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Complex Words as Shortest Paths in the Network of Lexical Knowledge.

Sergei Monakhov¹, Holger Diessel¹

¹Department of English and American Studies, Friedrich-Schiller University of Jena.

Cognitive Science

|October 28, 2024

Summary

This study introduces a novel computational model for morphology using graph theory and network science. It demonstrates that the shortest path analysis effectively segments complex words and predicts word acceptability, offering insights into language structure.

Keywords:

Complex words Construction morphology English morphology Grammar network Lexical knowledge Network science Word recognition and production

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Area of Science:

Computational Linguistics
Cognitive Science
Network Science

Background:

Lexical models debate the representation of complex words, contrasting morpheme-based and word-based approaches.
Existing models struggle to fully capture the nuances of morphological structure and processing.

Purpose of the Study:

To propose a novel computational model of morphology based on graph theory and network science.
To investigate word learning, storage, and processing using the concept of shortest paths in a morphological network.

Main Methods:

Developed a computational model based on graph theory, applying the network science concept of shortest paths.
Conducted two experiments with human participants to test the model.
Validated the model against behavioral data from the English Lexicon Project.

Main Results:

Shortest path analysis successfully segmented new complex words into morphemes.
Attested English words were found to represent the shortest paths within the morphological network.
Novel words formed along established shortest paths received higher acceptability ratings.

Conclusions:

Network science, particularly shortest path analysis, offers a powerful framework for understanding morphological structure and processing.
The proposed model supports a usage-based perspective on grammar, highlighting the role of optimal paths in word formation and perception.