Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Email classification via intention-based segmentation

Email is the most popular way of personal and official communication among people and organizations. Due to untrusted virtual environment, email systems may face frequent attacks like malware, spamming, social engineering, etc. Spamming is the most common malicious activity, where unsolicited emails are sent in bulk, and these spam emails can be the source of malware, waste resources, hence degrade the productivity. In spam filter development, the most important challenge is to find the correlation between the nature of spam and the interest of the users because the interests of users are dynamic. This paper proposes a novel dynamic spam filter model that considers the changes in the interests of users with time while handling the spam activities. It uses intention-based segmentation to compare different segments of text documents instead of comparing them as a whole. The proposed spam filter is a multi-tier approach where initially, the email content is divided into segments with the help of part of speech (POS) tagging based on voices and tenses. Further, the segments are clustered using hierarchical clustering and compared using the vector space model. In the third stage, concept drift is detected in the clusters to identify the change in the interest of the user. Later, the classification of ham emails into various categories is done in the last stage. For experiments Enron dataset is used and the obtained results are promising

Community based Question Answer Detection

Each day, millions of people ask questions and search for answers on the World Wide Web. Due to this, the Internet has grown to a world wide database of questions and answers, accessible to almost everyone. Since this database is so huge, it is hard to find out whether a question has been answered or even asked before. As a consequence, users are asking the same questions again and again, producing a vicious circle of new content which hides the important information. One platform for questions and answers are Web forums, also known as discussion boards. They present discussions as item streams where each item contains the contribution of one author. These contributions contain questions and answers in human readable form. People use search engines to search for information on such platforms. However, current search engines are neither optimized to highlight individual questions and answers nor to show which questions are asked often and which ones are already answered. In order to close this gap, this thesis introduces the \\emph{Effingo} system. The Effingo system is intended to extract forums from around the Web and find question and answer items. It also needs to link equal questions and aggregate associated answers. That way it is possible to find out whether a question has been asked before and whether it has already been answered. Based on these information it is possible to derive the most urgent questions from the system, to determine which ones are new and which ones are discussed and answered frequently. As a result, users are prevented from creating useless discussions, thus reducing the server load and information overload for further searches. The first research area explored by this thesis is forum data extraction. The results from this area are intended be used to create a database of forum posts as large as possible. Furthermore, it uses question-answer detection in order to find out which forum items are questions and which ones are answers and, finally, topic detection to aggregate questions on the same topic as well as discover duplicate answers. These areas are either extended by Effingo, using forum specific features such as the user graph, forum item relations and forum link structure, or adapted as a means to cope with the specific problems created by user generated content. Such problems arise from poorly written and very short texts as well as from hidden or distributed information

Cross-Layer Fragment Indexing based File Deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for Optimized Cloud Storage

Cloud computing and storage processing is a big service for maintaining a large number of data in a centralized server to store and retrieve data depending on the use to pay as a service model. Due to increasing storage depending on duplicate copy presence during different sceneries, the increased size leads to increased cost. To resolve this problem, we propose a Cross-Layer Fragment Indexing (CLFI) based file deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for optimized cloud storage. Initially, the file storage indexing easy carried out with Lexical Syntactic Parser (LSP) to split the files into blocks. Then comparativesector was created based on Chunk staking. Based on the file frequency weight, the relative Indexing was verified through Cross-Layer Fragment Indexing (CLFI). Then the fragmented index gets grouped by maximum relative threshold margin usingIntra Subset Near-Duplicate Clusters (ISNDC). The hashing is applied to get comparative index points based on hyper correlation comparer using Hyper Spectral Hash Duplicate Filter (HSHDF). This filter the near duplicate contentdepending on file content difference to identify the duplicates. This proposed system produces high performance compared to the other system. This optimizes cloudstorage and has a higher precision rate than other methods

Keep the conversations going, engagement-based customer segmentation on online social service platforms

Keep the conversations going, engagement-based customer segmentation on online social service platform

Studies on User Intent Analysis and Mining

Predicting the goals of users can be extremely useful in e-commerce, online entertainment, information retrieval, and many other online services and applications. In this thesis, we study the task of user intent understanding, trying to bridge the gap between user expressions to online services and their goals behind it. As far as we know, most of the existing user intent studies are focusing on web search and social media domain. Studies on other areas are not enough. For example, as people more and more rely our daily life on cellphone, our information needs expressing to mobile devices and related services are increasing dramatically. Studies of user intent mining on mobile devices are not much. And the intentions of using mobile devices are different from the ones we use web search engine or social network. So we cannot directly apply the existing user intention to this area. Besides, user's intents are not stable but changing over time. And different interests will impact each other. Modeling such kind of dynamic user interests can help accurately understand and predict user's intent. But there're few existing works in this area. Moreover, user intent could be explicitly or implicitly expressed by users. The implicit intent expression is more close to human's natural language and also have great value to recognize and mine. To make further studies of these challenges, we first try to answer the question of “What is the user intent?” By referring amount of previous studies, we give our definition of user intent as “User intent is a task-specific, predefined or latent concept, topic or knowledge-base that is under an expression from a user who is trying to express his goal of information or service need.“ Then, we focus on the driving scenario when a user using cellphone and study the user intent in this domain. As far as we know, it is the first time of user intent analysis and categorization in this domain. And we also build a dataset of user input and related intent category and attributes by crowdsourcing and carefully handcraft. With the user intent taxonomy and dataset in hand, we conduct a user intent classification and user intent attribute recognition by supervised machine learning models. To classify the user intent for a user intent query, we use a convolutional neural network model to build a multi-class classifier. And then we use a sequential labeling method to recognize the intent attribute in the query. The experiment results show that our proposed method outperforms several baseline models in precision, recall, and F-score. In addition, we study the implicit user intent mining method through web search log data. By using a Restricted Boltzmann Machine, we make use of the correlation of query and click information to learn the latent intent behind a user web search. We propose a user intent prediction model on online discussion forum using Multivariate Hawkes Process. It dynamically models user intentions change and interact over time.The method models both of the internal and external factors of user's online forum response motivations, and also integrated the time decay fact of user's interests. We also present a data visualization method, using an enriched domain ontology to highlight the domain-specific words and entity relations within an article.Ph.D., Information Studies -- Drexel University, 201

Segmentation of Social Media Users for Destinations: A Clustering Approach

This is a study into the segmentation of social media users interested in traveling into groups and aims to ascertain if differences exist in benefits among segments. It is based on a survey that examines the benefits they see social media as having before, during, and after a trip to a destination, using their responses to generate a data-driven segmentation. Data from a total of 218 questionnaires were analyzed using factor and cluster analysis in sequence, specifically applying a hierarchical cluster analysis using the Ward method and a Kmeans algorithm. The analysis led to the identification of four useful types of social media user: info-seeker, communication-seeker, interaction-seeker, and hybrid segments, each of which seeks different things from social media and use it in different ways (e.g., to seek information, to see what other people have said about a destination, or to post their own experiences). As such, the implications of our findings offer useful insights for both scholars and destination marketers, highlighting the significance of offering appropriate marketing strategies for each type of segment