Companion Proceedings of the Web Conference 2021最新文献_第4页

Emotion-Aware Event Summarization in Microblogs 微博中的情绪感知事件总结

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452311

R. Panchendrarajan, W. Hsu, M. Lee

引用次数: 4

Cross-city Analysis of Location-based Sentiment in User-generated Text 用户生成文本中基于位置的情感跨城市分析

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451889

Christopher Stelzmüller, Sebastian Tanzer, M. Schedl

引用次数: 2

Extraction and Evaluation of Statistical Information from Social and Behavioral Science Papers 从社会和行为科学论文中提取和评估统计信息

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451363

Sree Sai Teja Lanka, S. Rajtmajer, Jian Wu, C. Lee Giles

引用次数: 5

FastSNG: The Fastest Social Network Dataset Generator FastSNG:最快的社交网络数据集生成器

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3458604

Binbin Wang, Chaokun Wang, Hao Feng

引用次数: 1

LocWeb2021 Workshop – Chair’s Welcome: Eleventh International Workshop on Location and the Web at The Web Conference 2021 LocWeb2021研讨会-主席欢迎:第十一届“位置与网络”国际研讨会

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451891

Dirk Ahlers, Erik Wilde, R. Schifanella, Jalal S. Alowibdi

引用次数: 1

Information flow on COVID-19 over Wikipedia: A case study of 11 languages 维基百科上关于COVID-19的信息流:以11种语言为例

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452352

Chang-Ryong Jung, I. Hong, Diego Sáez-Trumper, Damin Lee, Jaehyeon Myung, Danu Kim, Jinhyuk Yun, Woo-Sung Jung, M. Cha

引用次数: 0

Generating Rich Product Descriptions for Conversational E-commerce Systems 生成会话式电子商务系统的丰富产品描述

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451893

Shashank Kedia, Aditya Mantha, Sneha R. Gupta, Stephen D. Guo, Kannan Achan

{"title":"Generating Rich Product Descriptions for Conversational E-commerce Systems","authors":"Shashank Kedia, Aditya Mantha, Sneha R. Gupta, Stephen D. Guo, Kannan Achan","doi":"10.1145/3442442.3451893","DOIUrl":"https://doi.org/10.1145/3442442.3451893","url":null,"abstract":"Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, ”Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free” is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as ”a 20.5 ounce box of lucky charms gluten free cereal” is preferred. Compared to display devices, where images and detailed product information can be presented to users, short titles for products which convey the most important information, are necessary when interfacing with voice assistants. We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles. Our extensive experiments on a real-world industry dataset, as well as human evaluation of model output, demonstrate that eBERT summarization outperforms comparable baseline models. Owing to the efficacy of the model, a version of this model has been deployed in real-world setting.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation 使用Traceroute位置传播和IP范围位置插值的IP地理定位

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451888

Ovidiu Dan, Vaibhav Parikh, Brian D. Davison

引用次数: 13

Analysis and Visualisation of Time Series Data on Networks with Pathpy 基于路径的网络时间序列数据分析与可视化

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452052

Jürgen Hackl, Ingo Scholtes, L. V. Petrovic, Vincenzo Perri, Luca Verginer, Christoph Gote

引用次数: 6

Predicting Paper Acceptance via Interpretable Decision Sets 通过可解释决策集预测论文接受度

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451370

Peng Bao, Weihui Hong, Xuanya Li

{"title":"Predicting Paper Acceptance via Interpretable Decision Sets","authors":"Peng Bao, Weihui Hong, Xuanya Li","doi":"10.1145/3442442.3451370","DOIUrl":"https://doi.org/10.1145/3442442.3451370","url":null,"abstract":"Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123312747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9