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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.
Knowledge Acquisition from User Reviews for Interactive Question AnsweringMitkov, Ruslan; Orasan, Constantin; De Boni, Marco; Konstantinova, Natalia (University of Wolverhampton, 2013)Nowadays, the effective management of information is extremely important for all spheres of our lives and applications such as search engines and question answering systems help users to find the information that they need. However, even when assisted by these various applications, people sometimes struggle to find what they want. For example, when choosing a product customers can be confused by the need to consider many features before they can reach a decision. Interactive question answering (IQA) systems can help customers in this process, by answering questions about products and initiating a dialogue with the customers when their needs are not clearly defined. The focus of this thesis is how to design an interactive question answering system that will assist users in choosing a product they are looking for, in an optimal way, when a large number of similar products are available. Such an IQA system will be based on selecting a set of characteristics (also referred to as product features in this thesis), that describe the relevant product, and narrowing the search space. We believe that the order in which these characteristics are presented in terms of these IQA sessions is of high importance. Therefore, they need to be ranked in order to have a dialogue which selects the product in an efficient manner. The research question investigated in this thesis is whether product characteristics mentioned in user reviews are important for a person who is likely to purchase a product and can therefore be used when designing an IQA system. We focus our attention on products such as mobile phones; however, the proposed techniques can be adapted for other types of products if the data is available. Methods from natural language processing (NLP) fields such as coreference resolution, relation extraction and opinion mining are combined to produce various rankings of phone features. The research presented in this thesis employs two corpora which contain texts related to mobile phones specifically collected for this thesis: a corpus of Wikipedia articles about mobile phones and a corpus of mobile phone reviews published on the Epinions.com website. Parts of these corpora were manually annotated with coreference relations, mobile phone features and relations between mentions of the phone and its features. The annotation is used to develop a coreference resolution module as well as a machine learning-based relation extractor. Rule-based methods for identification of coreference chains describing the phone are designed and thoroughly evaluated against the annotated gold standard. Machine learning is used to find links between mentions of the phone (identified by coreference resolution) and phone features. It determines whether some phone feature belong to the phone mentioned in the same sentence or not. In order to find the best rankings, this thesis investigates several settings. One of the hypotheses tested here is that the relatively low results of the proposed baseline are caused by noise introduced by sentences which are not directly related to the phone and phone feature. To test this hypothesis, only sentences which contained mentions of the mobile phone and a phone feature linked to it were processed to produce rankings of the phones features. Selection of the relevant sentences is based on the results of coreference resolution and relation extraction. Another hypothesis is that opinionated sentences are a good source for ranking the phone features. In order to investigate this, a sentiment classification system is also employed to distinguish between features mentioned in positive and negative contexts. The detailed evaluation and error analysis of the methods proposed form an important part of this research and ensure that the results provided in this thesis are reliable.
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.
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.