When the Whole Is Greater than the Sum of Its Parts: On the Conceptualization and Measurement of Populist Attitudes and Other Multidimensional Constructs

Multidimensional concepts are non-compensatory when higher values on one component cannot offset lower values on another. Thinking of the components of a multidimensional phenomenon as non-compensatory rather than substitutable can have wide-ranging implications, both conceptually and empirically. To demonstrate this point, we focus on populist attitudes that feature prominently in contemporary debates about liberal democracy. Given similar established public opinion constructs, the conceptual value of populist attitudes hinges on its unique specification as an attitudinal syndrome, which is characterized by the concurrent presence of its non-compensatory concept subdimensions. Yet this concept attribute is rarely considered in existing empirical research. We propose operationalization strategies that seek to take the distinct properties of non-compensatory multidimensional concepts seriously. Evidence on five populism scales in 12 countries reveals the presence and consequences of measurement-concept inconsistencies. Importantly, in some cases, using conceptually sound operationalization strategies upsets previous findings on the substantive role of populist attitudes.

NeutroAlgebra is a Generalization of Partial Algebra

In this paper we recall, improve, and extend several definitions, properties and applications of our previous 2019 research referred to NeutroAlgebras and AntiAlgebras (also called NeutroAlgebraic Structures and respectively AntiAlgebraic Structures). Let be an item (concept, attribute, idea, proposition, theory, etc.). Through the process of neutrosphication, we split the nonempty space we work on into three regions {two opposite ones corresponding to and , and one corresponding to neutral (indeterminate) (also denoted ) between the opposites}, which may or may not be disjoint – depending on the application, but they are exhaustive (their union equals the whole space). A NeutroAlgebra is an algebra which has at least one NeutroOperation or one NeutroAxiom (axiom that is true for some elements, indeterminate for other elements, and false for the other elements). A Partial Algebra is an algebra that has at least one Partial Operation, and all its Axioms are classical (i.e. axioms true for all elements). Through a theorem we prove that NeutroAlgebra is a generalization of Partial Algebra, and we give examples of NeutroAlgebras that are not Partial Algebras. We also introduce the NeutroFunction (and NeutroOperation).

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

Background To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. Methods A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. Results Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. Conclusions This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

Self-Adaptive QoS-Aware Web Service Discovery Using Ontology Approach

In recent years, although semantic has been widely used in service discovery mechanisms, it still needs to exploit all semantic aspects included in service documents so that the discovered service can highly be relevant with user request. Moreover, it also needs to consider self-adaptability in discovering the services which can adapt to searching conditions or parameters in order to find other suitable and potential services if no feasible solution could exactly satisfy user QoS requirements. Therefore, this paper proposes a novel self-adaptive QoS-based service discovery mechanism which can adapt the discovery process with the help of semantically structured ontology trees if unexpected results are encountered. The discovery process matches the equivalences between service advertisement and requirement using three similarity evaluation criteria namely concept, attribute and constraint similarity. This discovery process is repeated until feasible solution is found and a set of most suitable services are returned to the users. The authors prototype their system called SQoSD to evaluate the efficiency and adaptability compared with OWLS-CPS and RQSS. The experimental results prove that our mechanism is superior to the other compared mechanisms.