Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses

Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. Open-ended survey responses serve as a valuable source of information. This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in employee satisfaction surveys. Our approach aims to overcome the inherent noise and variability in these responses, enabling a comprehensive analysis of sentiments that can support employee lifecycle management. Through response clustering we identify six key aspects (salary, schedule, contact, communication, personal attention, agreements), which we validate by domain experts. We compile a dataset of 1,458 Dutch survey responses, revealing label imbalance in aspects and sentiments. We propose few-shot approaches for ABSA based on Dutch BERT models, and compare them against bag-of-words and zero-shot baselines. Our work significantly contributes to the field of ABSA by demonstrating the first successful application of Dutch pre-trained language models to aspect-based sentiment analysis in the domain of human resources (HR).Comment: Accepted at NLP4HR Workshop at EACL202

VIVACE: A Framework for the Systematic Evaluation of Variability Support in Process-Aware Information Systems

CONTEXT The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. OBJECTIVE The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). METHOD We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. RESULTS The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. CONCLUSIONS VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle

Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation

Objective. The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). Approach. This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. Main results. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. Significance. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.EC/H2020/641858/EU/Understanding and predicting developmental language abilities and disorders in multilingual Europe/PREDICTABL

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). Method: We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. Results: The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. Conclusions: VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle. (C) 2014 Elsevier B.V. All rights reserved.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011.Ayora Esteras, C.; Torres Bosch, MV.; Weber, B.; Reichert, M.; Pelechano Ferragud, V. (2015). VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems. Information and Software Technology. 57:248-276. https://doi.org/10.1016/j.infsof.2014.05.009S2482765

Linguistic Variation from Cognitive Variability: The Case of English \u27Have\u27

In this dissertation, I seek to construct a model of meaning variation built upon variability in linguistic structure, conceptual structure, and cognitive makeup, and in doing so, exemplify an approach to studying meaning that is both linguistically principled and neuropsychologically grounded. As my test case, I make use of the English lexical item ‘have\u27 by proposing a novel analysis of its meaning based on its well-described variability in English and its embed- ding into crosslinguistically consistent patterns of variation and change.I support this analysis by investigating its real-time comprehension patterns through behavioral, electropsychophysiological, and hemodynamic brain data, thereby incorporating dimensions of domain-general cognitive variability as crucial determinants of linguistic variability. Per my account, ‘have\u27 retrieves a generalized relational meaning which can give rise to a conceptually constrained range of readings, depending on the degree of causality perceived from either linguistic or contextual cues. Results show that comprehenders can make use of both for ‘have\u27-sentences, though they vary in the degree to which they rely on each.At the very broadest level, the findings support a model in which the semantic distribution of ‘have\u27 is inherently principled due to a unified conceptual structure. This underlying conceptual structure and relevant context cooperate in guiding comprehension by modulating the salience of potential readings, as comprehension unfolds; though, this ability to use relevant context–context-sensitivity–is variable but systematic across comprehenders. These linguistic and cognitive factors together form the core of normal language processing and, with a gradient conceptual framework, the minimal infrastructure for meaning variation and change

Linguistic variation from cognitive variability: the case of English \u27have\u27

In this dissertation, I seek to construct a model of meaning variation built upon variability in linguistic structure, conceptual structure, and cognitive makeup, and in doing so, exemplify an approach to studying meaning that is both linguistically principled and neuropsychologically grounded. As my test case, I make use of the English lexical item \u27have\u27 by proposing a novel analysis of its meaning based on its well-described variability in English and its embedding into crosslinguistically consistent patterns of variation and change. I support this analysis by investigating its real-time comprehension patterns through behavioral, electropsychophysiological, and hemodynamic brain data, thereby incorporating dimensions of domain-general cognitive variability as crucial determinants of linguistic variability. Per my account, \u27have\u27 retrieves a generalized relational meaning which can give rise to a conceptually constrained range of readings, depending on the degree of causality perceived from either linguistic or contextual cues. Results show that comprehenders can make use of both for \u27have\u27-sentences, though they vary in the degree to which they rely on each. At the very broadest level, the findings support a model in which the semantic distribution of \u27have\u27 is inherently principled due to a unified conceptual structure. This underlying conceptual structure and relevant context cooperate in guiding comprehension by modulating the salience of potential readings, as comprehension unfolds; though, this ability to use relevant context--context-sensitivity--is variable but systematic across comprehenders. These linguistic and cognitive factors together form the core of normal language processing and, with a gradient conceptual framework, the minimal infrastructure for meaning variation and change