Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections

Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list

A user-centred approach to information retrieval

A user model is a fundamental component in user-centred information retrieval systems. It enables personalization of a user's search experience. The development of such a model involves three phases: collecting information about each user, representing such information, and integrating the model into a retrieval application. Progress in this area is typically met with privacy and scalability challenges that hinder the ability to synthesize collective knowledge from each user's search behaviour. In this thesis, I propose a framework that addresses each of these three phases. The proposed framework is based on social role theory from the social science literature and at the centre of this theory is the concept of a social position. A social position is a label for a group of users with similar behavioural patterns. Examples of such positions are traveller, patient, movie fan, and computer scientist. In this thesis, a social position acts as a label for users who are expected to have similar interests. The proposed framework does not require real users' data; rather it uses the web as a resource to model users. The proposed framework offers a data-driven and modular design for each of the three phases of building a user model. First, I present an approach to identify social positions from natural language sentences. I formulate this task as a binary classification task and develop a method to enumerate candidate social positions. The proposed classifier achieves an accuracy score of 85.8%, which indicates that social positions can be identified with good accuracy. Through an inter-annotator agreement study, I further show a reasonable level of agreement between users when identifying social positions. Second, I introduce a novel topic modelling-based approach to represent each social position as a multinomial distribution over words. This approach estimates a topic from a document collection for each position. To construct such a collection for a particular position, I propose a seeding algorithm that extracts a set of terms relevant to the social position. Coherence-based evaluation shows that the proposed approach learns significantly more coherent representations when compared with a relevance modelling baseline. Third, I present a diversification approach based on the proposed framework. Diversification algorithms aim to return a result list for a search query that would potentially satisfy users with diverse information needs. I propose to identify social positions that are relevant to a search query. These positions act as an implicit representation of the many possible interpretations of the search query. Then, relevant positions are provided to a diversification technique that proportionally diversifies results based on each social position's importance. I evaluate my approach using four test collections provided by the diversity task of the Text REtrieval Conference (TREC) web tracks for 2009, 2010, 2011, and 2012. Results demonstrate that my proposed diversification approach is effective and provides statistically significant improvements over various implicit diversification approaches. Fourth, I introduce a session-based search system under the framework of learning to rank. Such a system aims to improve the retrieval performance for a search query using previous user interactions during the search session. I present a method to match a search session to its most relevant social positions based on the session's interaction data. I then suggest identifying related sessions from query logs that are likely to be issued by users with similar information needs. Novel learning features are then estimated from the session's social positions, related sessions, and interaction data. I evaluate the proposed system using four test collections from the TREC session track. This approach achieves state-of-the-art results compared with effective session-based search systems. I demonstrate that such a strong performance is mainly attributed to features that are derived from social positions' data

Diversified query expansion

La diversification des résultats de recherche (DRR) vise à sélectionner divers documents à partir des résultats de recherche afin de couvrir autant d’intentions que possible. Dans les approches existantes, on suppose que les résultats initiaux sont suffisamment diversifiés et couvrent bien les aspects de la requête. Or, on observe souvent que les résultats initiaux n’arrivent pas à couvrir certains aspects. Dans cette thèse, nous proposons une nouvelle approche de DRR qui consiste à diversifier l’expansion de requête (DER) afin d’avoir une meilleure couverture des aspects. Les termes d’expansion sont sélectionnés à partir d’une ou de plusieurs ressource(s) suivant le principe de pertinence marginale maximale. Dans notre première contribution, nous proposons une méthode pour DER au niveau des termes où la similarité entre les termes est mesurée superficiellement à l’aide des ressources. Quand plusieurs ressources sont utilisées pour DER, elles ont été uniformément combinées dans la littérature, ce qui permet d’ignorer la contribution individuelle de chaque ressource par rapport à la requête. Dans la seconde contribution de cette thèse, nous proposons une nouvelle méthode de pondération de ressources selon la requête. Notre méthode utilise un ensemble de caractéristiques qui sont intégrées à un modèle de régression linéaire, et génère à partir de chaque ressource un nombre de termes d’expansion proportionnellement au poids de cette ressource. Les méthodes proposées pour DER se concentrent sur l’élimination de la redondance entre les termes d’expansion sans se soucier si les termes sélectionnés couvrent effectivement les différents aspects de la requête. Pour pallier à cet inconvénient, nous introduisons dans la troisième contribution de cette thèse une nouvelle méthode pour DER au niveau des aspects. Notre méthode est entraînée de façon supervisée selon le principe que les termes reliés doivent correspondre au même aspect. Cette méthode permet de sélectionner des termes d’expansion à un niveau sémantique latent afin de couvrir autant que possible différents aspects de la requête. De plus, cette méthode autorise l’intégration de plusieurs ressources afin de suggérer des termes d’expansion, et supporte l’intégration de plusieurs contraintes telles que la contrainte de dispersion. Nous évaluons nos méthodes à l’aide des données de ClueWeb09B et de trois collections de requêtes de TRECWeb track et montrons l’utilité de nos approches par rapport aux méthodes existantes.Search Result Diversification (SRD) aims to select diverse documents from the search results in order to cover as many search intents as possible. For the existing approaches, a prerequisite is that the initial retrieval results contain diverse documents and ensure a good coverage of the query aspects. In this thesis, we investigate a new approach to SRD by diversifying the query, namely diversified query expansion (DQE). Expansion terms are selected either from a single resource or from multiple resources following the Maximal Marginal Relevance principle. In the first contribution, we propose a new term-level DQE method in which word similarity is determined at the surface (term) level based on the resources. When different resources are used for the purpose of DQE, they are combined in a uniform way, thus totally ignoring the contribution differences among resources. In practice the usefulness of a resource greatly changes depending on the query. In the second contribution, we propose a new method of query level resource weighting for DQE. Our method is based on a set of features which are integrated into a linear regression model and generates for a resource a number of expansion candidates that is proportional to the weight of that resource. Existing DQE methods focus on removing the redundancy among selected expansion terms and no attention has been paid on how well the selected expansion terms can indeed cover the query aspects. Consequently, it is not clear how we can cope with the semantic relations between terms. To overcome this drawback, our third contribution in this thesis aims to introduce a novel method for aspect-level DQE which relies on an explicit modeling of query aspects based on embedding. Our method (called latent semantic aspect embedding) is trained in a supervised manner according to the principle that related terms should correspond to the same aspects. This method allows us to select expansion terms at a latent semantic level in order to cover as much as possible the aspects of a given query. In addition, this method also incorporates several different external resources to suggest potential expansion terms, and supports several constraints, such as the sparsity constraint. We evaluate our methods using ClueWeb09B dataset and three query sets from TRECWeb tracks, and show the usefulness of our proposed approaches compared to the state-of-the-art approaches

The Group Creativity Exercise Getting MBAs to Work and Think Effectively in Groups

This experiential exercise is designed to engage participants in a process of group creativity that helps students lead or be a part of a creative team. The challenging and tangible nature of building a room­height tower provides a heightened experience that elicits many issues and strong emotions. The exercise provides a robust platform from which the instructor can choose which of many group creativity subtopics to emphasize. In addition to exercise instructions, guidance is given on how learning goals related to creativity techniques, group development, interpersonal dynamics, and leadership—can be addressed in a debriefing discussion. Both “pre­taught” and “retrospective” teaching approaches are discussed, although a retrospective approach in which the instructor makes connections with theory during debriefing discussions is recommended. The experience of learning by doing should yield more realistic and memorable understanding of group creativity than could be accomplished with readings and/or lecture alone

The Group Creativity Exercise Getting MBAs to Work and Think Effectively in Groups

This experiential exercise is designed to engage participants in a process of group creativity that helps students lead or be a part of a creative team. The challenging and tangible nature of building a room­height tower provides a heightened experience that elicits many issues and strong emotions. The exercise provides a robust platform from which the instructor can choose which of many group creativity subtopics to emphasize. In addition to exercise instructions, guidance is given on how learning goals related to creativity techniques, group development, interpersonal dynamics, and leadership—can be addressed in a debriefing discussion. Both “pre­taught” and “retrospective” teaching approaches are discussed, although a retrospective approach in which the instructor makes connections with theory during debriefing discussions is recommended. The experience of learning by doing should yield more realistic and memorable understanding of group creativity than could be accomplished with readings and/or lecture alone

Temporal models for mining, ranking and recommendation in the Web

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time

Multimedia Courseware for Interactive Teaching and Learning: Students' Needs and Perspectives

Education faces many new challenges in meeting the demands of teaching and learning for the 21st century. One of the new challenges is to integrate ICT (Information and communication technologies) in teaching and learning as a means of delivering alternative teaching. Multimedia technology, for example, has the potential to transform a traditional classroom into an unlimited imaginary world. This paper report on development and evaluation of a multimedia courseware for Design and Technology (RBT). An interactive CD was developed using the Adobe Flash CS6 software. Alpha and Beta testing have been carried out in the development process. 6 experts were assigned to evaluate the functionality of the interactive CD. In order to identify the usability of interactive CD, 103 respondents were involved in the survey by filling four-point Likert scaled questionnaire. The findings show that, the level of interactive CD usability is at a high level.  Based on this study, there are positive effects that we can see based on the use of multimedia elements in the education system. The meaningful benefits of using multimedia elements for learning include the presentation of various learning styles. The presentation of information usually integrates multimedia elements such as text, graphics, audio and video