Companion Proceedings of the The Web Conference 2018最新文献_第5页

Exploring the Efficiency of Batch Active Learning for Human-in-the-Loop Relation Extraction 探索批量主动学习在人在环关系提取中的效率

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191546

Ismini Lourentzou, D. Gruhl, Steve Welch

{"title":"Exploring the Efficiency of Batch Active Learning for Human-in-the-Loop Relation Extraction","authors":"Ismini Lourentzou, D. Gruhl, Steve Welch","doi":"10.1145/3184558.3191546","DOIUrl":"https://doi.org/10.1145/3184558.3191546","url":null,"abstract":"Domain-specific relation extraction requires training data for supervised learning models, and thus, significant labeling effort. Distant supervision is often leveraged for creating large annotated corpora however these methods require handling the inherent noise. On the other hand, active learning approaches can reduce the annotation cost by selecting the most beneficial examples to label in order to learn a good model. The choice of examples can be performed sequentially, i.e. select one example in each iteration, or in batches, i.e. select a set of examples in each iteration. The optimization of the batch size is a practical problem faced in every real-world application of active learning, however it is often treated as a parameter decided in advance. In this work, we study the trade-off between model performance, the number of requested labels in a batch and the time spent in each round for real-time, domain specific relation extraction. Our results show that the use of an appropriate batch size produces competitive performance, even compared to a fully sequential strategy, while reducing the training time dramatically.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129927587","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: 14

What about Mood Swings: Identifying Depression on Twitter with Temporal Measures of Emotions 情绪波动:用情绪的时间测量来识别推特上的抑郁症

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191624

Xuetong Chen, M. Sykora, Thomas W. Jackson, Suzanne Elayan

{"title":"What about Mood Swings: Identifying Depression on Twitter with Temporal Measures of Emotions","authors":"Xuetong Chen, M. Sykora, Thomas W. Jackson, Suzanne Elayan","doi":"10.1145/3184558.3191624","DOIUrl":"https://doi.org/10.1145/3184558.3191624","url":null,"abstract":"Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals' psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual's emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480815","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: 94

Urban Perception of Commercial Activeness from Satellite Images and Streetscapes 从卫星图像和街景看城市商业活跃度

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186581

Wenshan Wang, Su Yang, Zhiyuan He, Minjie Wang, Jiulong Zhang, Weishan Zhang

{"title":"Urban Perception of Commercial Activeness from Satellite Images and Streetscapes","authors":"Wenshan Wang, Su Yang, Zhiyuan He, Minjie Wang, Jiulong Zhang, Weishan Zhang","doi":"10.1145/3184558.3186581","DOIUrl":"https://doi.org/10.1145/3184558.3186581","url":null,"abstract":"People can percept social attributes from streetscapes such as safety, richness, and happiness by means of visual perception, which inspires the research in terms of urban perception. To the best of our knowledge, this is the first work focused on revealing the relationship between visual patterns of satellite images as well as streetscapes and commercial activeness. We propose to make use of bag of features (BoF) in the context of computer vision and sparse representation in the sense of machine learning to predict commercial activeness of urban commercial districts. After obtaining the urban commercial districts via clustering, we predict the commercial activeness degrees of them using four image features, namely, Histogram of Oriented Gradients (HOG), Autoencoder, GIST, and multifractal spectra for satellite images and street view images, respectively. The performance evaluation with four large-scale datasets demonstrates that the presented computational framework can not only predict the commercial activeness with satisfactory precision compared with that based on Point of Interest (POI) data but also discover the visual patterns related.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121336449","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: 21

Language-Integrated Queries: a BOLDR Approach 语言集成查询:BOLDR方法

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3185973

Véronique Benzaken, Giuseppe Castagna, L. Daynès, Julien Lopez, K. Nguyen, R. Vernoux

引用次数: 2

GeoSensor: On-line Scalable Change and Event Detection over Big Data GeoSensor:基于大数据的在线可扩展变化和事件检测

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186984

G. Argyriou, G. Papadakis, G. Stamoulis, Efi Karra Taniskidou, Nikiforos Pittaras, George Giannakopoulos, Sergio Albani, M. Lazzarini, E. Angiuli, A. Popescu, Argyros Argyridis, Manolis Koubarakis

引用次数: 2

Structured Knowledge on the Web 7.0 Web 7.0中的结构化知识

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3190666

Steffen Staab, Jens Lehmann, R. Verborgh

引用次数: 1

Open Information Extraction with Global Structure Constraints 基于全局结构约束的开放信息提取

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186927

Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Frank F. Xu, Jiawei Han

{"title":"Open Information Extraction with Global Structure Constraints","authors":"Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Frank F. Xu, Jiawei Han","doi":"10.1145/3184558.3186927","DOIUrl":"https://doi.org/10.1145/3184558.3186927","url":null,"abstract":"Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. However, current open IE systems ignore the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114082811","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: 6

INF-UFG at FiQA 2018 Task 1: Predicting Sentiments and Aspects on Financial Tweets and News Headlines 在FiQA 2018任务1:预测财经推文和新闻标题的情绪和方面

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3191828

Dayan de França Costa, Nádia Félix Felipe Da Silva

引用次数: 12

Linked Data for Production (LD4P): a Multi-Institutional Approach to Technical Services Transformation 生产关联数据(LD4P):技术服务转型的多机构方法

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186201

Philip E. Schreur

{"title":"Linked Data for Production (LD4P): a Multi-Institutional Approach to Technical Services Transformation","authors":"Philip E. Schreur","doi":"10.1145/3184558.3186201","DOIUrl":"https://doi.org/10.1145/3184558.3186201","url":null,"abstract":"Linked Data for Production (LD4P) is a collaboration between six institutions (Columbia, Cornell, Harvard, Library of Congress, Princeton, and Stanford) to begin the transition of technical services production workflows from a series of library-centric data formats (MARC) to ones based in Linked Open Data (LOD). This first phase of the transition focuses on the development of the ability to produce metadata as LOD communally, the enhancement of the BIBFRAME ontology to encompass the multiple resource formats that academic libraries must process, and the engagement of the broader academic library community to ensure a sustainable and extensible environment. As its name implies, LD4P focuses on the immediate needs of metadata production such as ontology coverage and workflow transition. The LD4P partners' work will be based, in part, on a collection of tools that currently exist, such as those developed by the Library of Congress. The cyclical feedback of use and enhancement request to the developers of these tools will allow for their enhancement based on use in an actual production environment. The six institutions involved will focus on materials ranging from art to rare books, from cartographic materials to music, from annotations to workflows. Tool development and enhancement will also be a key aspect of the project. By the end of the first phase of this project (Spring 2018), the partners will have the minimal tooling, workflows, and standards developed to begin the transformation from MARC to LOD in Phase 2 of the project.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121673385","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: 1

Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks 从文本学习程序:编纂如何在深度神经网络程序

Companion Proceedings of the The Web Conference 2018 Pub Date : 2018-04-23 DOI: 10.1145/3184558.3186347

Hogun Park, H. M. Nezhad

引用次数: 25