The Cambridge Grammar of the English Language

Learning Vocabulary in Another Language: Appendixes

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Learning Vocabulary In Another Language

1. Word frequency and vocabulary size Paul Nation and Rob Waring 2. Variation in spoken and written language Michael McCarthy and Ron Carter 3. Vocabulary connections Rosamund Moon 4. Vocabulary and context William Nagy 5. Productive vs. receptive aspects of vocabulary Francine Melka 6. Models of lexical acquisition Paul Meara 7. Cognitive constraints on vocabulary acquisition Nick Ellis 8. Intralexical factors affecting the difficulty of vocabulary acquisition Batia Laufer 9. L1 influences on L2 vocabulary acquisition 10. Acquisition of the formal aspects of vocabulary Michael Swan 11. Vocabulary learning strategies Ann Ryan 12. Vocabulary and the syllabus Norbert Schmitt 13. Teaching vocabulary: most recent trends Felicity O'Dell 14. Vocabulary and testing John Read 15. Dictionaries, thesauruses, lexical resources Phil Schofield.

詞彙:描述、習得與教學=Vocabulary : description, acquisition and pedagogy

The CoNLL-2014 shared task was devoted to grammatical error correction of all error types. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results. Compared to the CoNLL2013 shared task, we have introduced the following changes in CoNLL-2014: (1) A participating system is expected to detect and correct grammatical errors of all types, instead of just the five error types in CoNLL-2013; (2) The evaluation metric was changed from F1 to F0.5, to emphasize precision over recall; and (3) We have two human annotators who independently annotated the test essays, compared to just one human annotator in CoNLL-2013.

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The CoNLL-2014 Shared Task on Grammatical Error Correction

One of the most difficult challenges faced by non-native speakers of English is mastering the system of English articles. We trained a maximum entropy classifier to select among a/an, the, or zero article for noun phrases (NPs), based on a set of features extracted from the local context of each. When the classifier was trained on 6 million NPs, its performance on published text was about 83% correct. We then used the classifier to detect article errors in the TOEFL essays of native speakers of Chinese, Japanese, and Russian. These writers made such errors in about one out of every eight NPs, or almost once in every three sentences. The classifier's agreement with human annotators was 85% (kappa = 0.48) when it selected among a/an, the, or zero article. Agreement was 89% (kappa = 0.56) when it made a binary (yes/no) decision about whether the NP should have an article. Even with these levels of overall agreement, precision and recall in error detection were only 0.52 and 0.80, respectively. However, when the classifier was allowed to skip cases where its confidence was low, precision rose to 0.90, with 0.40 recall. Additional improvements in performance may require features that reflect general knowledge to handle phenomena such as indirect prior reference. In August 2005, the classifier was deployed as a component of Educational Testing Service's Criterion$^{SM}$ Online Writing Evaluation Service.

/pdf/detecting-errors-in-english-article-usage-by-non-native-2w6v13nsqh.pdf

Detecting errors in English article usage by non-native speakers

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays written by ESL learners, relying on contextual and grammatical features surrounding preposition usage. First, we show that such a model can achieve high performance values: 93.3% precision and 14.8% recall for error detection and 81.7% precision and 13.2% recall for error detection and correction when tested on preposition replacement errors. Second, we show that this model outperforms models trained on well-edited text produced by native speakers of English. We discuss the implications of our approach in the area of language error modeling and the issues stemming from working with a noisy data set whose error annotations are not exhaustive.

/pdf/using-an-error-annotated-learner-corpus-to-develop-an-esl-1k2ikafet8.pdf

Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System.

One of the most difficult challenges faced by non-native speakers of English is mastering the system of English articles We trained a maximum entropy classifier to select among a/an, the, or zero article for noun phrases, based on a set of features extracted from the local context of each When the classifier was trained on 6 million noun phrases, its performance was correct about 88% of the time We also used the classifier to detect article errors in the TOEFL essays of native speakers of Chinese, Japanese, and Russian Agreement with human annotators was about 88% (kappa = 036) Many of the disagreements were due to the classifier s lack of discourse information Performance rose to 94% agreement (kappa = 047) when the system accepted noun phrases as correct in cases where its own confidence was low

/pdf/detecting-errors-in-english-article-usage-with-a-maximum-56ple4bh3j.pdf

Detecting Errors in English Article Usage with a Maximum Entropy Classifier Trained on a Large, Diverse Corpus.

Zero pronouns, or dropped arguments, are a remarkably frequent phenomenon in Korean This single syntactic form is made up of diverse subcategories, each of which is characterized by distinct semantic and pragmatic properties More widely acknowledged types are those that depend on other linguistic expressions for their reference: such text-dependent types include anaphoric and discourse-deictic zero pronouns Other text-independent types are deictic zero pronouns, generic and specific indefinite zero pronouns and situational zero pronouns, although it is possible for some of these text-independent types to enter coreferential relations with other nominal expressions in their surroundings Previous research focusing on anaphoric zero pronouns, most notably that based on Centering Theory, claims that information-theoretic notions such as saliency govern their felicitous use and interpretation While the general insight holds true, various efforts to encapsulate it by way of precise formulation of Cf-ranking or other hierarchies fall short, largely due to the fact that the notion is encoded by heterogeneous linguistic factors whose relations cannot be expressed single-dimensionally From a language processing point of view, the diverse nature of Korean zero pronouns presents the unique challenge of blending the tasks of categorization and identification of their antecedents In this dissertation, using Maximum Entropy as the machine learning method of choice, various statistical models for Korean zero pronoun resolution have been successfully trained and tested on two Korean Treebank corpora These Models serve as a valuable opportunity for empirically testing various theoretical claims and observations made on Korean zero pronoun anaphora Features used in constructing the models and making predictions on zero pronoun reference encode linguistic properties surrounding zero pronouns and their potential antecedents The features found to have a particularly strong contribution are indeed those that encode the linguistic aspects that are commonly cited in the linguistic literature as playing a crucial role in Korean zero pronoun usage, such as topic-hood, subject-hood and the nullness of form While the relative importance of such features does not directly translate to linguistic hierarchies, it nevertheless provides support to some of the specific criteria used in them

Korean zero pronouns: analysis and resolution

This paper presents three treebanks in Korean that consist of dependency trees derived from existing treebanks, the Google UD Treebank, the Penn Korean Treebank, and the KAIST Treebank, and pseudo-annotated by the latest guidelines from the Universal Dependencies (UD) project. The Korean portion of the Google UD Treebank is re-tokenized to match the morpheme-level annotation suggested by the other corpora, and systematically assessed for errors. Phrase structure trees in the Penn Korean Treebank and the KAIST Treebank are automatically converted into dependency trees using head-finding rules and linguistic heuristics. Additionally, part-of-speech tags in all treebanks are converted into the UD tagset. A total of 38K+ dependency trees are generated that comprise a coherent set of dependency relations for over a half million tokens. To the best of our knowledge, this is the first time that these Korean corpora are analyzed together and transformed into dependency trees following the latest UD guidelines, version 2.

Na-Rae Han

Papers

Detecting errors in English article usage by non-native speakers

Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System.

Detecting Errors in English Article Usage with a Maximum Entropy Classifier Trained on a Large, Diverse Corpus.

Korean zero pronouns: analysis and resolution

Building Universal Dependency Treebanks in Korean