Emerging Topics in Textual Modelling

This is the final version. Available on open access via the link in this recordOCL 2019: Object Constraint Language and Textual Modeling 2019. 19th International Workshop in OCL and Textual Modeling (OCL 2019) co-located with IEEE/ACM 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS 2019), 16 September 2019, Munich, GermanyThe 19th edition of the OCL workshop featured a lightning talk session where authors were invited to present their recent work and open questions related to textual modeling in general and OCL in particular. These 5 minute presentations triggered fruitful discussions within the OCL community on the usage of textual modeling, model validation, and specific technical points of the OCL specification. This community paper provides an overview of the presented contributions (one per section), as well as a summary of the questions and discussions they have triggered during the session

Structural and Temporal Topic Models of Feedbacks on Service Quality – A Path to Theory Development?

There is growing interest in applying computational methods in analysing large amount of data without sacrificing rigour in Information Systems research. In this paper, we demonstrate how the use of structural and temporal topic modelling can be employed to produce insights of both theoretical and practical importance from the analysis of textual comments on the quality of services in hospitals. As a first step, we revealed the thematic structures in the comments as topics which were aligned with the SERVQUAL dimensions. Following this, we established the temporal precedence among SERVQUAL factors based on the evolution of the topics over time. Theoretically, our findings were consistent with the emerging consensus on the nature of SERVQUAL dimensions from extant quantitative research and offer new propositions on the relationships among these dimensions. From the practice perspective, we produced quantified measures of factors associated with healthcare service experienc

Temporal Cross-Media Retrieval with Soft-Smoothing

Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.Comment: To appear in ACM MM 201

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Successful Instructional Diagrams by Ric Lowe, London, Kogan Page, 1993. ISBN: 0–7494–0711–5

Using Sensor Metadata Streams to Identify Topics of Local Events in the City

In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification