High school writing teacher feedback on word choice errors

This study scrutinizes the range and types of feedback given for word choice errors occurring in the English Taiwan Learner Corpus (ETLC), which contains Taiwanese high school students’ English writings and the corrective feedback provided by L2 writing teachers. All instances of word choice error tags (n = 1,439) were extracted from the ETLC for analyses. Results showed L2 writing teachers provided indirect feedback more often than direct feedback, requiring students to self-correct without guidance. Furthermore, many errors tagged as word choice were grammar errors, further questioning L2 writing teachers’ understanding of word choice errors and competence to correct such errors. This study highlights the importance of raising students’ awareness to targeted lexis prior to completing L2 writing tasks. We also argue that there are benefits for L2 writing teachers to provide focused and direct word choice error feedback after the completion of such tasks

Cross-Lingual Lexico-Semantic Transfer in Language Learning

Lexico-semantic knowledge of our native language provides an initial foundation for second language learning. In this paper, we investigate whether and to what extent the lexico-semantic models of the native language (L1) are transferred to the second language (L2). Specifically, we focus on the problem of lexical choice and investigate it in the context of three typologically diverse languages: Russian, Spanish and English. We show that a statistical semantic model learned from L1 data improves automatic error detection in L2 for the speakers of the respective L1. Finally, we investigate whether the semantic model learned from a particular L1 is portable to other, typologically related languages.Ekaterina Kochmar’s research is supported by Cambridge English Language Assessment via the ALTA Institute. Ekaterina Shutova’s research is supported by the Leverhulme Trust Early Career Fellowship

‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English

In this paper we discuss three key points related to error detection (ED) in learners’ English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective–noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system’s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.Ekaterina Kochmar’s research is supported by Cambridge English Language Assessment via the ALTA Institute. Aurélie Herbelot’s contribution to this paper was similarly supported by ALTA

Detecting learner errors in the choice of content words using compositional distributional semantics

We describe a novel approach to error detection in adjective-noun combinations. We present and release a new dataset of annotated errors where the examples are extracted from learner texts and annotated with error types. We show how compositional distributional semantic approaches can be applied to discriminate between correct and incorrect word combinations from learner data. Finally, we show how the output of the compositional distributional semantic models can be used as features in a classifier yielding good precision and accuracy.We are grateful to Cambridge English Language Assessment and Cambridge University Press for supporting this research and for granting us access to the CLC for research purposes

Cross-linguistic collocational networks in the L1 Turkish–L2 English mental lexicon

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAccording to the Collocational Priming Theory, every word is primed to co-occur with particular other related words and priming could be regarded as the source of our creative language system. Previous research has shown evidence of collocational priming in both L1 and L2 users of English and has indicated that L2 processing is influenced by L1 collocations. Thisstudy attemptsto further our understanding of the relationship between first and second language collocations through the paradigm of cross-linguistic priming. That is, it will test the extent to which individual wordsin one language prime recognition of those words' collocates in the other language. Results suggest a complex picture of both cross-linguistic priming and cross-linguistic inhibition, operating differently across different part of speech combinations. They also suggest important methodological influences which future research will need to investigate. Findings are discussed in the light of the current bilingual mental lexicon models and some implications are drawn based on the observed collocational networks in the L1 Turkish-L2 English bilingual mental lexicon

The Study of Teacher Written Feedback: The Effectiveness of Electronic Feedback on Student Writing Revisions

The effectiveness of teacher written feedback has been a subject of debate in second language writing for decades. The most basic debate in this area among ESL writing researchers is whether teacher written feedback in various forms has any positive effects on student writing revisions. Among other researchers, Ferris, Lee, Ene & Upton and Stevenson & Phakiti argued that while the effectiveness of error feedback in the traditional paper-and-pen form (Ferris, 1999, 2002, 2003, 2004, 2006 and Lee, 2008a, 2008b), computer-facilitated form (Ene & Upton, 2014) or computer-generated form (Stevenson & Phakiti, 2013) was not conclusive, more research should be done to explore in what ways error feedback can be improved. Indeed, the heterogeneity of these studies characterized by different focus, research designs, institutional and instructional contexts, and participant backgrounds, alongside some methodological flaws and misinterpretation of findings identified in my critical review has possibly undermined the validity and reliability of the studies, giving rise to these mixed results for both paper-and-pen feedback and computer-based feedback. As such, the causality between different forms of feedback treatment and their outcomes of error reduction is questioned. With the primary interest in improving the effectiveness of teacher written feedback in error correction, ‘Mark My Words’ (‘MMWs’), the interactive-based electronic feedback system, was designed in such a way to accommodate individual learners’ language needs and to be more responsive to various error types. This study focused on examining on the effectiveness of ‘Mark My Words’ (‘MMWs’), as a kind of computer-facilitated feedback (i.e. electronic feedback), in improving students’ error reduction in their writing revisions, under a controlled condition. The mixed methods approach was adopted, namely the ‘error count’ method and ‘questionnaire’, in this study. The participants were 62 second-year engineering students enrolled in an English for Specific Purposes course in a Hong Kong University. Efforts were made to avoid the impact of extraneous variables on the validity and reliability of the research outcomes under such controlled condition. The positive results of this study can contribute some sort of concrete evidence to the growing body of literature of the ‘effectiveness of teacher written feedback’ and ‘second language writing’, thus clarifying some mixed results of the previous research

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

L2 writers referencing corpora to address accuracy: a qualitative analysis of learners' lexicogrammatical error corrections

Since the advent of process writing, the role of language has been relegated to an arguably minor position in L2 writing pedagogy, despite L2 writers' ongoing linguistic needs. Corpus referencing has emerged as a promising approach to address these needs, though numerous challenges exist for both learners and teachers who struggle with the specialized skills necessary for corpus research. This classroom-based study addresses these issues by qualitatively examining the corpus-based error correction process in relation to three error types: preposition combinations, collocations, and phrases. Specifically, the study investigates (1) corpus referencing as an alternative to teacher direct correction; (2) the linguistic patterns that emerge through the correction process; and (3) factors that influenced the learners' ability to apply corpus data to their writing. Based on 965 error corrections, results indicate that learners were generally successful; however, the interpretative demands placed on them to address these errors played an important role in their degree of success. The findings further imply that learners tended to analyze the corpus data paradigmatically, searching for word substitutions rather than examining co-text, and that they had difficulties understanding the phraseology of the language in the corpus as well as the language of their own written production

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

The effects of lexical input on L2 writing: a corpus-informed approach.

Huang, Zeping.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (leaves 121-132).Abstracts in English and Chinese; appendix two in English and Chinese.Acknowledgements --- p.iAbstract --- p.iiiAbstract (Chinese) --- p.vTable Of Contents --- p.viList of Tables --- p.ixList of Figures and Graphs --- p.xChapter CHAPTER ONE --- INTRODUCTION --- p.1Chapter 1.1. --- Motivation --- p.1Chapter 1.1.2. --- The importance of language use in L2 writing --- p.1Chapter 1.1.2. --- The possibilities of integrating corpora into L2 writing instruction --- p.2Chapter 1.1.3. --- The need for corpus-informed approach --- p.2Chapter 1.2. --- Purpose of this study --- p.4Chapter 1.3. --- Research questions --- p.5Chapter 1.4. --- Overall research methods --- p.5Chapter 1.5. --- Significance of the study --- p.5Chapter 1.6. --- Organization of the thesis --- p.6Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.8Chapter 2.1. --- "Research on corpora and L2 writing...," --- p.8Chapter 2.1.1. --- Studies on corpus use from teachers' perspective --- p.9Chapter 2.1.2. --- Studies on students' direct use of corpus --- p.10Chapter 2.1.3. --- Empirical Studies on corpus and vocabulary learning --- p.17Chapter 2.2. --- Evaluations of the studies under review --- p.19Chapter 2.2.1. --- Evaluation of research on corpus-informed teaching materials development --- p.19Chapter 2.2.2. --- Evaluations of empirical research on students' direct use of corpus --- p.21Chapter 2.3. --- Call for further studies --- p.22Chapter CHAPTER THREE --- METHODOLOGY --- p.24Chapter 3.1. --- Participants --- p.24Chapter 3.2. --- Research Setting --- p.25Chapter 3.3. --- Materials --- p.25Chapter 3.3.1. --- Corpora used --- p.26Chapter 3.3.2. --- Selecting the target words --- p.28Chapter 3.3.3. --- Sifting the concordance lines --- p.29Chapter 3.3.4. --- Formulating the queries --- p.30Chapter 3.4. --- Research design --- p.32Chapter 3.4.1. --- Pretest --- p.33Chapter 3.4.2. --- Immediate posttest --- p.33Chapter 3.4.3. --- Delayed posttest --- p.34Chapter 3.5. --- Procedures --- p.35Chapter 3.6. --- Instruments --- p.37Chapter 3.6.1. --- Questionnaires --- p.37Chapter 3.6.2. --- Learning journals --- p.38Chapter 3.6.3. --- Uptake sheets --- p.38Chapter 3.7. --- Data collection and analysis --- p.38Chapter 3.7.1. --- Holistic scoring --- p.39Chapter 3.7.2. --- Analysis of the use of target words --- p.40Chapter 3.7.3. --- Questionnaire responses --- p.42Chapter 3.8. --- Chapter summary --- p.43Chapter CHAPTER FOUR --- RESULTS --- p.44Chapter 4.1. --- Holistic scores --- p.44Chapter 4.2. --- Use of signaling nouns (SNs) --- p.46Chapter 4.2.1. --- Accuracy --- p.46Chapter 4.2.2. --- Complexity --- p.61Chapter 4.2.3. --- Retention of the target patterns --- p.73Chapter 4.3. --- Content Schemata nouns --- p.74Chapter 4.4. --- Evaluation of the concordance exercises --- p.75Chapter 4.4.1. --- Effects on vocabulary learning --- p.75Chapter 4.4.2. --- Effect on L2 writing --- p.78Chapter 4.4.3. --- Difficulties in doing the concordance exercises --- p.80Chapter 4.5. --- Chapter summary --- p.84Chapter CHAPTER FIVE --- DISCUSSION --- p.85Chapter 5.1. --- Did the corpus-informed approach improve students' overall writing quality? --- p.85Chapter 5.1.1. --- Cut-off sentences --- p.87Chapter 5.1.2. --- Culture-loaded information in concordance lines --- p.88Chapter 5.2. --- Did the corpus-informed approach improve vocabulary use in students' writing? --- p.90Chapter 5.2.1. --- Interface of lexis and syntax --- p.91Chapter 5.2.2. --- Encouraging usage-based learning --- p.95Chapter 5.2.3. --- Raising learner awareness of collocation and colligation --- p.97Chapter 5.2.4. --- Retention of lexico-grammatical patterns --- p.98Chapter 5.3. --- Did students think that corpus-informed approach helped their writing? --- p.100Chapter 5.4. --- Towards a tentative model of corpus-informed writing instruction --- p.102Chapter 5.4.1. --- Preparing Materials --- p.104Chapter 5.4.2. --- During the exploration of a topic-specific corpus --- p.105Chapter 5.4.3. --- Follow-up activities after exploration of the topic-specific corpus --- p.106Chapter 5.5. --- Chapter summary --- p.108Chapter CHAPTER SIX --- CONCLUSION --- p.109Chapter 6.1. --- Summary of this study --- p.109Chapter 6.1.1. --- Enhancement of lexico-grammatical patterns --- p.109Chapter 6.1.2. --- Enhanced awareness of the importance of collocations --- p.111Chapter 6.1.3. --- Pivotal role of prior grammatical knowledge in corpus-informed learning --- p.111Chapter 6.1.4. --- Insignificant correlation between learning CSNs and ideas development --- p.113Chapter 6.2. --- Pedagogical implications --- p.113Chapter 6.2.1. --- Writing materials development --- p.114Chapter 6.2.2. --- Implementation of corpus-informed activities --- p.115Chapter 6.3. --- Limitations and suggestions --- p.117Chapter 6.3.1. --- A longer experimental time frame --- p.117Chapter 6.3.2. --- More lexical input --- p.118Chapter 6.3.3. --- More comparison groups --- p.118Chapter 6.3.4. --- Different proficiency levels --- p.119Chapter 6.3.5. --- Web-based concordances and more follow-up learning activities --- p.119Chapter 6.3.6. --- Case studies --- p.120Chapter 6.4. --- Closing remarks --- p.120Bibliography --- p.121Appendix One Questionnaire One --- p.133Appendix Two Questionnaire Two --- p.136Appendix Three Learning Journal --- p.139Appendix Four Pre-writing Vocabulary Study --- p.140Appendix Five Pretest Writing Task --- p.153Appendix Six Immediate Posttest Writing Task --- p.154Appendix Seven Delayed Posttest Writing Task --- p.15