{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80949"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80949","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An Intelligent Agent Based Spoken Dialog System for Content Based Image Retrieval","abstract":"Current spoken dialog systems are not robust and scalable. This is due to the lack of a systematic approach for studying natural language understanding problems. We hope to propose a method to build robust dialog systems by exploring the fundamental relationship between language and mind. We argue that an intelligent agent is necessary for building viable natural language understanding systems for complex applications. By utilizing language knowledge related to each individual word, we claim scalable and robust semantic analysis can be achieved under the framework of an intelligent agent. A novel knowledge representation structure designed for language---the Word Concept Model---is proposed to separate world knowledge and language knowledge so that an intelligent agent can have integrated information processing for multimodal interface. An extensible layered concept structure is proposed for storing concepts abstracted from the internal world of the intelligent agent in a relatively open structure to enable expansion of the concept space. The word concept model then stores information between the words and this concept space. Syntax information related to a specific word can also be stored in the word concept model. The semantic analysis algorithm processes sentences according to their sentence types to achieve uniform processing. We tested this idea by building a spoken dialog interface for a content-based image retrieval system. An evaluation of the system was conducted, and a relatively robust performance was achieved.","abstract_html":"Current spoken dialog systems are not robust and scalable. This is due to the lack of a systematic approach for studying natural language understanding problems. We hope to propose a method to build robust dialog systems by exploring the fundamental relationship between language and mind. We argue that an intelligent agent is necessary for building viable natural language understanding systems for complex applications. By utilizing language knowledge related to each individual word, we claim scalable and robust semantic analysis can be achieved under the framework of an intelligent agent. A novel knowledge representation structure designed for language---the Word Concept Model---is proposed to separate world knowledge and language knowledge so that an intelligent agent can have integrated information processing for multimodal interface. An extensible layered concept structure is proposed for storing concepts abstracted from the internal world of the intelligent agent in a relatively open structure to enable expansion of the concept space. The word concept model then stores information between the words and this concept space. Syntax information related to a specific word can also be stored in the word concept model. The semantic analysis algorithm processes sentences according to their sentence types to achieve uniform processing. We tested this idea by building a spoken dialog interface for a content-based image retrieval system. An evaluation of the system was conducted, and a relatively robust performance was achieved.","abstract_has_math":false,"creators":["Li, Yang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Levinson, Stephen E.","Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:08:58Z","date_published":"2015-09-25T20:08:58Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Artificial Intelligence"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3202128"],"render_values":[{"text":"(MiAaPQ)AAI3202128","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/80949","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Levinson, Stephen E.","Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Li, Yang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:08:58Z","10000-01-01","2005"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/80949","(MiAaPQ)AAI3202128"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Current spoken dialog systems are not robust and scalable. 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The word concept model then stores information between the words and this concept space. Syntax information related to a specific word can also be stored in the word concept model. The semantic analysis algorithm processes sentences according to their sentence types to achieve uniform processing. We tested this idea by building a spoken dialog interface for a content-based image retrieval system. An evaluation of the system was conducted, and a relatively robust performance was achieved.","Made available in DSpace on 2015-09-25T20:08:58Z (GMT). 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This is due to the lack of a systematic approach for studying natural language understanding problems. We hope to propose a method to build robust dialog systems by exploring the fundamental relationship between language and mind. We argue that an intelligent agent is necessary for building viable natural language understanding systems for complex applications. By utilizing language knowledge related to each individual word, we claim scalable and robust semantic analysis can be achieved under the framework of an intelligent agent. A novel knowledge representation structure designed for language---the Word Concept Model---is proposed to separate world knowledge and language knowledge so that an intelligent agent can have integrated information processing for multimodal interface. An extensible layered concept structure is proposed for storing concepts abstracted from the internal world of the intelligent agent in a relatively open structure to enable expansion of the concept space. The word concept model then stores information between the words and this concept space. Syntax information related to a specific word can also be stored in the word concept model. The semantic analysis algorithm processes sentences according to their sentence types to achieve uniform processing. We tested this idea by building a spoken dialog interface for a content-based image retrieval system. An evaluation of the system was conducted, and a relatively robust performance was achieved.","Made available in DSpace on 2015-09-25T20:08:58Z (GMT). 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