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 small-world 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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Monaikul, Natawut"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["(Clare) Xueqing Tang, Ph.D.","Kong-Cheng Wong, Ph.D.","Chris Tweddle, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-04-01T07:00:00Z","date_published":"2015-04-01T07:00:00Z","updated_at":"2026-07-24T02:24:36Z","subjects":["small world","mental lexicon","lexical network","graph theory","Computer Sciences","OS and Networks"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://opus.govst.edu/theses/59","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["(Clare) Xueqing Tang, Ph.D.","Kong-Cheng Wong, Ph.D.","Chris Tweddle, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Monaikul, Natawut"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-06-02T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["small world","mental lexicon","lexical network","graph theory","Computer Sciences","OS and Networks"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://opus.govst.edu/theses/59"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 small-world 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.
"]},{"key":"dc:title","label":"Title","values":["Towards an Integrated Model of the Mental Lexicon"]}]}],"canonical_facts":{"dc:contributor":["(Clare) Xueqing Tang, Ph.D.","Kong-Cheng Wong, Ph.D.","Chris Tweddle, Ph.D."],"dc:creator":["Monaikul, Natawut"],"dc:date.available":["2015-06-02T07:00:00Z"],"dc:description.abstract":["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 small-world 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.
"],"dc:identifier":["https://opus.govst.edu/theses/59"],"dc:subject":["small world","mental lexicon","lexical network","graph theory","Computer Sciences","OS and Networks"],"dc:title":["Towards an Integrated Model of the Mental Lexicon"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T02:24:36Z"}