‘Understanding’ differs between English and German: Capturing systematic language differences of complex words

The pluralization palette: unveiling semantic clusters in English nominal pluralization through distributional semantics

Using distributional semantics, we show that English nominal pluralization exhibits semantic clusters. For instance, the change in semantic space from singulars to plurals differs depending on whether a word denotes, e.g., a fruit, or an animal. Languages with extensive noun classes such as Swahili and Kiowa distinguish between these kind of words in their morphology. In English, even though not marked morphologically, plural semantics actually also varies by semantic class. A semantically informed method, CosClassAvg, is introduced that is compared to two other methods, one implementing a fixed shift from singular to plural, and one creating plural vectors from singular vectors using a linear mapping (FRACSS). Compared to FRACSS, CosClassAvg predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of vector length, but somewhat less similar in terms of orientation. Both FRACSS and CosClassAvg outperform the method using a fixed shift vector to create plural vectors, which does not do justice to the intricacies of English plural semantics. A computational modeling study revealed that the observed difference between the plural semantics generated by these three methods carries over to how well a computational model of the listener can understand previously unencountered plural forms. Among all methods, CosClassAvg provides a good balance for the trade-off between productivity (being able to understand novel plural forms) and faithfulness to corpus-extracted plural vectors (i.e., understanding the particulars of the meaning of a given plural form).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11525-024-09428-9.

Morphological decomposition in Chinese compound word recognition: Electrophysiological evidence

The present study examined the effect of both morphological complexity and semantic transparency in Chinese compound word recognition. Using a visual lexical decision task, our electrophysiological results showed that transparent and opaque compounds induced stronger Left Anterior Negativity (LAN) than monomorphemic words. This result suggests that Chinese compounds might be decomposed into their constituent morphemes at the lemma level, whereas monomorphemic words are accessed as a whole-word lemma directly from the form level. In addition, transparent and opaque compounds produced a similar N400 as each other, suggesting that transparency did not show an effect on the involvement of constituent morphemes during access to the whole-word lemma. Two behavioral experiments additionally showed similar patterns to the EEG results. These findings support morphological decomposition for compounds at the lemma level as proposed by the full-parsing model, and no evidence is found to support the role of transparency during Chinese compound word recognition.

Mental Representation of Word Family Structure: The Case of German Infinitives, Conversion Nouns and Other Morphologically Related Forms

This study investigates how two non-finite forms, infinitives and conversion nouns, are represented in the mind of L1 and L2 speakers and what is their relationship to other members of the corresponding word family. German native speakers and proficient German learners with Czech as L1 participated in four overt priming experiments involving a grammatical judgement task. We investigated the relationship between infinitives (Experiment 1) and conversion nouns (Experiment 2) and formally identical verbal or noun forms. We further focussed on the relationship between conversion nouns and regular nominal derivation forms with two derivational suffixes: -er and -ung (Experiments 3 and 4). Our results show that the two non-finite forms differ in their relations to other members of a word family and do not constitute a special class of non-finites as suggested in previous literature. While German infinitives seem to be closer related to finite verbal forms, conversion nouns behave in the same way as other regular nominal derivatives within the same word family. As for the German L1 and L2 contrast, no significant difference in the mental representation of the examined forms was found. This finding suggests that with respect to the explored phenomena, proficient learners rely on the same linguistic organisation as L1 speakers.

From decomposition to distributed theories of morphological processing in reading

The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit representations of their constituent morphemes. In distributed accounts, in contrast, morphological sensitivity arises from the tuning of finer-grained representations to useful statistical regularities in the form-to-meaning mapping, without the need for explicit morpheme representations. In this theoretically guided review, we summarize research into the mechanisms of morphological processing, and discuss findings within the context of decomposition and distributed accounts. Although many findings fit within a decomposition model of morphological processing, we suggest that the full range of results is more naturally explained by a distributed approach, and discuss additional benefits of adopting this perspective.

Derivational Morphology in Agrammatic Aphasia: A Comparison Between Prefixed and Suffixed Words

Although a relatively large number of studies on acquired language impairments have tested the case of derivational morphology, none of these have specifically investigated whether there are differences in how prefixed and suffixed derived words are impaired. Based on linguistic and psycholinguistic considerations on prefixed and suffixed derived words, differences in how these two types of derivations are processed, and consequently impaired, are predicted. In the present study, we investigated the errors produced in reading aloud simple, prefixed, and suffixed words by three German individuals with agrammatic aphasia (NN, LG, SA). We found that, while NN and LG produced similar numbers of errors with prefixed and suffixed words, SA showed a selective impairment for prefixed words. Furthermore, NN and SA produced more errors specifically involving the affix with prefixed words than with suffixed words. We discuss our findings in terms of relative position of stem and affix in prefixed and suffixed words, as well as in terms of specific properties of prefixes and suffixes.

Semantic transparency effects in German compounds: A large dataset and multiple-task investigation

In the present study, we provide a comprehensive analysis and a multi-dimensional dataset of semantic transparency measures for 1810 German compound words. Compound words are considered semantically transparent when the contribution of the constituents' meaning to the compound meaning is clear (as in airport), but the degree of semantic transparency varies between compounds (compare strawberry or sandman). Our dataset includes both compositional and relatedness-based semantic transparency measures, also differentiated by constituents. The measures are obtained from a computational and fully implemented semantic model based on distributional semantics. We validate the measures using data from four behavioral experiments: Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study. We demonstrate that different semantic effects emerge in different behavioral tasks, which can only be captured using a multi-dimensional approach to semantic transparency. We further provide the semantic transparency measures derived from the model for a dataset of 40,475 additional German compounds, as well as for 2061 novel German compounds.

Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

Models that represent meaning as high-dimensional numerical vectors—such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec—have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.