Creating a test collection to evaluate diversity in image retrieval

This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters

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

AR-Miner: Mining informative reviews for developers from mobile app marketplace

Ministry of Education, Singapore under its Academic Research Funding Tier

Feature Extraction and Duplicate Detection for Text Mining: A Survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201