Jesin James, Isabella Shields, Vithya Yogarajan, Peter J. Keegan, Catherine I. Watson, Peter-Lucas Jones, Keoni Mahelona
{"title":"开发标记的reo Māori-English语言技术双语数据库","authors":"Jesin James, Isabella Shields, Vithya Yogarajan, Peter J. Keegan, Catherine I. Watson, Peter-Lucas Jones, Keoni Mahelona","doi":"10.1007/s10579-023-09680-1","DOIUrl":null,"url":null,"abstract":"Te reo Māori (referred to as Māori), New Zealand’s indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori–English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori–English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned automatically using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"75 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The development of a labelled te reo Māori–English bilingual database for language technology\",\"authors\":\"Jesin James, Isabella Shields, Vithya Yogarajan, Peter J. Keegan, Catherine I. Watson, Peter-Lucas Jones, Keoni Mahelona\",\"doi\":\"10.1007/s10579-023-09680-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Te reo Māori (referred to as Māori), New Zealand’s indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori–English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori–English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned automatically using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. 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The development of a labelled te reo Māori–English bilingual database for language technology
Te reo Māori (referred to as Māori), New Zealand’s indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori–English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori–English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned automatically using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.
期刊介绍:
Language Resources and Evaluation is the first publication devoted to the acquisition, creation, annotation, and use of language resources, together with methods for evaluation of resources, technologies, and applications.
Language resources include language data and descriptions in machine readable form used to assist and augment language processing applications, such as written or spoken corpora and lexica, multimodal resources, grammars, terminology or domain specific databases and dictionaries, ontologies, multimedia databases, etc., as well as basic software tools for their acquisition, preparation, annotation, management, customization, and use.
Evaluation of language resources concerns assessing the state-of-the-art for a given technology, comparing different approaches to a given problem, assessing the availability of resources and technologies for a given application, benchmarking, and assessing system usability and user satisfaction.