{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110573"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110573","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Spatial vs. graphical representation of distributional semantic knowledge","abstract":"Made available in DSpace on 2021-09-17T01:11:18Z (GMT). No. of bitstreams: 2 MAO-THESIS-2021.pdf: 1183634 bytes, checksum: 9e51e6a8017947611a5d3a89fd1b9ae9 (MD5) LICENSE.txt: 4207 bytes, checksum: d1fd63103009bea4dcc312059e33bac0 (MD5) Previous issue date: 2021-04-27","abstract_html":"Made available in DSpace on 2021-09-17T01:11:18Z (GMT). No. of bitstreams: 2 MAO-THESIS-2021.pdf: 1183634 bytes, checksum: 9e51e6a8017947611a5d3a89fd1b9ae9 (MD5) LICENSE.txt: 4207 bytes, checksum: d1fd63103009bea4dcc312059e33bac0 (MD5) Previous issue date: 2021-04-27","abstract_has_math":false,"creators":["Mao, Shufan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Willits, Jon Anthony"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:11:18Z","date_published":"2021-09-17T01:11:18Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Semantic memory","distributional models","spreading activation"],"languages":["en"],"rights":["Copyright 2021 Shufan Mao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110573","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Willits, Jon Anthony"]},{"key":"dc:creator","label":"Author","values":["Mao, Shufan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:11:18Z","2021-04-27","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Semantic memory","distributional models","spreading activation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Shufan Mao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110573"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Made available in DSpace on 2021-09-17T01:11:18Z (GMT). No. of bitstreams: 2 MAO-THESIS-2021.pdf: 1183634 bytes, checksum: 9e51e6a8017947611a5d3a89fd1b9ae9 (MD5) LICENSE.txt: 4207 bytes, checksum: d1fd63103009bea4dcc312059e33bac0 (MD5) Previous issue date: 2021-04-27","Distributional semantic models represent words in a vector space and are competent in various semantic tasks. They are also limited in certain aspects such as representing different types of relations (e.g. syntagmatic vs. paradigmatic) in the same space and form indirect semantic relations, resulting in difficulty of advanced tasks such as inference meaning of unseen phrases and analogy. In this article, we propose a hybrid semantic model encoding distributional data (word co-occurrence) with graphical structure and measure lexical semantic relatedness by a spreading activation algorithm, which addresses the issue of spatial models. We systematically investigated the modeling parameters contributing to the representational capability, by manipulating and controlling the hyperparameters, and testing the models on a selectional preference task. The models are trained on an artificial corpus generated to describe ordered events happened in a toy world simulation, which embeds verb-noun selectional preference, and the task require the models to recover the verb-noun co-occurrence information and making inference on the selectional preference of verb-noun pairs absent in the corpus. We showed that both the graphical data structure with the spreading activation measure and the co-occurrence (information) encoding type attributing to the better performance and the capability to encode both syntagmatic and paradigmatic relations to form indirect semantic relationship and infer on unseen word pairs. As the hybrid graphical model is trained on corpus data, it is a semantic network from linguistic distributional statistics, and a new way of learning and representing semantic knowledge.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Shufan Mao, accepted the attached license on 2021-04-26 at 10:04.","The student, Shufan Mao, submitted this Thesis for approval on 2021-04-26 at 10:13.","This Thesis was approved for publication on 2021-04-27 at 10:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16550 on 2021-09-16 at 16:47:39"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Spatial vs. graphical representation of distributional semantic knowledge"]}]}],"canonical_facts":{"dc:contributor":["Willits, Jon Anthony"],"dc:creator":["Mao, Shufan"],"dc:date":["2021-09-17T01:11:18Z","2021-04-27","2021-05"],"dc:description":["Made available in DSpace on 2021-09-17T01:11:18Z (GMT). No. of bitstreams: 2 MAO-THESIS-2021.pdf: 1183634 bytes, checksum: 9e51e6a8017947611a5d3a89fd1b9ae9 (MD5) LICENSE.txt: 4207 bytes, checksum: d1fd63103009bea4dcc312059e33bac0 (MD5) Previous issue date: 2021-04-27","Distributional semantic models represent words in a vector space and are competent in various semantic tasks. They are also limited in certain aspects such as representing different types of relations (e.g. syntagmatic vs. paradigmatic) in the same space and form indirect semantic relations, resulting in difficulty of advanced tasks such as inference meaning of unseen phrases and analogy. In this article, we propose a hybrid semantic model encoding distributional data (word co-occurrence) with graphical structure and measure lexical semantic relatedness by a spreading activation algorithm, which addresses the issue of spatial models. We systematically investigated the modeling parameters contributing to the representational capability, by manipulating and controlling the hyperparameters, and testing the models on a selectional preference task. The models are trained on an artificial corpus generated to describe ordered events happened in a toy world simulation, which embeds verb-noun selectional preference, and the task require the models to recover the verb-noun co-occurrence information and making inference on the selectional preference of verb-noun pairs absent in the corpus. We showed that both the graphical data structure with the spreading activation measure and the co-occurrence (information) encoding type attributing to the better performance and the capability to encode both syntagmatic and paradigmatic relations to form indirect semantic relationship and infer on unseen word pairs. As the hybrid graphical model is trained on corpus data, it is a semantic network from linguistic distributional statistics, and a new way of learning and representing semantic knowledge.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Shufan Mao, accepted the attached license on 2021-04-26 at 10:04.","The student, Shufan Mao, submitted this Thesis for approval on 2021-04-26 at 10:13.","This Thesis was approved for publication on 2021-04-27 at 10:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16550 on 2021-09-16 at 16:47:39"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110573"],"dc:language":["en"],"dc:rights":["Copyright 2021 Shufan Mao"],"dc:subject":["Semantic memory","distributional models","spreading activation"],"dc:title":["Spatial vs. graphical representation of distributional semantic knowledge"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}