Verbal Response Modes in Action:Microrelationships as the Building Blocks of Relationship Role Dimensions

Dimensions of interpersonal relationships, such as attentiveness, directiveness, and presumptuousness, have typically been assessed through impressionistic ratings or by aggregate scores derived from coding of specific (e.g., verbal) behaviors. However, the meanings of these dimensions rest on the interpersonal microrelationships that are actually observed by the raters or coders. In this qualitative study, the way these global relationship qualities were built from microrelationships at the utterance level was examined in passages from one medical interaction. Applications of microrelationships to future communications research are suggested

Semantic Interpretation of User Queries for Question Answering on Interlinked Data

The Web of Data contains a wealth of knowledge belonging to a large number of domains. Retrieving data from such precious interlinked knowledge bases is an issue. By taking the structure of data into account, it is expected that upcoming generation of search engines is approaching to question answering systems, which directly answer user questions. But developing a question answering over these interlinked data sources is still challenging because of two inherent characteristics: First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between these datasets on both the schema and instance levels. In this respect, several challenges such as resource disambiguation, vocabulary mismatch, inference, link traversal are raised. In this dissertation, we address these challenges in order to build a question answering system for Linked Data. We present our question answering system Sina, which transforms user-supplied queries (i.e. either natural language queries or keyword queries) into conjunctive SPARQL queries over a set of interlinked data sources. The contributions of this work are as follows: 1. A novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation approach). We employed a Hidden Markov Model, whose parameters were bootstrapped with different distribution functions. 2. A novel method for constructing federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph, which ultimately renders a corresponding SPARQL query. 3. Regarding the problem of vocabulary mismatch, our contribution is divided into two parts, First, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Data. We evaluate the effectiveness of each feature individually as well as their combinations, employing Support Vector Machines and Decision Trees. Second, we propose a novel method for automatic query expansion, which employs a Hidden Markov Model to obtain the optimal tuples of derived words. 4. We provide two benchmarks for two different tasks to the community of question answering systems. The first one is used for the task of question answering on interlinked datasets (i.e. federated queries over Linked Data). The second one is used for the vocabulary mismatch task. We evaluate the accuracy of our approach using measures like mean reciprocal rank, precision, recall, and F-measure on three interlinked life-science datasets as well as DBpedia. The results of our accuracy evaluation demonstrate the effectiveness of our approach. Moreover, we study the runtime of our approach in its sequential as well as parallel implementations and draw conclusions on the scalability of our approach on Linked Data

Exploring teachers’ and learners’ overlapped turns in the language classroom: Implications for classroom interactional competence

The language choices that teachers make in the language classroom have been found to influence the opportunities for learning given to learners (Seedhouse, 2004; Walsh, 2012; Waring, 2009, 2011). The present study expands on research addressing learner-initiated contributions (Garton, 2012; Jacknick, 2011; Waring, Reddington, & Tadic, 2016; Yataganbaba & Yıldırım, 2016) by demonstrating that opportunities for participation and learning can be promoted when teachers allow learners to expand and finish their overlapped turns. Audio recordings of lessons portraying language classroom interaction from three teachers in an adult foreign language classroom (EFL) setting were analyzed and discussed through conversation analysis (CA) methodology. Findings suggest that when teachers are able to navigate overlapping talk in such a way that provides interactional space for learners to complete their contributions, they demonstrate classroom interactional competence (Sert, 2015; Walsh, 2006). The present study contributes to the literature by addressing interactional features that increase interactional space, and an approach to teacher and learner talk that highlights CA’s methodological advantages in capturing the interactional nuances of classroom discourse

Three-party interactions between neurologists, patients and their companions in the seizure clinic

When patients attend seizure clinics, they are advised to bring along a companion (usually a family member or friend) who can help them to answer questions about their condition. Despite their role being officially sanctioned, there has been some debate over the usefulness of companions in this environment, with some seeing them as eating into the time that patients have to provide diagnostically-important information (Robson, Drew, & Reuber, 2013; Schwabe, Reuber, Schöndienst, & Gülich, 2008), whilst others suggest that companions contributions may actually help with diagnosis (Robson, Drew, & Reuber, 2016). Research from other medical contexts, meanwhile, suggests that companions can be helpful in ways that go beyond diagnosis, and that these other functions should be taken into account when considering their role in the consultation room (Ellingson, 2002; Laidsaar-Powell et al., 2013). This thesis aims to build upon this work by examining the companion s role at all stages (beyond just diagnosis and history-taking) in seizure clinic interactions. Based on 30 video-recorded initial visits in a seizure clinic in which a companion was present, my research used conversation analysis (CA) to examine, across four analytic chapters, several aspects of this topic. In chapter 3 I examine how it is that companions actually come to contribute to these interactions in the first place. My analysis shows that companions were explicitly invited to contribute in 20% of these cases (n=406), were implicitly invited to contribute in 27.6% of these cases (n=553), and that they volunteered themselves to contribute in the remaining 42.6% of cases (n=854). The second part of the chapter then analyses some of these instances in detail. It shows how companion participation is co-constructed between the participants and how companions are attuned to the relevance of their contributions for the ongoing interaction, as well as maintaining an orientation to the patient s rights as primary respondent. Having delineated the basic means of companion participation, the next two chapters consider how companions can contribute to the medical outcomes of the consultation, in their role as information-providers. Chapter 4 considers how companions can correct patients accounts of their illness. It shows, specifically how these corrections often upgrade the severity of the patient s own descriptions (e.g. provide a symptom, after the patient has given a no symptom answer, or upgrade the frequency of how many attacks the patient describes having). Chapter 5 then discusses companions contributions to talk, specifically about medication in both the history-taking and treatment-recommendation phases of the consultation. Based on the observation that companions contributed at least once to medication discussions in 67% of cases (n=20) in the data, the chapter shows that, during the history-taking phase, companions were used as an informational resource by both patient and doctor. In the treatment recommendation phase, meanwhile, companions showed initiative in asking questions, making suggestions, expressing concerns, and complaining about medication. In chapter 6 I demonstrate that companions contribute in a way that goes beyond simply providing medical information, by emotionally supporting patients. It shows that one important way in which they do so is by touching patients at points where they are displaying difficulty or emotional distress. This chapter will discuss how these touches appear to occur systematically in a sequential context where there is something delicate being discussed. This thesis provides an overview of companion participation in the seizure clinic. It shows how companions can, as expected, act as information-providers, thus supporting previous research (Ellingson, 2002; Laidsaar-Powell et al., 2013; Wolff et al., 2016). It also goes beyond this, though, to show how companions can provide a form of interpersonal emotional support which, while not necessarily part of their official role in the consultation, nonetheless serves an important function. Companions thus contribute at all points in seizure clinic interactions

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd