標(biāo)題: Titlebook: Automated Grammatical Error Detection for Language Learners; Claudia Leacock,Martin Chodorow,Joel Tetreault Book 2010 Springer Nature Swit [打印本頁] 作者: 對將來事件 時(shí)間: 2025-3-21 20:08
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書目名稱Automated Grammatical Error Detection for Language Learners讀者反饋學(xué)科排名
作者: Foreknowledge 時(shí)間: 2025-3-21 23:26
Visualizing Astrocytes of the Optic Nerve,ommas and semicolons, to confuse homophones, and to write run-on sentences. But language learners also make errors that occur comparatively infrequently in writing by native speakers. This is especially true for errors where usage is guided by complex rules that interact with one another, or where n作者: 戲服 時(shí)間: 2025-3-22 02:22
https://doi.org/10.1007/3-540-34773-9ived in electronic form. Currently, however, a number of corpora are being developed, some of considerable size, that contain rich error annotations. These corpora can be used for a number of purposes such as error analysis and studies of the influence of L1 on learner errors. And, of course, these 作者: JAMB 時(shí)間: 2025-3-22 05:15
Paul J. Foster MD,Ravi Thomas MDher NLP areas, grammatical error detection does not have a shared task/repository to establish agreed upon standards for evaluation (and annotation). For example, SensEval provides researchers with annotated corpora for word sense disambiguation, where any researcher can download training and test d作者: 煩躁的女人 時(shí)間: 2025-3-22 12:08
Paul L. Kaufman MD, PhD,B’Ann True Gabeltese two error types. First, as we have seen in Chapter 3, prepositions and articles are notoriously difficult for English learners to master, so they represent a large subset of learner errors. Second, there has been a growing body of literature on the prediction of articles and prepositions in text作者: 削減 時(shí)間: 2025-3-22 16:21
Paul L. Kaufman MD, PhD,B’Ann True Gabeltd meaning, words also show tendencies or preferences in the ways they combine. As we noted in Chapter 3, native speakers of English know that the noun phrase . is preferred over ., even though both are grammatically and semantically well formed. Examples of such arbitrary, conventional usage appear 作者: ELUDE 時(shí)間: 2025-3-22 20:48
Update on Tube-Shunt Procedures for Glaucomalocations. While those three dominate current NLP work in grammatical error detection, there are interesting approaches for other error types as well. In this chapter, we review some of the work on different kinds of errors that language learners make. Specifically, we look at approaches for disting作者: 使害怕 時(shí)間: 2025-3-22 23:26 作者: genesis 時(shí)間: 2025-3-23 04:31
https://doi.org/10.1007/978-3-642-18633-2archers have used to enhance system performance by relying on very large corpora, such as the World Wide Web, by using machine translation technology, and by developing models of common learner errors. The second section discusses one important aspect of error detection that has not been addressed b作者: 胖人手藝好 時(shí)間: 2025-3-23 06:38
,What’s New in Laser Therapy for Glaucoma,e introduction, we noted that this an exciting area of research because there is the potential for real impact given the vast number of people learning another language and because there is no shortage of interesting problems that need to be solved.作者: BOLT 時(shí)間: 2025-3-23 13:31 作者: inflame 時(shí)間: 2025-3-23 14:21
,What’s New in Laser Therapy for Glaucoma,e introduction, we noted that this an exciting area of research because there is the potential for real impact given the vast number of people learning another language and because there is no shortage of interesting problems that need to be solved.作者: 溝通 時(shí)間: 2025-3-23 19:56
Springer Nature Switzerland AG 2010作者: agnostic 時(shí)間: 2025-3-23 23:02 作者: maculated 時(shí)間: 2025-3-24 02:40
https://doi.org/10.1007/3-540-34773-90) used a corpus of . error-annotated sentences to train a classifier for detection and correction of preposition errors. Gamon (2010) used a fully annotated error corpus to train a meta-classifier which combines evidence from other models that are trained on well-formed data. We will discuss this research in detail in Chapter 6.作者: abstemious 時(shí)間: 2025-3-24 09:16 作者: Mosaic 時(shí)間: 2025-3-24 14:23
Language Learner Data,0) used a corpus of . error-annotated sentences to train a classifier for detection and correction of preposition errors. Gamon (2010) used a fully annotated error corpus to train a meta-classifier which combines evidence from other models that are trained on well-formed data. We will discuss this research in detail in Chapter 6.作者: 貨物 時(shí)間: 2025-3-24 16:09 作者: 天空 時(shí)間: 2025-3-24 22:18
Visualizing Astrocytes of the Optic Nerve,o rules exist, or where they are influenced by the grammar of their native language. We describe, in detail, what a language learner (human or machine) must master in order to use articles, prepositions and collocations. We believe this is necessary background for any NLP research in detecting these errors.作者: DEFT 時(shí)間: 2025-3-25 02:22 作者: ALLEY 時(shí)間: 2025-3-25 06:40 作者: 諂媚于性 時(shí)間: 2025-3-25 09:39
Evaluating Error Detection Systems,ata and submit results for evaluation. Researchers working on grammatical error detection find themselves at the other end of the spectrum. Probably no two researchers use the same training and test data. Worse, it is not unusual for researchers to train and test on proprietary or licensed corpora that cannot be shared.作者: 共同給與 時(shí)間: 2025-3-25 13:12 作者: 名詞 時(shí)間: 2025-3-25 18:50
Update on Tube-Shunt Procedures for Glaucoma In this chapter, we review some of the work on different kinds of errors that language learners make. Specifically, we look at approaches for distinguishing grammatical and ungrammatical sentences, uses of heuristic rules, statistical approaches to verb-form errors, and methods for detecting and correcting homophone spelling errors.作者: figure 時(shí)間: 2025-3-26 00:02
https://doi.org/10.1007/978-3-642-18633-2 and by developing models of common learner errors. The second section discusses one important aspect of error detection that has not been addressed by NLP research: the efficacy of automated systems for improving the writing of actual users.作者: 善于 時(shí)間: 2025-3-26 00:26
Different Approaches for Different Errors, In this chapter, we review some of the work on different kinds of errors that language learners make. Specifically, we look at approaches for distinguishing grammatical and ungrammatical sentences, uses of heuristic rules, statistical approaches to verb-form errors, and methods for detecting and correcting homophone spelling errors.作者: hysterectomy 時(shí)間: 2025-3-26 07:27
New Directions, and by developing models of common learner errors. The second section discusses one important aspect of error detection that has not been addressed by NLP research: the efficacy of automated systems for improving the writing of actual users.作者: 使增至最大 時(shí)間: 2025-3-26 12:28 作者: 性別 時(shí)間: 2025-3-26 15:33 作者: cuticle 時(shí)間: 2025-3-26 18:26
Book 2010or English but for other languages as well. These language learners provide a burgeoning market for tools that help identify and correct learners‘ writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are ha作者: Confirm 時(shí)間: 2025-3-26 21:09
Special Problems of Language Learners,ommas and semicolons, to confuse homophones, and to write run-on sentences. But language learners also make errors that occur comparatively infrequently in writing by native speakers. This is especially true for errors where usage is guided by complex rules that interact with one another, or where n作者: 骯臟 時(shí)間: 2025-3-27 04:52
Language Learner Data,ived in electronic form. Currently, however, a number of corpora are being developed, some of considerable size, that contain rich error annotations. These corpora can be used for a number of purposes such as error analysis and studies of the influence of L1 on learner errors. And, of course, these 作者: 旅行路線 時(shí)間: 2025-3-27 07:48
Evaluating Error Detection Systems,her NLP areas, grammatical error detection does not have a shared task/repository to establish agreed upon standards for evaluation (and annotation). For example, SensEval provides researchers with annotated corpora for word sense disambiguation, where any researcher can download training and test d作者: GIDDY 時(shí)間: 2025-3-27 12:15 作者: hyperuricemia 時(shí)間: 2025-3-27 13:59 作者: Nonconformist 時(shí)間: 2025-3-27 18:57 作者: Aspirin 時(shí)間: 2025-3-28 01:01 作者: 鳥籠 時(shí)間: 2025-3-28 04:25 作者: 打折 時(shí)間: 2025-3-28 10:04
Conclusion,e introduction, we noted that this an exciting area of research because there is the potential for real impact given the vast number of people learning another language and because there is no shortage of interesting problems that need to be solved.作者: jovial 時(shí)間: 2025-3-28 13:59
Paul L. Kaufman MD, PhD,B’Ann True Gabeltoach, the context of a word’s usage is seen as crucially important in determining the word’s meaning. This view underlies Firth’s now-famous dictum that “you shall know a word by the company it keeps”.作者: 切碎 時(shí)間: 2025-3-28 18:16
Book 2010ion because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for res作者: 含糊其辭 時(shí)間: 2025-3-28 20:05
1947-4040 ctices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for res978-3-031-02137-4Series ISSN 1947-4040 Series E-ISSN 1947-4059 作者: 愛社交 時(shí)間: 2025-3-28 23:12 作者: 楓樹 時(shí)間: 2025-3-29 06:04 作者: EXTOL 時(shí)間: 2025-3-29 09:37
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