2017 IEEE International Conference on Information Reuse and Integration (IRI)最新文献_第5页

Unsupervised Terminological Ontology Learning Based on Hierarchical Topic Modeling 基于分层主题建模的无监督术语本体学习

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.18

Xiaofeng Zhu, D. Klabjan, Patrick N. Bless

引用次数: 5

GFEL: Generalized Feature Embedding Learning Using Weighted Instance Matching GFEL:基于加权实例匹配的广义特征嵌入学习

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.21

Eric Golinko, Xingquan Zhu

{"title":"GFEL: Generalized Feature Embedding Learning Using Weighted Instance Matching","authors":"Eric Golinko, Xingquan Zhu","doi":"10.1109/IRI.2017.21","DOIUrl":"https://doi.org/10.1109/IRI.2017.21","url":null,"abstract":"Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Many feature embedding algorithms exist, but they are often designed to handle a single type of feature, or users have to clearly separate features into different feature views and supply such information for feature embedding learning. In this paper, we propose a generalized feature embedding learning algorithm, GFEL, which learns feature embedding from any type of data or data with mixed feature types. GFEL is an eigendecomposition based approach, which calculates weighted instance matching in the original feature space, and then uses an eigenvector decomposition to convert the proximity matrix into a low-dimensional space. The learned numerical embedding features, which blend the original features, can be directly used to represent instances for effective learning. Our experiments and comparisons on 28 datasets, including categorical, numerical, and ordinal features, demonstrate that embedding features learned from GFEL can effectively represent the original instances for clustering and classification tasks.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124324759","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

Addressing Forest Management Challenges by Refining Tree Cover Type Classification with Machine Learning Models 利用机器学习模型改进树木覆盖类型分类，解决森林管理挑战

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.89

Duncan Macmichael, Dong Si

引用次数: 5

Documentation Reuse: Managing Similar Documents 文档重用:管理类似文档

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.52

S. Jarzabek, D. Dan

引用次数: 2

AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter Twitter中机器人检测和姿态分类的关联亲和因子分析

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.25

Saad Sadiq, Yilin Yan, Asia Taylor, M. Shyu, Shu‐Ching Chen, D. Feaster

{"title":"AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter","authors":"Saad Sadiq, Yilin Yan, Asia Taylor, M. Shyu, Shu‐Ching Chen, D. Feaster","doi":"10.1109/IRI.2017.25","DOIUrl":"https://doi.org/10.1109/IRI.2017.25","url":null,"abstract":"The rise in popularity of social interacting websites such as Facebook, Twitter, and Snapchat has been challenged by the upsurge of unwelcomed and troubling bodies on these systems. This includes spam senders, malware systems, and other content contaminators. It is noted that highly automated accounts with 450 tweets per day produced almost 18% of entire Twitter circulation in the 2016 U.S. Presidential election. It is also observed that those disruptive systems called bots are inclined more towards circulating negative news than positive information. This paper introduces a novel framework named Associative Affinity Factor Analysis (AAFA) designed for stance detection and bot identification. Using AAFA, the proposed framework identifies real people from bots and detects the stance in bipolar affinities. The 2016 U.S. Presidential election campaign was used as a test use case because of its significant and unique counter-factual properties. The results show that our proposed AAFA framework achieves high accuracy when compared to several existing state-of-theart methods.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122168439","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

Modernizing Analytics for Melanoma with a Large-Scale Research Dataset 使用大规模研究数据集实现黑色素瘤的现代化分析

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.45

Aaron N. Richter, T. Khoshgoftaar

引用次数: 10

Entropy in Design Phase: A Higraph-Based Model Approach 设计阶段的熵:基于图的模型方法

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.51

H. Aboutaleb, B. Monsuez

引用次数: 1

Toward Semantic Search for the Biogeochemical Literature 生物地球化学文献的语义检索

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.49

Joshua D. Eisenberg, Deya Banisakher, Maria E. Presa-Reyes, Kalli Unthank, Mark A. Finlayson, René Price, Shu‐Ching Chen

{"title":"Toward Semantic Search for the Biogeochemical Literature","authors":"Joshua D. Eisenberg, Deya Banisakher, Maria E. Presa-Reyes, Kalli Unthank, Mark A. Finlayson, René Price, Shu‐Ching Chen","doi":"10.1109/IRI.2017.49","DOIUrl":"https://doi.org/10.1109/IRI.2017.49","url":null,"abstract":"Literature search is a vital step of every research project. Semantic literature search is an approach to article retrieval and ranking using concepts rather than keywords, in an attempt to address the well-known deficiencies of keyword-based search, namely, (1) retrieval of an overwhelming number of results, (2) rankings that do not precisely reflect true relevance, and (3) the omission of relevant results because they do not contain the idiosyncratic keywords of the query. The difficulty of semantic search, however, is that it requires significant knowledge engineering, often in the form of conceptual ontologies tailored to a particular scientific domain. It also requires non-trivial tuning, in the form of domain-specific term and concepts weights. Here we present preliminary, work-in-progress results in the development of a semantic search system for the biogeochemical scientific literature. We report the following initial steps: first, one of the co-authors—a biogeochemistry expert—wrote a sample search query, and ranked the five most relevant articles that were returned for that query from a popular keyword-based search engine. We then hand annotated the five articles and the query with the Environmental Ontology (ENVO), an existing ontology for the domain. Critically, this pilot annotation revealed a number of missing concepts that we will add in future work. We then showed that a straightforward ontology distance metric between concepts in the search query and the five articles was sufficient to produce the expected ranking. We discuss the implications of these results, and outline next steps required produce a full-fledged semantic search system for the biogeochemistry scientific literature.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122582065","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: 3

Deep Learning for Sentiment Analysis on Google Play Consumer Review 基于深度学习的Google Play消费者评论情感分析

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.79

Min-Yuh Day, Yue-Da Lin

{"title":"Deep Learning for Sentiment Analysis on Google Play Consumer Review","authors":"Min-Yuh Day, Yue-Da Lin","doi":"10.1109/IRI.2017.79","DOIUrl":"https://doi.org/10.1109/IRI.2017.79","url":null,"abstract":"In recent years, there has been an increasing interest in sentiment analysis on consumer reviews to understand the opinion polarity on social media. However, little attention has been paid to the development of deep learning for sentiment analysis on consumer reviews in Chinese. The research objective of this paper is to explore the impact of deep learning for sentiment analysis on Google Play consumer reviews in Chinese. A web mining technique was implemented for collecting 196,651 reviews on Google Play. We used Long Short Term Memory (LSTM) deep learning model, Naïve Bayes (NB), and support vector machine (SVM) approaches for sentiment analysis on consumer reviews and compared the experimental results. The experimental results suggest that the accuracy of deep learning for sentiment analysis on Google Play consumer review achieves 94% and deep learning approach outperforms Naïve Bayes (74.12%) and Support Vector Machine (76.46%) in the present study. Our finding confirmed that sentiment analysis on Google Play consumer review with deep learning is outstanding. The contributions of this paper are three-fold. First, the present study confirmed sentiment analysis with deep learning on Google Play consumer review may improve the accuracy of prediction. Second, we create a sentiment dictionary named iSGoPaSD for Google Play review. Third, the study compared the result of average sampling data and non-average sampling data. We found that deep learning method with non-average sampling data reached the better performance.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690445","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: 66

FA-MCADF: Feature Affinity Based Multiple Correspondence Analysis and Decision Fusion Framework for Disaster Information Management FA-MCADF:基于特征亲和的灾害信息管理多对应分析与决策融合框架

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.20

Haiman Tian, Shu‐Ching Chen, S. Rubin, William K. Grefe

引用次数: 4