Employing an Enhanced Interval Approach to encode words into Linear General Type-2 fuzzy sets for Computing With Words applications

In 1996, Zadeh coined Computing With Words (CWWs) to be a methodology in which words are used instead of numbers for computing and reasoning. One of the main challenges which faced the CWWs paradigm has been modelling words adequately. Mendel has pointed out that the CWWs paradigm should employ type-2 fuzzy logic to model words. This paper proposes employing an Enhanced Interval Approach (EIA) to create Linear General Type-2 (LGT2) fuzzy sets from Interval Type-2 (IT2) fuzzy sets to encode words for CWWs applications. We have performed experiments on 18 words belonging to 3 different linguistic variables (having 6 linguistic terms each). Interval data has been collected from 17 subjects and 18 linguistic terms have been modeled with IT2 fuzzy sets using EIA. The proposed conversion approach uses several key points within the parameters of IT2 fuzzy sets to redesign the linguistic variable using LGT2 fuzzy sets. Both IT2 and LGT2 fuzzy sets have been evaluated within a CWWs Framework, which aims to mimic the ability of humans to communicate and manipulate perceptions via words. The comparison results show that LGT2 fuzzy sets can be better than IT2 fuzzy sets in mimicking human reasoning as well as learning and adaptation since the progressive Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for LGT2 based CWWs Framework converge faster and are lower than those for IT2 based CWWs Framework

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

Volume CXIV, Number 4, November 7, 1996

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population.Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014.Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosis) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto's thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%.Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespa

A Computing with Words Framework for Ambient Intelligence

One of the challenges for the fast advancing ambient intelligence vision is to maintain the perception of the future home being a safe place where the inhabitants relax, enjoy and feel comfortable. In a home environment, the role of technology is approached skeptically; hence, there is a need to provide highlevel communication between the users and the intelligent space so that the users get accustomed to what technology has to offer. In this paper, we introduce a Computing with Words (CWWs) framework which provides human-like reasoning via abstraction and high-level description of thoughts, feelings, etc. This framework can be considered as an exocortex as it aids the human thinking outside the bio-brain. The CWWs paradigm aims to establish high level home-human communication, which is necessary for people to perceive the technology as a cooperative guide and an improvement on their life styles. © 2013 IEEE