{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/100621"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/100621","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Using a symbolic language parser to Improve Markov language models","abstract":"This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.","abstract_html":"This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model.","abstract_has_math":false,"creators":["Townsend, Duncan Clarke McIntire"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Boris Katz."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-22T22:21:24Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/100621","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Boris Katz."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Townsend, Duncan Clarke McIntire"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-01-04T19:59:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-01-04T19:59:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/100621"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 31-32)."]},{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Using a symbolic language parser to Improve Markov language models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Boris Katz."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Townsend, Duncan Clarke McIntire"],"dc:date.accessioned":["2016-01-04T19:59:30Z"],"dc:date.available":["2016-01-04T19:59:30Z"],"dc:date.issued":["2015"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 31-32)."],"dc:description.abstract":["This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplification. The n-gram system is comprised of a relational database backend with a frontend application that presents a homogeneous interface for both direct n-gram lookup and Markov approximation. The query language exposed by the frontend also applies lexical information from the START natural language system to allow queries based on part of speech. Using the START natural language system's parser, English sentences are transformed into a collection of structural, syntactic, and lexical statements that are uniquely well-suited to the process of simplification. After reducing the parse of the sentence, the resulting expressions can be processed back into English. These reduced sentences are ranked by likelihood by the n-gram model."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/100621"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Using a symbolic language parser to Improve Markov language models"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:24Z"}