Predicting the basic level in a hierarchy of concepts

The “basic level”, according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels. We argue that applications that use concept hierarchies could improve their user interfaces if they ‘knew’ which concepts are the basic level concepts. This paper examines to what extent the basic level can be learned from data. We test the utility of three types of concept features, that were inspired by the basic level theory: lexical features, structural features and frequency features. We evaluate our approach on WordNet, and create a training set of manually labelled examples from different part of WordNet. Our findings include that the basic level concepts can be accurately identified within one domain. Concepts that are difficult to label for humans are also harder to classify automatically. Our experiments provide insight into how classification performance across different parts of the hierarchy could be improved, which is necessary for identification of basic level concepts on a larger scale

Towards Transparent Linguistic Analysis of Dutch Newspaper Article Genres using Machine Learning

Systematic study of genre in newspapers sheds light on the development of journalism discourse. The genre conventions that can be discerned in a newspaper text signal the underlying discursive norms and practices of journalism as a profession. Historical newspapers are increasingly becoming available thanks to digital newspaper archives (in the Netherlands available through Delpher.nl), providing the opportunity for large-scale empirical research. However, the digital archives do not contain fine-grained genre information that is required for this purpose. Therefore, we use machine learning to automatically assign genre labels to newspaper articles.Machine learning facilitates substantial improvements to the outcomes of existing research by providing increased amounts of enriched data. However, the decision-making process of the machine learning pipeline needs to be verified. Our previous findings (Bilgin et al., 2018) show that accuracy scores alone are not enough to assess the performance of these pipelines and that making an informed choice not only empowers optimal study of the historical development of genre, but also increases the trustworthiness of the results. This work shows that employing a transparent approach driven by model interpretability facilitates fair comparison as well as validation of the underlying decision-making criteria of the machine learning pipelines. The criteria are presented in the form of important features, creating insights on interactions between genre-related linguistic features and bag-of-words features.</p

Comparing methods for finding search sessions on a specified topic: A double case study

Users searching for different topics in a collection may show distinct search patterns. To analyze search behavior of users searching for a specific topic, we need to retrieve the sessions containing this topic. In this paper, we compare different topic representations and approaches to find topic-specific sessions. We conduct our research in a double case study of two topics, World War II and feminism, using search logs of a historical newspaper collection. We evaluate the results using manually created ground truths of over 600 sessions per topic. The two case studies show similar results: The query-based methods yield high precision, at the expense of recall. The document-based methods find more sessions, at the expense of precision. In both approaches, precision improves significantly by manually curating the topic representations. This study demonstrates how different methods to find sessions containing specific topics can be applied by digital humanities scholars and practitioners

Towards a linear general type-2 fuzzy logic based approach for computing with words

Within the last two decades, the paradigm of Computing With Words (CWW) has been gaining more attention. Mainly, CWW has an exciting vision which tries to tackle the problem of human intelligence by taking the human mind as a role model. The human intelligence has been investigated by various disciplines including psychology, philosophy, neuroscience, linguistics, computer science, and cognitive sciences. Notably, it is not a straightforward task to map the human's brain reasoning into computer processes. In this paper, we propose to facilitate such mapping by investigating a key element, which is to identify the step-by-step formation of perceptual judgments. Herein, we first introduce an approach that employs general type-2 fuzzy logic to dynamically model the human perceptions based on the human experience. This approach can be regarded as a step to enable the CWW vision. We have deployed the proposed approach in real-world settings and we will present two sets of real-world experiments which were conducted in the intelligent apartment (iSpace) in the University of Essex. The first set of experiments demonstrates the results of the proposed approach for the adaptive modeling of ambient luminance perception. In the second set of experiments, we show that our approach performs better in the rule base evaluation processing time and in output accuracy with comparison to an interval type-2 fuzzy logic system. © 2013 Springer-Verlag Berlin Heidelberg