2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第2页

Semi-supervised text classification with deep convolutional neural network using feature fusion approach 基于特征融合的深度卷积神经网络半监督文本分类

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352548

Parvaneh Shayegh, Yuefeng Li, Jinglan Zhang, Qing Zhang

{"title":"Semi-supervised text classification with deep convolutional neural network using feature fusion approach","authors":"Parvaneh Shayegh, Yuefeng Li, Jinglan Zhang, Qing Zhang","doi":"10.1145/3350546.3352548","DOIUrl":"https://doi.org/10.1145/3350546.3352548","url":null,"abstract":"Supervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data and a large set of unlabeled data to improve predication performance and thus may be a good alternative to supervised learning algorithms for large text datasets. Although many semi-supervised learning algorithms have been proposed in the data science literature, most of these algorithms are not feasible for discrete and unstructured text data.This paper aims to improve classification accuracy of semi-supervised learning algorithms applied to text data. To achieve this goal, a novel design for convolutional neural network is employed in a co-training semi-supervised learning algorithm which adds augmented data as the second input of the convolutional neural network to predict labels of text data. we also propose a novel approach for partitioning the dataset into independent views via topic modeling to train independent classifiers. In so doing, neighbour classifiers are found and confident predictions of unlabeled data are fused into labeled data. The prediction accuracy of the combined algorithm is then compared to the state-of-the-art supervised and semi-supervised learning algorithms. Our findings show that the proposed combined algorithm outperforms the supervised and semi-supervised algorithms in terms of prediction accuracy. CCS CONCEPTS• Information systems → Content analysis and feature selection.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116878334","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

Sentence Simplification from Non-Parallel Corpus with Adversarial Learning 基于对抗性学习的非平行语料库句子简化

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352499

Takashi Kawashima, T. Takagi

引用次数: 2

Bridging Between Emojis and Kaomojis by Learning Their Representations from Linguistic and Visual Information 从语言和视觉信息中学习表情符号和Kaomojis之间的桥梁

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352508

Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Hiroya Takamura, M. Okumura

{"title":"Bridging Between Emojis and Kaomojis by Learning Their Representations from Linguistic and Visual Information","authors":"Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Hiroya Takamura, M. Okumura","doi":"10.1145/3350546.3352508","DOIUrl":"https://doi.org/10.1145/3350546.3352508","url":null,"abstract":"Small images of emojis have unique characteristics as additional information in understanding writers’ intentions. They enable social media users to emphasize their emotions and to express gestural movements in their posts. In addition to emojis, kaomojis (emoticons or facemarks) also behave in a similar way. They are composed of a sequence of characters, which are popularized especially in Asian countries. Although both emojis and kaomojis fulfill similar functions and share the same meaning that can be clues in opinion mining or sentiment analysis, the previous researches have been biased to explore emojis and kaomojis separately. In this paper, we align emojis and kaomojis together as a single token in the Japanese context to offer a bridge between them. Specifically, we aim to judge whether emojis and kaomojis share the same meaning or are similar with each other. We assume that emojis and kaomojis are both a single word in order to obtain their linguistic information with the skip-gram model. Furthermore, we present a new approach to consider the appearances of emojis and kaomojis in themselves, meaning that we explore the information of their visually similar shapes. We regard both of them as a single image to take into account their visual information with the CNN model. We merge two different perspectives toward emojis and kaomojis by exploring their linguistic and visual information simultaneously on the same space. The experimental results showed that we can align an unlimited number of emojis and kaomojis together with their representations (embeddings), and adding the visual information to the linguistic information can improve their representations.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126936853","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

Learning User Reputation on Reddit 学习Reddit上的用户声誉

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352524

Alexandre Parmentier, R. Cohen

引用次数: 7

Detecting Anomalous Behaviour from Textual Content in Financial Records 从财务记录的文本内容中检测异常行为

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352550

Jerry George Thomas, S. Mudur, Nematollaah Shiri

{"title":"Detecting Anomalous Behaviour from Textual Content in Financial Records","authors":"Jerry George Thomas, S. Mudur, Nematollaah Shiri","doi":"10.1145/3350546.3352550","DOIUrl":"https://doi.org/10.1145/3350546.3352550","url":null,"abstract":"Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.CCS CONCEPTS• Applied computing → Secure online transactions.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125361883","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

Improving Collaborative Filtering’s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information 利用项目采用热心信息提高协同过滤评分预测质量

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352544

Dionisis Margaris, D. Spiliotopoulos, C. Vassilakis

{"title":"Improving Collaborative Filtering’s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information","authors":"Dionisis Margaris, D. Spiliotopoulos, C. Vassilakis","doi":"10.1145/3350546.3352544","DOIUrl":"https://doi.org/10.1145/3350546.3352544","url":null,"abstract":"Collaborative filtering generates recommendations tailored to the users’ preferences by exploiting item ratings registered by users. Collaborative filtering algorithms firstly find people that have rated items in a similar fashion; these people are coined as “near neighbors” and their ratings on items are combined in the recommendation generation phase to predict ratings and generate recommendations. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as “Early Adopters”, who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we propose an algorithm that considers the eagerness shown by users to adopt products, so as to leverage the accuracy of rating prediction. The proposed algorithm is evaluated using seven popular datasets.CCS CONCEPTS •Information systems → Collaborative filtering.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126394179","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

Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search 探索用户痕迹对阿拉伯文和英文Facebook搜索影响的差异

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352522

Ismail Badache

{"title":"Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search","authors":"Ismail Badache","doi":"10.1145/3350546.3352522","DOIUrl":"https://doi.org/10.1145/3350546.3352522","url":null,"abstract":"This paper proposes an approach on Facebook search in Arabic and English, which exploits several users’ traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic and English Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook posts. Therefore, the polarity of each comment expressed in Arabic or in English on a given Facebook post, is estimated on the basis of a neural sentiment model. Secondly, we group signals according to their complementarity using attributes (features) selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics, for each of the two languages. Experiments results reveal that Random Forests was the most effective LTR approach for this task, and for the both languages. However, the best appropriate features selection algorithms are ReliefFAttributeEval and InfoGainAttributeEval for Arabic and English Facebook search task, respectively.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114244681","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

Analysis of Computational Models to Describe Individual Decision-Making Process 描述个体决策过程的计算模型分析

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352515

E. Santos, Hien Nguyen, K. Kim, Russell Jacob, Luke Veenhuis, Luke De Guelle

{"title":"Analysis of Computational Models to Describe Individual Decision-Making Process","authors":"E. Santos, Hien Nguyen, K. Kim, Russell Jacob, Luke Veenhuis, Luke De Guelle","doi":"10.1145/3350546.3352515","DOIUrl":"https://doi.org/10.1145/3350546.3352515","url":null,"abstract":"Understanding the human decision-making process and evaluating the quality of these decisions has been the focus of many researchers. Previously, we proposed a computational, cognitive framework called the Double Transition Model (DTM) to study human decision-making processes. We applied it to simulate a couple of scenarios developed through a naval warfare simulation game called Steel Ocean. This framework concentrated on the cognitive process of an individual’s decision-making process and capturing his cognitive style. One of the key functionalities of this framework has been to provide a reward distribution indicating the quality of decisions made under certain conditions. In this paper, we present a rigorous investigation of our models capturing individual characteristics with respect to decision-making style and the reward distributions. In particular, our models explored the following questions: 1) whether individual models are different from each other like human beings are; 2) whether these models exhibit particular decision-making styles; and 3) whether these models can capture different situations as human beings do. We evaluated the capability of our models capturing these individuals’ characteristics by comparing multiple DTMs against each other, each built from a couple of individuals under various circumstances. We confirmed that individual characteristics could be captured in the DTMs. Furthermore, we compared individuals’ trajectories (i.e., a sequence of decisions) identified by multiple DTMs in addition to their associated neighbors to verify that decision-making process in various social conditions could be described with DTMs. Our empirical study was conducted on two sets of real-world data: Supervisory Control Operations User Testbed (SCOUT) and the naval warfare simulation game (Steel Ocean).","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"27 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120896906","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

Word Embedding based Clustering to Detect Topics in Social Media 基于词嵌入聚类的社交媒体主题检测

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352518

C. Comito, Agostino Forestiero, C. Pizzuti

引用次数: 29

A Graph-Theoretic Embedding-Based Approach for Rumor Detection in Twitter 基于图论嵌入的Twitter谣言检测方法

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352569

M. Abulaish, N. kumari, Mohd Fazil, Basanta Singh

引用次数: 13