This article examines the status of constructed controlled terminologies from the perspective of the coverage of terms/concepts. To facilitate controlled authoring of Japanese texts of the municipal domain and promote machine translatability into English, we constructed terminologies in the following way: (1) Japanese-English term pairs are extracted from aligned texts; (2) term variations are controlled by defining preferred and proscribed terms for both languages. To assess the coverage of the constructed terminologies, we propose a quantitative extrapolation method that estimates the potential vocabulary size. The coverage estimations show that the coverage of terms for Japanese is higher than that for English by about 10%, which reflects the greater diversity of the translated English terms. The coverage of concepts reaches around 60% for both Japanese and English. The method also enables us to quantitatively estimate how much effort is needed to further increase the coverage.
{"title":"Building controlled bilingual terminologies for the municipal domain and evaluating them using a coverage estimation\u0000 approach","authors":"Rei Miyata, K. Kageura","doi":"10.1075/TERM.00017.MIY","DOIUrl":"https://doi.org/10.1075/TERM.00017.MIY","url":null,"abstract":"\u0000 This article examines the status of constructed controlled terminologies from the perspective of the coverage of terms/concepts. To\u0000 facilitate controlled authoring of Japanese texts of the municipal domain and promote machine translatability into English, we\u0000 constructed terminologies in the following way: (1) Japanese-English term pairs are extracted from aligned texts; (2) term\u0000 variations are controlled by defining preferred and proscribed terms for both languages. To assess the coverage of the constructed\u0000 terminologies, we propose a quantitative extrapolation method that estimates the potential vocabulary size. The coverage\u0000 estimations show that the coverage of terms for Japanese is higher than that for English by about 10%, which\u0000 reflects the greater diversity of the translated English terms. The coverage of concepts reaches around 60% for\u0000 both Japanese and English. The method also enables us to quantitatively estimate how much effort is needed to further increase the\u0000 coverage.","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46955730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article reviews The Future of Translation Technology – Towards a world without Babel 978-1-138-84204-5
这篇文章回顾了翻译技术的未来——走向一个没有巴别塔的世界978-1-138-84204-5
{"title":"Chan Sin-wai. 2017. The Future of Translation Technology – Towards a world without\u0000 Babel","authors":"Haoda Feng","doi":"10.1075/TERM.00023.FEN","DOIUrl":"https://doi.org/10.1075/TERM.00023.FEN","url":null,"abstract":"This article reviews The Future of Translation Technology – Towards a world without Babel 978-1-138-84204-5","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42982161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Humbley, John et Danielle Candel (dir.). 2017. Neologica. Revue internationale de néologie,\u0000 no 11 – La néologie en terminologie","authors":"L. Bowker","doi":"10.1075/TERM.00024.BOW","DOIUrl":"https://doi.org/10.1075/TERM.00024.BOW","url":null,"abstract":"This article reviews Neologica. Revue internationale de neologie, no 11 — La neologie en terminologie 978-2-406-06994-2","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46663577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite going through some ups and downs, grammar teaching has always been one of the central issues in the history of second language teaching. In order to teach grammar, teachers frequently get involved in metalanguage, which has grammatical terminology as one of its major components. Since the nature and use of grammatical terminology in language teaching has remained a considerably under-researched area to the day, the present study, originally a doctoral dissertation, was an attempt to find a difficulty index for a more or less comprehensive list of English grammatical terms, collected from various sources of English grammar. For this purpose, frequency of terms in a researcher-built corpus of EFL/ESL pedagogic grammar textbooks and English students’ familiarity with the terms were used as the two main criteria for calculating the difficulty index. A corpus of 14 grammatical textbooks was created, and then each of the 459 terms in the list was searched for in the textbooks to calculate their frequencies as well as ranks in the corpus. Student familiarity with the terms in the list was also measured through a productive test of grammatical terminology administered to 72 BA students of English at Shahid Beheshti University in Iran. Based on the results, the traditional dichotomy of scientific versus pedagogic terminology was questioned, arguing for an additional category, non-pedagogic term. Accordingly, 173 (37.7%) of the terms in the list never appeared in the corpus and thus were labelled non-pedagogic. Terms with a large corpus/test rank were reanalyzed to find out about the reasons for the gap. Furthermore, the distribution of terms across the corpus textbooks revealed that as the level of the books rises, the number of terms also increases, indicating the direct relationship between second language proficiency and metalingual knowledge. Most importantly, more than 10 major and minor trends in the use of grammatical terminology in pedagogy were explored and suggested. Finally, as the output of the study, 6 equivalent objective tests of pedagogic grammatical terminology were developed for the first time in the literature.
{"title":"Towards finding a difficulty index for English grammatical terminology","authors":"Mehrdad Yousefpoori-Naeim, S. Baleghizadeh","doi":"10.1075/TERM.00020.YOU","DOIUrl":"https://doi.org/10.1075/TERM.00020.YOU","url":null,"abstract":"\u0000 Despite going through some ups and downs, grammar teaching has always been one of the central issues in the history of second\u0000 language teaching. In order to teach grammar, teachers frequently get involved in metalanguage, which has grammatical terminology\u0000 as one of its major components. Since the nature and use of grammatical terminology in language teaching has remained a\u0000 considerably under-researched area to the day, the present study, originally a doctoral dissertation, was an attempt to find a\u0000 difficulty index for a more or less comprehensive list of English grammatical terms, collected from various sources of English\u0000 grammar. For this purpose, frequency of terms in a researcher-built corpus of EFL/ESL pedagogic grammar textbooks and English\u0000 students’ familiarity with the terms were used as the two main criteria for calculating the difficulty index. A corpus of 14\u0000 grammatical textbooks was created, and then each of the 459 terms in the list was searched for in the textbooks to calculate their\u0000 frequencies as well as ranks in the corpus. Student familiarity with the terms in the list was also measured through a productive\u0000 test of grammatical terminology administered to 72 BA students of English at Shahid Beheshti University in Iran. Based on the\u0000 results, the traditional dichotomy of scientific versus pedagogic terminology was questioned, arguing for an additional category,\u0000 non-pedagogic term. Accordingly, 173 (37.7%) of the terms in the list never appeared in the corpus and thus\u0000 were labelled non-pedagogic. Terms with a large corpus/test rank were reanalyzed to find out about the reasons for the gap.\u0000 Furthermore, the distribution of terms across the corpus textbooks revealed that as the level of the books rises, the number of\u0000 terms also increases, indicating the direct relationship between second language proficiency and metalingual knowledge. Most\u0000 importantly, more than 10 major and minor trends in the use of grammatical terminology in pedagogy were explored and suggested.\u0000 Finally, as the output of the study, 6 equivalent objective tests of pedagogic grammatical terminology were developed for the\u0000 first time in the literature.","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46472813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ławrynowicz, A. 2017. Semantic Data Mining, An Ontology-Based Approach,","authors":"Hui Liu, Fan Xu","doi":"10.1075/TERM.00022.LIU","DOIUrl":"https://doi.org/10.1075/TERM.00022.LIU","url":null,"abstract":"This article reviews Semantic Data Mining, An Ontology-Based Approach 978-1-61499-745-0978-1-61499-746-7","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44419506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper is a corpus-based study of certain key nouns used in promotional texts describing cheese in English. A corpus of online cheese descriptions was compiled to extract lexical information regarding the characterization of cheese. All in all, 37 key nouns were identified and classified into 7 semantic categories: appearance, part, colour, aroma, texture, taste and quality ranking. All the occurrences of each key noun were analysed to obtain an exhaustive inventory of the phraseological patterns in which they occur. The results show both objective characteristics (size, shape and colour) and subjective features which tend to be described positively in this promotional subgenre (aroma, texture and taste). Additionally, this text type includes key nouns related to the quality ranking of the cheeses promoted. The main aim of this paper is to gain insight into one particular subgenre – promotional cheese descriptions – through the major lexical co-occurrences used to convey positive evaluation of cheeses.
{"title":"Selling cheese online","authors":"Noelia Ramón, Belén Labrador","doi":"10.1075/TERM.00019.RAM","DOIUrl":"https://doi.org/10.1075/TERM.00019.RAM","url":null,"abstract":"\u0000 This paper is a corpus-based study of certain key nouns used in promotional texts describing cheese in English. A corpus of online\u0000 cheese descriptions was compiled to extract lexical information regarding the characterization of cheese. All in all, 37 key nouns\u0000 were identified and classified into 7 semantic categories: appearance, part, colour, aroma, texture, taste and quality ranking.\u0000 All the occurrences of each key noun were analysed to obtain an exhaustive inventory of the phraseological patterns in which they\u0000 occur. The results show both objective characteristics (size, shape and colour) and subjective features which tend to be described\u0000 positively in this promotional subgenre (aroma, texture and taste). Additionally, this text type includes key nouns related to the\u0000 quality ranking of the cheeses promoted. The main aim of this paper is to gain insight into one particular subgenre – promotional\u0000 cheese descriptions – through the major lexical co-occurrences used to convey positive evaluation of cheeses.","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46912798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Drouin, N. Grabar, Thierry Hamon, K. Kageura, Koichi Takeuchi
{"title":"Computational terminology and filtering of terminological information","authors":"P. Drouin, N. Grabar, Thierry Hamon, K. Kageura, Koichi Takeuchi","doi":"10.1075/TERM.00010.DRO","DOIUrl":"https://doi.org/10.1075/TERM.00010.DRO","url":null,"abstract":"","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1075/TERM.00010.DRO","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48947028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a word embedding dataset NWJC2Vec constructed using ‘NINJAL Web Japanese Corpus (NWJC)’. NWJC is a Web-crawled text corpus that contains 25.8 billion tokens. We construct two types of the word embedding dataset: one is based on the surface form, and the other is based on the complete morpheme information provided by UniDic, which is a lexicon for the Japanese morphological analyser MeCab. We perform an evaluation of the dataset by comparing it with the ‘Word List by Semantic Principles (Bunrui Goihyo)’.
{"title":"NWJC2Vec","authors":"Masayuki Asahara","doi":"10.1075/term.00011.asa","DOIUrl":"https://doi.org/10.1075/term.00011.asa","url":null,"abstract":"\u0000 In this paper, we present a word embedding dataset NWJC2Vec constructed using ‘NINJAL Web Japanese Corpus (NWJC)’. NWJC is a Web-crawled text corpus that contains 25.8 billion tokens. We construct two types of the word embedding dataset: one is based on the surface form, and the other is based on the complete morpheme information provided by UniDic, which is a lexicon for the Japanese morphological analyser MeCab. We perform an evaluation of the dataset by comparing it with the ‘Word List by Semantic Principles (Bunrui Goihyo)’.","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1075/term.00011.asa","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59107724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Narrative clinical records and biomedical articles constitute rich sources of information about phenotypes, i.e., markers distinguishing individuals with specific medical conditions from the general population. Phenotypes help clinicians to provide personalised treatments. However, locating information about them within huge document repositories is difficult, since each phenotypic concept can be mentioned in many ways. Normalisation methods automatically map divergent phrases to unique concepts in domain-specific terminologies, to allow location and linking of all mentions of a concept of interest. We have developed a hybrid normalisation method (HYPHEN) to handle concept mentions with wide ranging characteristics, across different text types. HYPHEN integrates various normalisation techniques that handle surface-level variations (e.g., differences in word order, word forms or acronyms/abbreviations) and lexical-level variations (where terms have similar meanings, but potentially unrelated forms). HYPHEN achieves robust performance for both biomedical academic text and narrative clinical records, and has the ability to significantly outperform related methods.
{"title":"HYPHEN","authors":"Paul Thompson, S. Ananiadou","doi":"10.1075/term.00015.tho","DOIUrl":"https://doi.org/10.1075/term.00015.tho","url":null,"abstract":"\u0000 Narrative clinical records and biomedical articles constitute rich sources of information about phenotypes, i.e., markers distinguishing individuals with specific medical conditions from the general population. Phenotypes help clinicians to provide personalised treatments. However, locating information about them within huge document repositories is difficult, since each phenotypic concept can be mentioned in many ways. Normalisation methods automatically map divergent phrases to unique concepts in domain-specific terminologies, to allow location and linking of all mentions of a concept of interest. We have developed a hybrid normalisation method (HYPHEN) to handle concept mentions with wide ranging characteristics, across different text types. HYPHEN integrates various normalisation techniques that handle surface-level variations (e.g., differences in word order, word forms or acronyms/abbreviations) and lexical-level variations (where terms have similar meanings, but potentially unrelated forms). HYPHEN achieves robust performance for both biomedical academic text and narrative clinical records, and has the ability to significantly outperform related methods.","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1075/term.00015.tho","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59107799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Chiyu and Libo, 2013. Traditional Chinese Theories of Translation:Terminology","authors":"Guo Huarui","doi":"10.1075/TERM.00007.GUO","DOIUrl":"https://doi.org/10.1075/TERM.00007.GUO","url":null,"abstract":"","PeriodicalId":44429,"journal":{"name":"Terminology","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1075/TERM.00007.GUO","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48992925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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