Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
AdvisorsOrasan, Constantin Dr.
Mitkov, Ruslan Prof.
Navigli, Roberto Prof.
MetadataShow full item record
AbstractOpen-domain question answering (QA) is an established NLP task which enables users to search for speciVc pieces of information in large collections of texts. Instead of using keyword-based queries and a standard information retrieval engine, QA systems allow the use of natural language questions and return the exact answer (or a list of plausible answers) with supporting snippets of text. In the past decade, open-domain QA research has been dominated by evaluation fora such as TREC and CLEF, where shallow techniques relying on information redundancy have achieved very good performance. However, this performance is generally limited to simple factoid and deVnition questions because the answer is usually explicitly present in the document collection. Current approaches are much less successful in Vnding implicit answers and are diXcult to adapt to more complex question types which are likely to be posed by users. In order to advance the Veld of QA, this thesis proposes a shift in focus from simple factoid questions to encyclopaedic questions: list questions composed of several constraints. These questions have more than one correct answer which usually cannot be extracted from one small snippet of text. To correctly interpret the question, systems need to combine classic knowledge-based approaches with advanced NLP techniques. To Vnd and extract answers, systems need to aggregate atomic facts from heterogeneous sources as opposed to simply relying on keyword-based similarity. Encyclopaedic questions promote QA systems which use basic reasoning, making them more robust and easier to extend with new types of constraints and new types of questions. A novel semantic architecture is proposed which represents a paradigm shift in open-domain QA system design, using semantic concepts and knowledge representation instead of words and information retrieval. The architecture consists of two phases, analysis – responsible for interpreting questions and Vnding answers, and feedback – responsible for interacting with the user. This architecture provides the basis for EQUAL, a semantic QA system developed as part of the thesis, which uses Wikipedia as a source of world knowledge and iii employs simple forms of open-domain inference to answer encyclopaedic questions. EQUAL combines the output of a syntactic parser with semantic information from Wikipedia to analyse questions. To address natural language ambiguity, the system builds several formal interpretations containing the constraints speciVed by the user and addresses each interpretation in parallel. To Vnd answers, the system then tests these constraints individually for each candidate answer, considering information from diUerent documents and/or sources. The correctness of an answer is not proved using a logical formalism, instead a conVdence-based measure is employed. This measure reWects the validation of constraints from raw natural language, automatically extracted entities, relations and available structured and semi-structured knowledge from Wikipedia and the Semantic Web. When searching for and validating answers, EQUAL uses the Wikipedia link graph to Vnd relevant information. This method achieves good precision and allows only pages of a certain type to be considered, but is aUected by the incompleteness of the existing markup targeted towards human readers. In order to address this, a semantic analysis module which disambiguates entities is developed to enrich Wikipedia articles with additional links to other pages. The module increases recall, enabling the system to rely more on the link structure of Wikipedia than on word-based similarity between pages. It also allows authoritative information from diUerent sources to be linked to the encyclopaedia, further enhancing the coverage of the system. The viability of the proposed approach was evaluated in an independent setting by participating in two competitions at CLEF 2008 and 2009. In both competitions, EQUAL outperformed standard textual QA systems as well as semi-automatic approaches. Having established a feasible way forward for the design of open-domain QA systems, future work will attempt to further improve performance to take advantage of recent advances in information extraction and knowledge representation, as well as by experimenting with formal reasoning and inferencing capabilities.
PublisherUniversity of Wolverhampton
TypeThesis or dissertation
Showing items related by title, author, creator and subject.
Prosuming tourist information: asking questions on TripAdvisorOriade, Ade; Robinson, Peter (Wiley, 2018-10-21)This paper aims to improve our knowledge regarding types of queries raised by travellers on digital platforms by developing a model that helps in identifying and classifying such queries. Qualitative data collection and analysis of questions and answer postings of visitors on TripAdvisor forum of 10 U.K. destinations were used. Extracted data were analysed using NVivo11. Preliminary analysis identified basic themes in tourist information search. Further analysis indicated that two principal factors help in classifying online travel queries facilitating the development of the WOLF model. Findings in this study also indicate some practical implications and areas of further study.
What jihad questions do Muslims ask?Emad Mohamed; Bakinaz Abdalla (Indiana University Press, 2017-05-01)Using digital humanities tools and methods, we extract, classify, and analyze 1,006 jihad fatwas from a corpus of 164,000 online fatwas. We use the questions and page hits to rank clusters of fatwas in order to discover what jihad questions Muslims ask, what jihad issues interest Muslims the most, and what the targets of jihad may be. We focus more on the questions than the answers, since it is the questions that give us a window into what may be called the “Muslim collective mind.” The results show that jihad questions are interwoven with several key topics, from performance of prayers to expiation for homosexuality. While the Prophet Muhammad's military expeditions were the most asked about and most viewed category, since they provide a model of what jihad is, the second most important category was concubinage. When there was a specific target, Jews were found in 73% of the questions.
Automatic Generation of Factual Questions from Video DocumentariesMitkov, Ruslan; Specia, Lucia; Ha, Le An; Skalban, Yvonne (University of Wolverhampton, 2013-10)Questioning sessions are an essential part of teachers’ daily instructional activities. Questions are used to assess students’ knowledge and comprehension and to promote learning. The manual creation of such learning material is a laborious and time-consuming task. Research in Natural Language Processing (NLP) has shown that Question Generation (QG) systems can be used to efficiently create high-quality learning materials to support teachers in their work and students in their learning process. A number of successful QG applications for education and training have been developed, but these focus mainly on supporting reading materials. However, digital technology is always evolving; there is an ever-growing amount of multimedia content available, and more and more delivery methods for audio-visual content are emerging and easily accessible. At the same time, research provides empirical evidence that multimedia use in the classroom has beneficial effects on student learning. Thus, there is a need to investigate whether QG systems can be used to assist teachers in creating assessment materials from these different types of media that are being employed in classrooms. This thesis serves to explore how NLP tools and techniques can be harnessed to generate questions from non-traditional learning materials, in particular videos. A QG framework which allows the generation of factual questions from video documentaries has been developed and a number of evaluations to analyse the quality of the produced questions have been performed. The developed framework uses several readily available NLP tools to generate questions from the subtitles accompanying a video documentary. The reason for choosing video vii documentaries is two-fold: firstly, they are frequently used by teachers and secondly, their factual nature lends itself well to question generation, as will be explained within the thesis. The questions generated by the framework can be used as a quick way of testing students’ comprehension of what they have learned from the documentary. As part of this research project, the characteristics of documentary videos and their subtitles were analysed and the methodology has been adapted to be able to exploit these characteristics. An evaluation of the system output by domain experts showed promising results but also revealed that generating even shallow questions is a task which is far from trivial. To this end, the evaluation and subsequent error analysis contribute to the literature by highlighting the challenges QG from documentary videos can face. In a user study, it was investigated whether questions generated automatically by the system developed as part of this thesis and a state-of-the-art system can successfully be used to assist multimedia-based learning. Using a novel evaluation methodology, the feasibility of using a QG system’s output as ‘pre-questions’ with different types of prequestions (text-based and with images) used was examined. The psychometric parameters of the automatically generated questions by the two systems and of those generated manually were compared. The results indicate that the presence of pre-questions (preferably with images) improves the performance of test-takers and they highlight that the psychometric parameters of the questions generated by the system are comparable if not better than those of the state-of-the-art system. In another experiment, the productivity of questions in terms of time taken to generate questions manually vs. time taken to post-edit system-generated questions was analysed. A viii post-editing tool which allows for the tracking of several statistics such as edit distance measures, editing time, etc, was used. The quality of questions before and after postediting was also analysed. Not only did the experiments provide quantitative data about automatically and manually generated questions, but qualitative data in the form of user feedback, which provides an insight into how users perceived the quality of questions, was also gathered.