Dataset Information

Characterizing the semantic and form-based similarity spaces of the mental lexicon by means of the multi-arrangement method.

ABSTRACT: Collecting human similarity judgments is instrumental to measuring and modeling neurocognitive representations (e.g., through representational similarity analysis) and has been made more efficient by the multi-arrangement task. While this task has been tested for collecting semantic similarity judgments, it is unclear whether it also lends itself to phonological and orthographic similarity judgments of words. We have extended the task to include these lexical modalities and compared the results between modalities and against computational models. We find that similarity judgments can be collected for all three modalities, although word forms were considered more difficult to sort and resulted in less consistent inter- and intra-rater agreement than semantics. For all three modalities we can construct stable group-level representational similarity matrices. However, these do not capture significant idiosyncratic similarity information unique to each participant. We discuss the potential underlying causes for differences between modalities and their effect on the application of the multi-arrangement task.

SUBMITTER: Ansteeg L

PROVIDER: S-EPMC9407019 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Characterizing the semantic and form-based similarity spaces of the mental lexicon by means of the multi-arrangement method.

Ansteeg Lukas L Leoné Frank F Dijkstra Ton T

Frontiers in psychology 20220811

Collecting human similarity judgments is instrumental to measuring and modeling neurocognitive representations (e.g., through representational similarity analysis) and has been made more efficient by the multi-arrangement task. While this task has been tested for collecting semantic similarity judgments, it is unclear whether it also lends itself to phonological and orthographic similarity judgments of words. We have extended the task to include these lexical modalities and compared the results ...[more]

PMID: 36033027

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Project description:The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (GL). VerbNet defines classes of verbs based on both their semantic and syntactic similarities, paying particular attention to shared diathesis alternations. For each class of verbs, VerbNet provides common semantic roles and typical syntactic patterns. For each syntactic pattern in a class, VerbNet defines a detailed semantic representation that traces the event participants from their initial states, through any changes and into their resulting states. The Generative Lexicon guided the structure of these representations. In GL, event structure has been integrated with dynamic semantic models in order to represent the attribute modified in the course of the event (the location of the moving entity, the extent of a created or destroyed entity, etc.) as a sequence of states related to time points or intervals. We applied that model to VerbNet semantic representations, using a class's semantic roles and a set of predicates defined across classes as components in each subevent. We will describe in detail the structure of these representations, the underlying theory that guides them, and the definition and use of the predicates. We will also evaluate the effectiveness of this resource for NLP by reviewing efforts to use the semantic representations in NLP tasks.

Dataset Information

Characterizing the semantic and form-based similarity spaces of the mental lexicon by means of the multi-arrangement method.

Publications

Characterizing the semantic and form-based similarity spaces of the mental lexicon by means of the multi-arrangement method.

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