標(biāo)題: Titlebook: Experimental IR Meets Multilinguality, Multimodality, and Interaction; 9th International Co Patrice Bellot,Chiraz Trabelsi,Nicola Ferro Con [打印本頁(yè)] 作者: Intermediary 時(shí)間: 2025-3-21 20:02
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction影響因子(影響力)
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction影響因子(影響力)學(xué)科排名
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction被引頻次
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction被引頻次學(xué)科排名
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction年度引用
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction年度引用學(xué)科排名
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction讀者反饋
書(shū)目名稱Experimental IR Meets Multilinguality, Multimodality, and Interaction讀者反饋學(xué)科排名
作者: VOC 時(shí)間: 2025-3-21 20:46
Multi-view Personality Profiling Based on Longitudinal Data may help in understanding the reasons behind one’s behavior and his/her motivation in undertaking new life challenges. In this study, we take the first step towards solving the problem of automatic personality profiling. Specifically, we propose the idea of fusing multi-source multi-modal temporal 作者: anagen 時(shí)間: 2025-3-22 04:22 作者: 熟練 時(shí)間: 2025-3-22 04:37 作者: offense 時(shí)間: 2025-3-22 09:05
Learning-to-Rank and Relevance Feedback for Literature Appraisal in Empirical Medicineting this evidence can be both challenging and time-consuming for researchers conducting systematic reviews. Technologically assisted review (TAR) aims to assist this process by finding as much relevant information as possible with the least effort. Toward this, we present an incremental learning me作者: insidious 時(shí)間: 2025-3-22 14:55
Combining Tags and Reviews to Improve Social Book Search PerformanceSocial information retrieval is one of the areas that aim to use this social information to improve the information retrieval performance. This information can be textual, like tags or reviews, or non textual like ratings, number of likes, number of shares, etc. In this paper, we focus on the integr作者: insidious 時(shí)間: 2025-3-22 18:41 作者: 單挑 時(shí)間: 2025-3-23 00:53 作者: 暖昧關(guān)系 時(shí)間: 2025-3-23 03:47 作者: abracadabra 時(shí)間: 2025-3-23 06:15 作者: 缺乏 時(shí)間: 2025-3-23 10:45
Addressing Social Bias in Information Retrievalotypes. In this position paper, I argue that IR researchers and in particular, evaluation communities such as CLEF, can and should address such concerns. Using as a guide the . recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social bias作者: 植物茂盛 時(shí)間: 2025-3-23 15:40
Analyzing and Visualizing Translation Patterns of Wikidata Propertiesr, achieving a multilingual experience is a rather challenging task for a highly evolving site like Wikidata built with the collaboration of contributors from around the world. It is important to let the contributors analyse and discover how properties are translated and also detect potential proble作者: Mammal 時(shí)間: 2025-3-23 20:11 作者: 思想流動(dòng) 時(shí)間: 2025-3-24 00:38
Simply the Best: Minimalist System Trumps Complex Models in Author Profiling, Spanish, Arabic and Portuguese) of Twitter users with very high accuracy. All our attempts at improving performance by including more data, smarter features, and employing more complex architectures plainly fail. In addition, we experiment with joint and multitask modelling, but find that they are作者: buoyant 時(shí)間: 2025-3-24 03:48 作者: abreast 時(shí)間: 2025-3-24 08:26 作者: epicondylitis 時(shí)間: 2025-3-24 13:14 作者: LAVE 時(shí)間: 2025-3-24 15:38 作者: Graduated 時(shí)間: 2025-3-24 19:38
https://doi.org/10.1007/978-3-030-97785-6e retrieval effectiveness. After several experiments, on the CLEF social book search collection, we concluded that combining the results obtained from two separate indexes and two models with specific parameters for tags and reviews gives good results compared to when using a single index and a single model.作者: acclimate 時(shí)間: 2025-3-24 23:51 作者: fidelity 時(shí)間: 2025-3-25 04:12 作者: 疏忽 時(shí)間: 2025-3-25 10:51 作者: 脆弱帶來(lái) 時(shí)間: 2025-3-25 14:06
Character N-Grams for Detecting Deceptive Controversial Opinionsn using psycholinguistic features. Our results indicate that this representation is able to capture relevant information about style and content useful for this task. This fact allows us to conclude that the proposed one is a competitive text representation with a good trade-off between simplicity and performance.作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-25 18:10 作者: 付出 時(shí)間: 2025-3-25 23:15 作者: 過(guò)份艷麗 時(shí)間: 2025-3-26 01:58 作者: Oscillate 時(shí)間: 2025-3-26 06:29
Albino Bacolla PhD,Robert D. Wells PhDns. Using as a guide the . recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social biases in IR systems and in particular, search engines.作者: 來(lái)自于 時(shí)間: 2025-3-26 10:32
On the Genetic Origin of the Turks,ors from around the world. It is important to let the contributors analyse and discover how properties are translated and also detect potential problems. This article focuses on developing a tool for understanding and visualizing the translation patterns of Wikidata.作者: 凌辱 時(shí)間: 2025-3-26 13:09 作者: Blood-Vessels 時(shí)間: 2025-3-26 17:43
S. Backiyarani,C. Anuradha,S. Umadata in our computational “PersonalLSTM” framework for automatic user personality inference. Experimental results show that incorporation of multi-source temporal data allows for more accurate personality profiling, as compared to non-temporal baselines and different data source combinations.作者: ANT 時(shí)間: 2025-3-26 21:02 作者: Sedative 時(shí)間: 2025-3-27 03:19 作者: 埋伏 時(shí)間: 2025-3-27 06:54
Deep Multimodal Classification of Image Types in Biomedical Journal Figuression methods are analyzed as well as data augmentation approaches. The proposed system is validated on the ImageCLEF 2013 and 2016 figure and subfigure classification tasks, largely improving the currently best performance from 83.5% to 93.7% accuracy and 88.4% to 89.0% respectively.作者: LAITY 時(shí)間: 2025-3-27 11:47 作者: intoxicate 時(shí)間: 2025-3-27 14:16
Using R Markdown for Replicable Experiments in Evidence Based Medicinelevant medical documents, given an information need, with the least effort. We study how lay people, students of a master degree in languages in this case, can help the retrieval system in finding more relevant documents by means of a query rewriting approach.作者: deficiency 時(shí)間: 2025-3-27 18:59
Automatic Query Selection for?Acquisition and Discovery of?Food-Drug Interactionsy that aims to automatically identify scientific publications that describe food-drug interactions from a database of biomedical literature. We make use of an expert curated corpus of food-drug interactions to analyse different methods for query selection and we propose a high-recall approach based on feature selection.作者: collateral 時(shí)間: 2025-3-28 01:06
Ganapathy Kuyyamudi Nanaiah,Sujay Rakshitlicity, it is fast and can be scaled to large datasets. Experimental results on several real-world datasets demonstrate that the proposed method has overall better performance compared to several deterministic, random, or order-sensitive methods in terms of clustering quality and runtime.作者: Decline 時(shí)間: 2025-3-28 02:46 作者: Hallmark 時(shí)間: 2025-3-28 08:52 作者: Motilin 時(shí)間: 2025-3-28 11:01 作者: Manifest 時(shí)間: 2025-3-28 17:24
Allelic ,-sensing and Imprinting,his task was divided into two subtasks: multi-drug resistance prediction, and TB type classification. The participation in this task showed the strength of our model, leading to best results in the competition for multi-drug resistance detection (AUC?=?0.5825) and good results in the TB type classification (Cohen’s Kappa coefficient?=?0.1623).作者: 音的強(qiáng)弱 時(shí)間: 2025-3-28 22:09 作者: 使增至最大 時(shí)間: 2025-3-29 01:26 作者: conflate 時(shí)間: 2025-3-29 05:26
Effects of Language and Terminology of?Query Suggestions on the Precision of?Health Searchesese suggestions tends to perform better than a system without them. On specific groups of users, clicking on suggestions has positive effects on precision while using them as sources of new terms has the opposite effect. This suggests that a personalized suggestion system might have a good impact on precision.作者: Endometrium 時(shí)間: 2025-3-29 07:51
Simply the Best: Minimalist System Trumps Complex Models in Author Profilingn average accuracy of 0.86 on the test set, with performance on sub-tasks ranging from 0.68 to 0.98. These were the best results achieved at the competition overall. To allow lay people to easily use and see the value of machine learning for author profiling, we also built a web application on top our models.作者: 新星 時(shí)間: 2025-3-29 12:13
Textured Graph-Based Model of the Lungs: Application on Tuberculosis Type Classification and Multi-dhis task was divided into two subtasks: multi-drug resistance prediction, and TB type classification. The participation in this task showed the strength of our model, leading to best results in the competition for multi-drug resistance detection (AUC?=?0.5825) and good results in the TB type classification (Cohen’s Kappa coefficient?=?0.1623).作者: Pander 時(shí)間: 2025-3-29 16:12 作者: 偉大 時(shí)間: 2025-3-29 19:46 作者: 小溪 時(shí)間: 2025-3-30 03:16
0302-9743 gual information access. In addition to this, 10 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions. The papers 978-3-319-98931-0978-3-319-98932-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: painkillers 時(shí)間: 2025-3-30 07:56
Learning-to-Rank and Relevance Feedback for Literature Appraisal in Empirical Medicinetors derived from word embedding methods such as Word2Vec and Doc2Vec. We test our approach using the dataset provided by the Task II of CLEF eHealth 2017 and we empirically compare it with other approaches participated in the task.作者: Harpoon 時(shí)間: 2025-3-30 10:46
Plant Classification Based on Gated Recurrent Unitualizing the learned attention maps. To our knowledge, this is the first study to venture into such dependencies modeling and interpret the respective neural net for plant classification. Finally, we show that our proposed method outperforms the conventional CNN approach on the PlantClef2015 benchma作者: 阻礙 時(shí)間: 2025-3-30 12:41 作者: ALERT 時(shí)間: 2025-3-30 18:28 作者: Kindle 時(shí)間: 2025-3-31 00:14
https://doi.org/10.1007/978-3-319-98932-7artificial intelligence; biocommunications; bioinformatics; biomedical technologies; classification; imag作者: Campaign 時(shí)間: 2025-3-31 00:56 作者: fluoroscopy 時(shí)間: 2025-3-31 05:44 作者: Dissonance 時(shí)間: 2025-3-31 11:49
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/318846.jpg
