. This not only affects the decision-making of consumers, but also seriously undermines the fairness of the consumption market. Most existing research ignored the abundant metadata in reviews and its effective combination with the review text. In this paper, we firstly collect r作者: 誘導(dǎo) 時(shí)間: 2025-3-30 17:50
Jeremy Kearney,Catherine Donovanthe internal and external influence at either element level or fact level. However, the multi-granularity information is essential for TKG modeling and the connection in between is also under-explored. In this paper, we propose the method that .ligning-internal .egularity and external .nfluence of .作者: aesthetic 時(shí)間: 2025-3-30 22:34
Catherine Donovan,Jeremy Kearneyttracted much attention from researchers. Most of these approaches focus on constructing high-quality positives, while only using other in-batch sentences for negatives which are insufficient for training accurate discriminative boundaries. In this paper, we demonstrate that high-quality negative re作者: 愛社交 時(shí)間: 2025-3-31 02:38 作者: Servile 時(shí)間: 2025-3-31 09:05
Marianna Manca,Cecilia Vergnano. Due to the complex syntax of SQL and matching issues with table content, this task is prone to produce factual errors. In this paper, we propose ., a .ithfu. .trastive generation framework to improve the factual correctness of generated responses. . forces the generation model to identify examples作者: LEVER 時(shí)間: 2025-3-31 09:38 作者: 嫻熟 時(shí)間: 2025-3-31 16:07 作者: PAEAN 時(shí)間: 2025-3-31 17:36
Rational Choice Under Convex Conditions but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel .ulti-.ask .earning framework with .hared .ulti-channel .nteractions (MTL-SMI), which is composed of 作者: 不能平靜 時(shí)間: 2025-4-1 00:45
Vladimir I. Danilov,Alexander I. Sotskovty, some recent research works introduce external text for KBQA. However, such external information is not always readily available. We argue that it is critical for a KBQA system to know whether it lacks the knowledge to answer a given question. In this paper, we present a novel .neration .ssisted 作者: Mindfulness 時(shí)間: 2025-4-1 02:04
Rational Choice Under Convex Conditionsly. Therefore, the global search for similar pixels helps to infer the pixel value of a certain location, which can be used for the extraction of image details. In this paper, a two-stage image decomposition framework (TS-PLNSS) is proposed. It combines the pixel-level nonlocal self-similarity prior作者: 胰臟 時(shí)間: 2025-4-1 07:48
