Towards an Integrated Model of the Mental Lexicon

<p>Several models have been proposed attempting to describe the mental lexicon-the abstract organization of words in the human mind. Numerous studies have shown that by representing the mental lexicon as a network, where nodes represent words and edges connect similar words using a metric based on some word feature, a <em>small-world</em> structure is formed. This property, pervasive in many real-world networks, implies processing efficiency and resiliency to node deletion within the system, explaining the need for such a robust network as the mental lexicon. However, each model considered a single word feature at a time, such as semantic or phonological information. Moreover, these studies modeled the mental lexicon as an unweighted graph. In this thesis, I expand upon these works by proposing a model that incorporates several word features into a weighted network. Analyses on this model applied to the English lexicon show that while this model does not exhibit the same small-world characteristics as a weighted graph, by setting a minimum threshold on the weights (reminiscent of action potential thresholds in neural networks), the resulting unweighted counterpart is a small-world network. These results suggest that a more integrated model of the mental lexicon can be adopted while affording the same computational benefits of a small-world network. An increased understanding of the structure of the mental lexicon can provide a stronger foundation for more accurate computational models of speech and text processing and word-learning.</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2015

Author dc:creator

• Monaikul, Natawut

Contributors dc:contributor

• (Clare) Xueqing Tang, Ph.D.
• Kong-Cheng Wong, Ph.D.
• Chris Tweddle, Ph.D.

• small world
• mental lexicon
• lexical network
• graph theory
• Computer Sciences
• OS and Networks

Repository record dc:identifier

https://opus.govst.edu/theses/59

OAI identifier oai:identifier

oai:opus.govst.edu:theses-1060