{"id":{"repo_id":"dcu","oai_identifier":"oai:doras.dcu.ie:15093"},"canonical_url":"https://search.dev.ndltd.org/etd/dcu/oai:doras.dcu.ie:15093","repository":{"repo_id":"dcu","name":"Dublin City University","base_url":"http://doras.dcu.ie/cgi/oai2"},"display":{"title":"-ing words in RBMT: multilingual evaluation and exploration of pre- and post-processing solutions","abstract":"This PhD dissertation falls within the domain of machine translation and it specifically focuses on the machine translation of IT-domain -ing words into four target languages: French, German, Japanese and Spanish. Claimed to be problematic due to their linguistic flexibility, i.e. -ing words can function as nouns, adjectives and verbs, this dissertation investigates how problematic -ing words are and explores possible solutions for improvement of their MT output. A corpus-based approach for a better representation of the domain-specific structures where -ing words occur is used. After selecting a significant sample, the -ing words are classified following a functional categorisation presented by Izquierdo (2006). The sample is machine-translated using a customised RBMT system. A feature-based human evaluation is then performed in order to obtain information about the specific feature under study. The results showed that 73% of the -ing words were correctly translated in terms of grammaticality and accuracy for German, Japanese and Spanish. The percentage for French was lower at 52%. These data, combined with a thorough analysis of the MT output, allows for the identification of cross-language and language-specific issues and their characteristics, setting the path for improvement. The approaches for improvements examined cover both the pre- and post-processing stages of automated translation. For pre-processing, controlled language (CL) and automatic source re-writing (ASR) are explored and evaluated. For post-processing, global search and replace (Global S&R) and statistical post-editing (SPE) methods are tested. CL is reported to reduce -ing word ambiguity but to not achieve substantial machine translation improvement. Regex-based implementations of ASR and Global S&R efforts show considerable translation improvements ranging from 60% to 95% and minimal degradation, ranging from 0% to 18%. The results yielded for SPE show little improvement, or even degradation at both sentence and -ing word level.","abstract_html":"This PhD dissertation falls within the domain of machine translation and it specifically focuses on the machine translation of IT-domain -ing words into four target languages: French, German, Japanese and Spanish. Claimed to be problematic due to their linguistic flexibility, i.e. -ing words can function as nouns, adjectives and verbs, this dissertation investigates how problematic -ing words are and explores possible solutions for improvement of their MT output. A corpus-based approach for a better representation of the domain-specific structures where -ing words occur is used. After selecting a significant sample, the -ing words are classified following a functional categorisation presented by Izquierdo (2006). The sample is machine-translated using a customised RBMT system. A feature-based human evaluation is then performed in order to obtain information about the specific feature under study. The results showed that 73% of the -ing words were correctly translated in terms of grammaticality and accuracy for German, Japanese and Spanish. The percentage for French was lower at 52%. These data, combined with a thorough analysis of the MT output, allows for the identification of cross-language and language-specific issues and their characteristics, setting the path for improvement. The approaches for improvements examined cover both the pre- and post-processing stages of automated translation. For pre-processing, controlled language (CL) and automatic source re-writing (ASR) are explored and evaluated. For post-processing, global search and replace (Global S&R) and statistical post-editing (SPE) methods are tested. CL is reported to reduce -ing word ambiguity but to not achieve substantial machine translation improvement. Regex-based implementations of ASR and Global S&R efforts show considerable translation improvements ranging from 60% to 95% and minimal degradation, ranging from 0% to 18%. The results yielded for SPE show little improvement, or even degradation at both sentence and -ing word level.","abstract_has_math":false,"creators":["Aranberri Monasterio, Nora"],"institution":"Dublin City University","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-03","date_published":"2010-03","updated_at":"2026-07-24T06:26:25Z","subjects":["Machine translating"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Enterprise Ireland","Symantec"]},{"key":"dc:creator","label":"Author","values":["Aranberri Monasterio, Nora"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2010-03"]},{"key":"dc:date.issued","label":"Date","values":["2010-03"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Dublin City University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://doras.dcu.ie/15093/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine translating"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doras.dcu.ie/15093/1/thesis_nora_aranberri_2009.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This PhD dissertation falls within the domain of machine translation and it specifically focuses on the machine translation of IT-domain -ing words into four target languages: French, German, Japanese and Spanish. Claimed to be problematic due to their linguistic flexibility, i.e. -ing words can function as nouns, adjectives and verbs, this dissertation investigates how problematic -ing words are and explores possible solutions for improvement of their MT output. A corpus-based approach for a better representation of the domain-specific structures where -ing words occur is used. After selecting a significant sample, the -ing words are classified following a functional categorisation presented by Izquierdo (2006). The sample is machine-translated using a customised RBMT system. A feature-based human evaluation is then performed in order to obtain information about the specific feature under study. The results showed that 73% of the -ing words were correctly translated in terms of grammaticality and accuracy for German, Japanese and Spanish. The percentage for French was lower at 52%. These data, combined with a thorough analysis of the MT output, allows for the identification of cross-language and language-specific issues and their characteristics, setting the path for improvement. The approaches for improvements examined cover both the pre- and post-processing stages of automated translation. For pre-processing, controlled language (CL) and automatic source re-writing (ASR) are explored and evaluated. For post-processing, global search and replace (Global S&R) and statistical post-editing (SPE) methods are tested. CL is reported to reduce -ing word ambiguity but to not achieve substantial machine translation improvement. Regex-based implementations of ASR and Global S&R efforts show considerable translation improvements ranging from 60% to 95% and minimal degradation, ranging from 0% to 18%. The results yielded for SPE show little improvement, or even degradation at both sentence and -ing word level."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["-ing words in RBMT: multilingual evaluation and exploration of pre- and post-processing solutions"]}]}],"canonical_facts":{"dc:contributor.sponsor":["Enterprise Ireland","Symantec"],"dc:creator":["Aranberri Monasterio, Nora"],"dc:date":["2010-03"],"dc:date.issued":["2010-03"],"dc:description.abstract":["This PhD dissertation falls within the domain of machine translation and it specifically focuses on the machine translation of IT-domain -ing words into four target languages: French, German, Japanese and Spanish. Claimed to be problematic due to their linguistic flexibility, i.e. -ing words can function as nouns, adjectives and verbs, this dissertation investigates how problematic -ing words are and explores possible solutions for improvement of their MT output. A corpus-based approach for a better representation of the domain-specific structures where -ing words occur is used. After selecting a significant sample, the -ing words are classified following a functional categorisation presented by Izquierdo (2006). The sample is machine-translated using a customised RBMT system. A feature-based human evaluation is then performed in order to obtain information about the specific feature under study. The results showed that 73% of the -ing words were correctly translated in terms of grammaticality and accuracy for German, Japanese and Spanish. The percentage for French was lower at 52%. These data, combined with a thorough analysis of the MT output, allows for the identification of cross-language and language-specific issues and their characteristics, setting the path for improvement. The approaches for improvements examined cover both the pre- and post-processing stages of automated translation. For pre-processing, controlled language (CL) and automatic source re-writing (ASR) are explored and evaluated. For post-processing, global search and replace (Global S&R) and statistical post-editing (SPE) methods are tested. CL is reported to reduce -ing word ambiguity but to not achieve substantial machine translation improvement. Regex-based implementations of ASR and Global S&R efforts show considerable translation improvements ranging from 60% to 95% and minimal degradation, ranging from 0% to 18%. The results yielded for SPE show little improvement, or even degradation at both sentence and -ing word level."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://doras.dcu.ie/15093/1/thesis_nora_aranberri_2009.pdf"],"dc:language":["en"],"dc:publisher.institution":["Dublin City University"],"dc:relation.isreferencedby":["https://doras.dcu.ie/15093/"],"dc:subject":["Machine translating"],"dc:title":["-ing words in RBMT: multilingual evaluation and exploration of pre- and post-processing solutions"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T06:26:25Z"}