Browsing by Author "Schulte im Walde, Sabine"

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Open Access
A clustering approach to automatic verb classification incorporating selectional preferences: model, implementation, and user manual
(2010) Schulte im Walde, Sabine; Schmid, Helmut; Wagner, Wiebke; Hying, Christian; Scheible, Christian
This report presents two variations of an innovative, complex approach to semantic verb classes that relies on selectional preferences as verb properties. The underlying linguistic assumption for this verb class model is that verbs which agree on their selectional preferences belong to a common semantic class. The model is implemented as a soft-clustering approach, in order to capture the polysemy of the verbs. The training procedure uses the Expectation-Maximisation (EM) algorithm (Baum, 1972) to iteratively improve the probabilistic parameters of the model, and applies the Minimum Description Length (MDL) principle (Rissanen, 1978) to induce WordNet-based selectional preferences for arguments within subcategorisation frames. One variation of the MDL principle replicates a standard MDL approach by Li and Abe (1998), the other variation presents an improved pruning strategy that outperforms the standard implementation considerably. Our model is potentially useful for lexical induction (e.g., verb senses, subcategorisation and selectional preferences, collocations, and verb alternations), and for NLP applications in sparse data situations. We demonstrate the usefulness of the model by a standard evaluation (pseudo-word disambiguation), and three applications (selectional preference induction, verb sense disambiguation, and semi-supervised sense labelling).
Open Access
Distributional measures of semantic abstraction
(2022) Schulte im Walde, Sabine; Frassinelli, Diego
This article provides an in-depth study of distributional measures for distinguishing between degrees of semantic abstraction. Abstraction is considered a “central construct in cognitive science” (Barsalou, 2003) and a “process of information reduction that allows for efficient storage and retrieval of central knowledge” (Burgoon et al., 2013). Relying on the distributional hypothesis, computational studies have successfully exploited measures of contextual co-occurrence and neighbourhood density to distinguish between conceptual semantic categorisations. So far, these studies have modeled semantic abstraction across lexical-semantic tasks such as ambiguity; diachronic meaning changes; abstractness vs. concreteness; and hypernymy. Yet, the distributional approaches target different conceptual types of semantic relatedness, and as to our knowledge not much attention has been paid to apply, compare or analyse the computational abstraction measures across conceptual tasks. The current article suggests a novel perspective that exploits variants of distributional measures to investigate semantic abstraction in English in terms of the abstract-concrete dichotomy (e.g., glory-banana) and in terms of the generality-specificity distinction (e.g., animal-fish), in order to compare the strengths and weaknesses of the measures regarding categorisations of abstraction, and to determine and investigate conceptual differences. In a series of experiments we identify reliable distributional measures for both instantiations of lexical-semantic abstraction and reach a precision higher than 0.7, but the measures clearly differ for the abstract-concrete vs. abstract-specific distinctions and for nouns vs. verbs. Overall, we identify two groups of measures, (i) frequency and word entropy when distinguishing between more and less abstract words in terms of the generality-specificity distinction, and (ii) neighbourhood density variants (especially target-context diversity) when distinguishing between more and less abstract words in terms of the abstract-concrete dichotomy. We conclude that more general words are used more often and are less surprising than more specific words, and that abstract words establish themselves empirically in semantically more diverse contexts than concrete words. Finally, our experiments once more point out that distributional models of conceptual categorisations need to take word classes and ambiguity into account: results for nouns vs. verbs differ in many respects, and ambiguity hinders fine-tuning empirical observations.