Maximilian Köper

Pattern-Based Distinction of Paradigmatic Relations for German Nouns, Verbs, Adjectives

This paper implements a simple vector space model relying on lexico-syntactic patterns to distinguish between the paradigmatic relations synonymy, antonymy and hypernymy. Our study is performed across word classes, and models the lexical relations between German nouns, verbs and adjectives. Applying nearest-centroid classification to the relation vectors, we achieve a precision of 59.80%, which significantly outperforms the majority baseline (χ 2, p<0.05). The best results rely on large-scale, noisy patterns, without significant improvements from various pattern generalisations and reliability filters. Analysing the classification shows that (i) antonym/synonym distinction is performed significantly better than synonym/hypernym distinction, and (ii) that paradigmatic relations between verbs are more difficult to predict than paradigmatic relations between nouns or adjectives.

Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling.

Recent models in distributional semantics consider derivational patterns (e.g., use → use + f ul ) as the result of a compositional process, where base term and affix are combined. We exploit such models for German particle verbs (PVs), and focus on the task of learning a mapping function between base verbs and particle verbs. Our models apply particle-verb motivated training-space restrictions relying on nearest neighbors, as well as recent advances from zeroshot-learning. The models improve the mapping between base terms and derived terms for a new PV derivation dataset, and also across existing derivation datasets for German and English.

Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality.

This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information. We focus on nominal vs. verbal compounds, and zoom into lexical, empirical and perceptual target properties to explore the contribution of the visual modality. Our experiments show that (i) visual features contribute differently for verbs than for nouns, and (ii) images complement textual information, if (a) the textual modality by itself is poor and appropriate image subsets are used, or (b) the textual modality by itself is rich and large (potentially noisy) images are added.

Optimizing Visual Representations in Semantic Multi-modal Models with Dimensionality Reduction, Denoising and Contextual Information

This paper improves visual representations for multi-modal semantic models, by (i) applying standard dimensionality reduction and denoising techniques, and by (ii) proposing a novel technique \( ContextVision \) that takes corpus-based textual information into account when enhancing visual embeddings. We explore our contribution in a visual and a multi-modal setup and evaluate on benchmark word similarity and relatedness tasks. Our findings show that NMF, denoising as well as \( ContextVision \) perform significantly better than the original vectors or SVD-modified vectors.

Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses

Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for multi-word units by propagating abstractness to verb-noun pairs which lead to better metaphor detection, iii) we overcome the limitation of learning a single rating per word and show that multisense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3 million English words and multi-words as wellwords refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for multi-word units by propagating abstractness to verb-noun pairs which lead to better metaphor detection, iii) we overcome the limitation of learning a single rating per word and show that multisense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3 million English words and multi-words as well as automatically created sense-specific abstractness ratings.

Distinguishing Literal and Non-Literal Usage of German Particle Verbs

This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage. A random forest improving standard features (e.g., bagof-words; affective ratings) with PV-specific information and abstraction over common nouns significantly outperforms the majority baseline. In addition, PV-specific classification experiments demonstrate the role of shared particle semantics and semantically related base verbs in PV meaning shifts.

Automatic Semantic Classification of German Preposition Types: Comparing Hard and Soft Clustering Approaches across Features

This paper addresses an automatic classification of preposition types in German, comparing hard and soft clustering approaches and various window- and syntax-based co-occurrence features. We show that (i) the semantically most salient preposition features (i.e., subcategorised nouns) are the most successful, and that (ii) soft clustering approaches are required for the task but reveal quite different attitudes towards predicting ambiguity.