Parameters, Interactions, and Model Selection in Distributional Semantics

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Title: Parameters, Interactions, and Model Selection in Distributional Semantics
Authors: Lapesa, Gabriella
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Thesis advisor: Prof. Dr. Stefan Evert
Thesis referee: Prof. Dr. Kai-Uwe Kühnberger
Prof. Dr. Alessandro Lenci
Apl. Prof. Dr. Sabine Schulte im Walde
Abstract: Distributional Semantic Models are one of the possible answers produced in (computational) semantics to the question of what the meaning of a word is. The distributional semantic answer to this question is a usage-based one, as distributional semantics models (henceforth, DSMs) are employed to produce semantic representations of words from co-occurrence patterns in texts or documents. DSMs have proven to be useful in many applications in the domains of Natural Language Processing. Despite this progress, however, a full understanding of the different parameters governing a DSM and their influence on model performance (which, in fact, is also important for getting a better linguistic understanding of neural word embeddings) has not been achieved yet. This is precisely the goal of this dissertation. Taken together, the experiments presented in this thesis represent (to the best of our knowledge) the largest-scope study in which window and syntax-based DSMs have been tested in all parameter settings. As a further contribution, the thesis proposes a novel methodology for the interpretation of evaluation results: we employ linear regression as a statistical tool to understand the impact of different parameters on model performance. In this way, we achieve a solid understanding of the influence of specific parameters and parameter interactions on DSM performance, which can inform the selection of DSM settings that are robust to overfitting. This thesis has a strong focus on cognitive data, that is, on DSM parameters that lend themselves to a cognitive interpretation and on evaluation tasks in which DSMs are tested in their capability of mirroring speakers’ behavior in psychological tasks (semantic priming and free associations). One of the most important contributions of this thesis is the consistent finding that neighbor rank (i.e., the rank of a word among the distributional neighbors of a target) is a better indicator of semantic similarity/relatedness than the distance in the semantic space, which is commonly used in the literature. The cognitive interpretation of this result is straightforward: neighbor rank, which is evaluated systematically for the first time in this thesis, is able to capture asymmetry in the relation between two words, while distance metrics, commonly employed in distributional semantics, are symmetric.
Subject Keywords: distributional semantics; evaluation methodology; cognitive modeling
Issue Date: 22-Dec-2020
License name: Attribution-NonCommercial-NoDerivs 3.0 Germany
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Type of publication: Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:FB08 - E-Dissertationen

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