Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user requires the process of appropriate answer extraction, re-ranking and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the question. The features are weighted by the score returned by ensemble ExtraTreesClassifier according to features importance. An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question and answer candidate and thus achieving 0.64 Mean Reciprocal Rank (MRR). Further, answer is validated by matching the answer type of answer candidate and returns the highest ranked answer candidate with matched answer type to a user.

Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user requires the process of appropriate answer extraction, re-ranking and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the question. The features are weighted by the score returned by ensemble ExtraTreesClassifier according to features importance. An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question and answer candidate and thus achieving 0.64 Mean Reciprocal Rank (MRR). Further, answer is validated by matching the answer type of answer candidate and returns the highest ranked answer candidate with matched answer type to a user.

Why-type Question to Query Reformulation for efficient Document Retrieval

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.

Building a Discourse-Argument Hybrid System for Vietnamese Why-Question Answering

Recently, many deep learning models have archived high results in question answering task with overall F1 scores above 0.88 on SQuAD datasets. However, many of these models have quite low F1 scores on why-questions. These F1 scores range from 0.57 to 0.7 on SQuAD v1.1 development set. This means these models are more appropriate to the extraction of answers for factoid questions than for why-questions. Why-questions are asked when explanations are needed. These explanations are possibly arguments or simply subjective opinions. Therefore, we propose an approach to finding the answer for why-question using discourse analysis and natural language inference. In our approach, natural language inference is applied to identify implicit arguments at sentence level. It is also applied in sentence similarity calculation. Discourse analysis is applied to identify the explicit arguments and the opinions at sentence level in documents. The results from these two methods are the answer candidates to be selected as the final answer for each why-question. We also implement a system with our approach. Our system can provide an answer for a why-question and a document as in reading comprehension test. We test our system with a Vietnamese translated test set which contains all why-questions of SQuAD v1.1 development set. The test results show that our system cannot beat a deep learning model in F1 score; however, our system can answer more questions (answer rate of 77.0%) than the deep learning model (answer rate of 61.0%).

My journey through finance and stochastics

This year, Finance and Stochastics celebrates its 25th anniversary. The journal provides a platform for the community of researchers on which they can publish their ideas and results.Publication is an outcome of research which may be conducted for a number of years before it reaches the required maturity. I find this research process to be very important. Unfortunately, it is almost impossible to decode it from reading the research publications. This special issue of Finance and Stochastics gives me an opportunity to focus on it. I am grateful I can present my personal memory of this process. Understanding why questions are asked and how the answers are found is critical.

Is leadership a myth? A ‘new wave’ critical leadership-focused research agenda for recontouring the landscape of educational leadership

Intentionally provocative, this study identifies weaknesses in mainstream educational leadership scholarship, and draws upon ‘new wave’ critical leadership studies to propose a new, potentially paradigm-shifting, direction for the field. The central argument is that educational leadership researchers, in focusing predominantly on how institutional heads and other formal ‘leaders’ may best ‘do’ leadership, are addressing the wrong questions and setting off from the wrong departure point. The unit of analysis should shift, it is argued, from leadership to influence, within a new research agenda that replaces surface-level, causality-assumptive ‘how?’ and ‘why?’ questions that have shaped mainstream educational leadership research for over thirty years, with more fundamental ‘who? and ‘what?’ questions, aimed at identifying who is in fact doing the influencing. An aspect of such inquiry is leadership scepticism and agnosticism, which confronts the question: Does leadership exist, or is it a myth that we have reified? A highly original feature of the proposed new research agenda is the adoption of the author's theoretical notion of a singular unit of micro-level influence as an ‘epistemic object’ – a concept derived from STEMM research, denoting a vague and undefined potential focus of inquiry that may (or may not) turn out to be significant.

Cross-cultural realist interviews: An integration of the realist interview and cross-cultural qualitative research methods

Realist interviews are a data collection method used in realist evaluations. There is little available guidance for realist interviewing in cross-cultural contexts. Few published realist evaluations have included cross-cultural interviews, providing limited analyses of the cross-cultural application of realist methodology. This study integrated realist and cross-cultural qualitative methods in a realist evaluation of an Australian doula support program. The interviews were conducted with Arabic speaking clients of the program. The process included collaboration with a bicultural researcher, philosophically situating the study for methodologically coherent integration, bicultural review of the appropriateness of realist ‘how’ and ‘why’ questions, decisions about language translation and interpretation, pilot interviews, and co-facilitation of the interviews. Integration of the methods was feasible and valuable. This study may support other realist evaluators to give voice to people from culturally diverse groups, in a manner that is culturally safe, methodologically coherent and rigorous, and that produces trustworthy results.

Digital topics on cultural heritage investigated: how can data-driven and data-guided methods support to identify current topics and trends in digital heritage?

In research and policies, the identification of trends as well as emerging topics and topics in decline is an important source of information for both academic and innovation management. Since at present policy analysis mostly employs qualitative research methods, the following article presents and assesses different approaches – trend analysis based on questionnaires, quantitative bibliometric surveys, the use of computer-linguistic approaches and machine learning and qualitative investigations. Against this backdrop, this article examines digital applications in cultural heritage and, in particular, built heritage via various investigative frameworks to identify topics of relevance and trendlines, mainly for European Union (EU)-based research and policies. Furthermore, this article exemplifies and assesses the specific opportunities and limitations of the different methodical approaches against the backdrop of data-driven vs. data-guided analytical frameworks. As its major findings, our study shows that both research and policies related to digital applications for cultural heritage are mainly driven by the availability of new technologies. Since policies focus on meta-topics such as digitisation, openness or automation, the research descriptors are more granular. In general, data-driven approaches are promising for identifying topics and trendlines and even predicting the development of near future trends. Conversely, qualitative approaches are able to answer “why” questions with regard to whether topics are emerging due to disruptive innovations or due to new terminologies or whether topics are becoming obsolete because they are common knowledge, as is the case for the term “internet”.