How Question Answering Technology Helps to Locate Malevolent Online Content

The inherent lack of control over the Internet content resulted in proliferation of online material that can be potentially detrimental. For example, the infamous “Anarchist Cookbook” teaching how to make weapons, home made bombs, and poisons, keeps re-appearing in various places. Some websites teach how to break into computer networks to steal passwords and credit card information. Law enforcement, security experts, and public watchdogs started to locate, monitor, and act when such malevolent content surfaces on the Internet. Since the resources of law enforcement are limited, it may take some time before potentially malevolent content is located, enough for it to disseminate and cause harm. Currently applied approach for searching the content of the Internet, available for law enforcement and public watchdogs is by using a search engine, such as Google, AOL, MSN, etc. We have suggested and empirically evaluated an alternative technology (called automated question answering or QA) capable of locating potentially malevolent online content. We have implemented a proof-of-concept prototype that is capable of finding web pages that may potentially contain the answers to specified questions (e.g. “How to steal a password?”). Using students as subjects in a controlled experiment, we have empirically established that our QA prototype finds web pages that are more likely to provide answers to given questions than simple keyword search using Google. This suggests that QA technology can be a good replacement or an addition to the traditional keyword searching for the task of locating malevolent online content and, possibly, for a more general task of interactive online information exploration

Online Music Store Accessibility

This study evaluates the web accessibility of online music stores for blind and low vision users. Music and audio books are one of the main sources of information for that group of people. The study reviews two of the online music stores: Amazon.com and iTunes. An online survey was designed to evaluate the usability and accessibility of those stores adapting the Technology Acceptance Model as a main research model

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

Algorithms for assessing the quality and difficulty of multiple choice exam questions

Multiple Choice Questions (MCQs) have long been the backbone of standardized testing in academia and industry. Correspondingly, there is a constant need for the authors of MCQs to write and refine new questions for new versions of standardized tests as well as to support measuring performance in the emerging massive open online courses, (MOOCs). Research that explores what makes a question difficult, or what questions distinguish higher-performing students from lower-performing students can aid in the creation of the next generation of teaching and evaluation tools. In the automated MCQ answering component of this thesis, algorithms query for definitions of scientific terms, process the returned web results, and compare the returned definitions to the original definition in the MCQ. This automated method for answering questions is then augmented with a model, based on human performance data from crowdsourced question sets, for analysis of question difficulty as well as the discrimination power of the non-answer alternatives. The crowdsourced question sets come from PeerWise, an open source online college-level question authoring and answering environment. The goal of this research is to create an automated method to both answer and assesses the difficulty of multiple choice inverse definition questions in the domain of introductory biology. The results of this work suggest that human-authored question banks provide useful data for building gold standard human performance models. The methodology for building these performance models has value in other domains that test the difficulty of questions and the quality of the exam takers

Learning to represent, categorise and rank in community question answering

The task of Question Answering (QA) is arguably one of the oldest tasks in Natural Language Processing, attracting high levels of interest from both industry and academia. However, most research has focused on factoid questions, e.g. Who is the president of Ireland? In contrast, research on answering non-factoid questions, such as manner, reason, difference and opinion questions, has been rather piecemeal. This was largely due to the absence of available labelled data for the task. This is changing, however, with the growing popularity of Community Question Answering (CQA) websites, such as Quora, Yahoo! Answers and the Stack Exchange family of forums. These websites provide natural labelled data allowing us to apply machine learning techniques. Most previous state-of-the-art approaches to the tasks of CQA-based question answering involved handcrafted features in combination with linear models. In this thesis we hypothesise that the use of handcrafted features can be avoided and the tasks can be approached with representation learning techniques, specifically deep learning. In the first part of this thesis we give an overview of deep learning in natural language processing and empirically evaluate our hypothesis on the task of detecting semantically equivalent questions, i.e. predicting if two questions can be answered by the same answer. In the second part of the thesis we address the task of answer ranking, i.e. determining how suitable an answer is for a given question. In order to determine the suitability of representation learning for the task of answer ranking, we provide a rigorous experimental evaluation of various neural architectures, based on feedforward, recurrent and convolutional neural networks, as well as their combinations. This thesis shows that deep learning is a very suitable approach to CQA-based QA, achieving state-of-the-art results on the two tasks we addressed