The goal of the current paper is to introduce an existing, but as far as we know not yet linguist... more The goal of the current paper is to introduce an existing, but as far as we know not yet linguistically applied Multidimensional Scaling (MDS) method called Individual
In this paper, we address the general problem of dealing with a complex and interwoven set of inf... more In this paper, we address the general problem of dealing with a complex and interwoven set of influential factors, potentially both linguistic and extralinguistic factors, behind linguistic choices. We do so by investigating the rare phenomenon of swearing with diseases in Dutch, rather than with the more common Western taboo concepts of (among others) religion and sexuality. The standing hypothesis is that swearing with diseases is related to the Calvinistic cultural background of the Dutch. Methodologically speaking, we perform a corpus-based analysis of a large database of location-specific tweets from Flanders and The Netherlands in which we observe "bad language". Since the hypothesized influential factor of Calvinism shows an outspoken geographical pattern, we intuitively expect clear overlap of the area in which we observe a preference for disease-based swearing and the area in which Calvinism is the predominant religion. We do not find a straightforward overlap in the geography of Calvinism and disease-based swearing. Although the locations where disease-based swearing is conspicuously frequent are all within the Calvinistic area of The Netherlands, disease-based swearing is more likely in the highly-urbanized region around Amsterdam. Therefore, we can only conclude that urbanity, socioeconomic factors, religious affiliation and nationality play intertwined roles in the choice for disease-based swearing versus swearing with words from other lexical domains. Consequently, we end up with a complex and interwoven set of extra-linguistic factors that influence the lexical choice for a taboo word from a specific domain for swearing.
This study uses multi-dimensional analysis to investigate the distribution of a varied set of lin... more This study uses multi-dimensional analysis to investigate the distribution of a varied set of linguistic features in blogs, with the goal of finding dimensions of functional variation that allow to distinguish between two opposing occupational sectors, the Humanities and Exact Sciences. A corpus of 9 million words sampled across a wide range of topics and written by men in their twenties is tagged and functions as input for a frequency matrix, on which a factor analysis is carried out to identify quantitatively the co-occurrence relations among linguistic features. The interpretation of the four resultant factors reveals clear axes of variation, allowing to some extent to differentiate the professional sectors. Furthermore, insight is gained into certain functional tendencies across dimension borders.
Photovoltaic (PV) event log data are typically underexploited mainly because of the heterogeneity... more Photovoltaic (PV) event log data are typically underexploited mainly because of the heterogeneity of the events. To unlock these data, we propose an explorative methodology that overcomes two main constraints: (1) the rampant variability in event labelling, and (2) the unavailability of a clear methodology to traverse the amount of generated event sequences. With respect to the latter constraint, we propose to integrate heterogeneous event logs from PV plants with a semantic model of the events. However, since different manufacturers report events at different levels of granularity and since the finest granularity may sometimes not be the right level of detail for exploitable insights, we propose to explore PV event logs with Multi-level Sequential Pattern Mining. On the basis of patterns that are retrieved across taxonomic levels, several event-related processes can be optimized, e.g. by predicting PV inverter failures. The methodology is validated on real-life data from two PV pla...
The goal of the current paper is to introduce an existing, but as far as we know not yet linguist... more The goal of the current paper is to introduce an existing, but as far as we know not yet linguistically applied Multidimensional Scaling (MDS) method called Individual
In this paper, we address the general problem of dealing with a complex and interwoven set of inf... more In this paper, we address the general problem of dealing with a complex and interwoven set of influential factors, potentially both linguistic and extralinguistic factors, behind linguistic choices. We do so by investigating the rare phenomenon of swearing with diseases in Dutch, rather than with the more common Western taboo concepts of (among others) religion and sexuality. The standing hypothesis is that swearing with diseases is related to the Calvinistic cultural background of the Dutch. Methodologically speaking, we perform a corpus-based analysis of a large database of location-specific tweets from Flanders and The Netherlands in which we observe "bad language". Since the hypothesized influential factor of Calvinism shows an outspoken geographical pattern, we intuitively expect clear overlap of the area in which we observe a preference for disease-based swearing and the area in which Calvinism is the predominant religion. We do not find a straightforward overlap in the geography of Calvinism and disease-based swearing. Although the locations where disease-based swearing is conspicuously frequent are all within the Calvinistic area of The Netherlands, disease-based swearing is more likely in the highly-urbanized region around Amsterdam. Therefore, we can only conclude that urbanity, socioeconomic factors, religious affiliation and nationality play intertwined roles in the choice for disease-based swearing versus swearing with words from other lexical domains. Consequently, we end up with a complex and interwoven set of extra-linguistic factors that influence the lexical choice for a taboo word from a specific domain for swearing.
This study uses multi-dimensional analysis to investigate the distribution of a varied set of lin... more This study uses multi-dimensional analysis to investigate the distribution of a varied set of linguistic features in blogs, with the goal of finding dimensions of functional variation that allow to distinguish between two opposing occupational sectors, the Humanities and Exact Sciences. A corpus of 9 million words sampled across a wide range of topics and written by men in their twenties is tagged and functions as input for a frequency matrix, on which a factor analysis is carried out to identify quantitatively the co-occurrence relations among linguistic features. The interpretation of the four resultant factors reveals clear axes of variation, allowing to some extent to differentiate the professional sectors. Furthermore, insight is gained into certain functional tendencies across dimension borders.
Photovoltaic (PV) event log data are typically underexploited mainly because of the heterogeneity... more Photovoltaic (PV) event log data are typically underexploited mainly because of the heterogeneity of the events. To unlock these data, we propose an explorative methodology that overcomes two main constraints: (1) the rampant variability in event labelling, and (2) the unavailability of a clear methodology to traverse the amount of generated event sequences. With respect to the latter constraint, we propose to integrate heterogeneous event logs from PV plants with a semantic model of the events. However, since different manufacturers report events at different levels of granularity and since the finest granularity may sometimes not be the right level of detail for exploitable insights, we propose to explore PV event logs with Multi-level Sequential Pattern Mining. On the basis of patterns that are retrieved across taxonomic levels, several event-related processes can be optimized, e.g. by predicting PV inverter failures. The methodology is validated on real-life data from two PV pla...
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Papers by Tom Ruette