Towards hierarchical affiliation resolution: framework, baselines, dataset

Author affiliations provide key information when attributing academic performance like publication counts. So far, such measures have been aggregated either manually or only to top-level institutions, such as universities. Supervised affiliation resolution requires a large number of annotated alignments between affiliation strings and known institutions, which are not readily available. We introduce the task of unsupervised hierarchical affiliation resolution, which assigns affiliations to institutions on all hierarchy levels (e.g. departments), discovering the institutions as well as their hierarchical ordering on the fly. From the corresponding requirements, we derive a simple conceptual framework based on the subset partial order that can be extended to account for the discrepancies evident in realistic affiliations from the Web of Science. We implement initial baselines and provide datasets and evaluation metrics for experimentation. Results show that mapping affiliations to known institutions and discovering lower-level institutions works well with simple baselines, whereas unsupervised top-level- and hierarchical resolution is more challenging. Our work provides structured guidance for further in-depth studies and improved methodology by identifying and discussing a number of observed difficulties and important challenges that future work needs to address

Analyzing Authentic Texts for Language Learning: Web-based Technology for Input Enrichment and Question Generation

Acquisition of a language largely depends on the learner's exposure to and interaction with it. Our research goal is to explore and implement automatic techniques that help create a richer grammatical intake from a given text input and engage learners in making form-meaning connections during reading. A starting point for addressing this issue is the automatic input enrichment method, which aims to ensure that a target structure is richly represented in a given text. We demonstrate the high performance of our rule-based algorithm, which is able to detect 87 linguistic forms contained in an official curriculum for the English language. Showcasing the algorithm's capability to differentiate between the various functions of the same linguistic form, we establish the task of tense sense disambiguation, which we approach by leveraging machine learning and rule-based methods. Using the aforementioned technology, we develop an online information retrieval system FLAIR that prioritizes texts with a rich representation of selected linguistic forms. It is implemented as a web search engine for language teachers and learners and provides effective input enrichment in a real-life teaching setting. It can also serve as a foundation for empirical research on input enrichment and input enhancement. The input enrichment component of the FLAIR system is evaluated in a web-based study that demonstrates that English teachers prefer automatic input enrichment to standard web search when selecting reading material for class. We then explore automatic question generation for facilitating and testing reading comprehension as well as linguistic knowledge. We give an overview of the types of questions that are usually asked and can be automatically generated from text in the language learning context. We argue that questions can facilitate the acquisition of different linguistic forms by providing functionally driven input enhancement, i.e., by ensuring that the learner notices and processes the form. The generation of well-established and novel types of questions is discussed and examples are provided; moreover, the results from a crowdsourcing study show that automatically generated questions are comparable to human-written ones

Between Nodes and Edges: Possibilities and Limits of Network Analysis in Art History

This article examines a number of prominent network analysis projects in the field of art history and explores the unique promises and problems that this increasingly significant mode of analysis presents to the discipline. By bringing together projects that conceptualize art historical networks in different ways, it demonstrates how established theories and methods of art history—such as feminist and postcolonial theory—may be productively used in conjunction with quantitative/computational approaches to art historical analysis. It argues that quantitative analysis of art and its networks can expand the qualitative approaches that have traditionally defined the field, particularly if theorizing is not positioned as something to be overcome by quantifiable data, but rather regarded as a fundamental means of understanding how data is structured, examined, and visualized. Although network analysis has a great potential to reveal the significance of actors marginalized by canonical narratives of art history and track unforeseen transnational and intercommunal histories of artistic exchange, it may also paradoxically silence social hierarchies and mechanisms of marginalization, as well as historical disruptions to them, if the principles underlying the data are not interrogated from the outset. Ultimately, the article proposes much can be gained when art historians work with and through digital technologies, using critical visual analysis to examine the epistemologies which structure the network visualizations that they produce

A metaphorical characterization of D.J. Opperman's Komas uit 'n bamboesstok in terms of relevance theory and the contemporary theory of metaphor

Includes abstract.Includes bibliographical references (leaves 188-198).Similar to previous academic papers on the topic of relevance, this dissertation too responds to specific claims proposed by relevance theory. The relevance-theoretic account of the recovery of metaphorical interpretations is of particular interest and is considered relative to the assertions of Lakoff and Johnson (1980, 1999) and Lakoff and Turner (1989), proponents of the cognitive linguistic approach to metaphor. The study has three distinct parts. It firstly explores the treatment of metaphor within the framework of Relevance Theory. Secondly, it argues via the assertions of the Contemporary Theory of Metaphor that the relevance-theoretic treatment of metaphor is in violation of one of its fundamental claims about cognition, namely, that human cognition tends to be geared to the maximization of relevance. Thirdly the salience of the cognitive-linguistic view of metaphor is illustrated through the metaphorical characterisation of D.J. Opperman’s (1979) volume of poetry, Komas uit ‘n bamboesstok