Akira Murakami

University of Birmingham, English Language and Applied Linguistics, Faculty Member

Followers

197

Following

Co-author

Public Views

I am a Birmingham Fellow at the Department of English Language and Linguistics, University of Birmingham. My primary research interests are second language acquisition and corpus linguistics. I am particularly investigating the second language development of linguistic complexity and accuracy based on a large-scale learner corpus.
Address: Department of Theoretical and Applied Linguistics,
9 West Road, University of Cambridge, Cambridge,
CB3 9DP
United Kingdom

less

Michael Spivey

University of California, Merced

Michał B. Paradowski

University of Warsaw

Ahmar Mahboob

The University of Sydney

Gerard Steen

University of Amsterdam

T. Florian Jaeger

University of Rochester

Eitan Grossman

The Hebrew University of Jerusalem

Shadi Davari

Tehran University of Medical Sciences

Thomas Payne

SIL International

Alessio Palmisano

Università degli Studi di Torino

Michael Lessard-Clouston

Biola University

InterestsView All (7)

Uploads

Papers by Akira Murakami

Developing, Analyzing and Sharing Multivariate and Multifactorial Datasets for Open Science: Individual Differences in the Dynamic System of L2 Speech Learning Revisited

by Meltem Ilkan and Akira Murakami

Following the trends established in psychology and emerging in L2 research, we explain our suppor... more Following the trends established in psychology and emerging in L2 research, we explain our support for an Open Science approach in this paper (i.e., developing, analyzing and sharing datasets) as a way to answer controversial and complex questions in applied linguistics. We illustrate this with a focus on a frequently debated question, what underlies individual differences in the dynamic system of post-pubertal L2 speech learning? We provide a detailed description of our dataset which consists of spontaneous speech samples, elicited from 110 late L2 speakers in the UK with diverse linguistic, experiential and sociopsychological backgrounds, rated by ten L1 English listeners for comprehensibility and nativelikeness. We explain how we examined the source of individual differences by linking different levels of L2 speech performance to a range of learner-extrinsic and intrinsic variables related to first language backgrounds, age, experience, motivation, awareness, and attitudes using a series of factor and Bayesian mixed-effects ordinal regression analyses. We conclude with a range of suggestions for the applied linguistics and SLA, including the use of Bayesian methods in analyzing multivariate, multifactorial data of this kind, and advocating publicly available datasets. In keeping with recommendations for increasing openness of the field, we invite readers to rethink and redo our analyses and interpretations from multiple angles by making our dataset publicly available and coding as part of our 40 th anniversary ARAL article.

Download

Majewska, O., Vulić, I., McCarthy, D., Huang, Y., Murakami, A., Laippala, V., & Korhonen, A. (forthcoming). Investigating the cross-lingual translatability of VerbNet-style classification. Language Resources and Evaluation. doi: 10.1007/s10579-017-9403-x

VerbNet—the most extensive online verb lexicon currently available for English—has proved useful ... more VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.

Thompson, P., Hunston, S., Murakami, A., & Vajn, D. (2017). Multi-Dimensional Analysis, text constellations, and interdisciplinary discourse. International Journal of Corpus Linguistics, 22(2), 153–186. doi: 10.1075/ijcl.22.2.01tho

Multi-Dimensional Analysis (MDA) has been widely used to explore register variation. This paper r... more Multi-Dimensional Analysis (MDA) has been widely used to explore register variation. This paper reports on a project using MDA to explore the features of an interdisciplinary academic domain. Six dimensions of variation are identified in a corpus of 11,000 journal articles in environmental studies. We then focus on articles in one interdisciplinary journal, Global Environmental Change (GEC). It is expected that these articles will diverge sufficiently to produce differences that are analogous to register differences. Instead of identifying these “registers” on external criteria, we use the dimensional profiles of individual texts to identify ‘constellations’ of texts sharing combinations of features. Six such constellations are derived, consisting of texts with commonalities in their approaches to research: the development of predictive models; quantitative research; discussions of theory and policy; and human-environment studies focusing on individual voices. The identification of these constellations could not have been achieved through an a priori categorisation of texts.

Murakami, A., Hunston, S., Thompson, P., & Vajn, D. (2017). 'What is this corpus about?' Using topic modelling to explore a specialized corpus. Corpora, 12(2), 243–277. doi: 10.3366/cor.2017.0118

This paper introduces topic modelling, a machine learning technique that automatically identifies... more This paper introduces topic modelling, a machine learning technique that automatically identifies ‘topics’ in a given corpus. The paper illustrates its use in the exploration of a corpus of academic English. It first offers the intuitive explanation of the underlying mechanism of topic modelling and describes the procedure for building a model, including the decisions involved in the model-building process. The paper then explores the model. A topic in topic models is characterised by a set of co-occurring words, and we will demonstrate that such topics bring us rich insights into the nature of a corpus. As exemplary tasks, this paper identifies the prominent topics in different parts of papers, investigates the chronological change of a journal, and reveals different types of papers in the journal. The paper further compares topic modelling to two more traditional techniques in corpus linguistics, semantic annotation and keywords analysis, and highlights the strengths of topic modelling.We believe that topic modelling is particularly useful in the initial exploration of a corpus.

Alexopoulou, T., Michel, M., Murakami, A., & Meurers, D. (2017). Task effects on linguistic complexity and accuracy: A large-scale learner corpus analysis employing Natural Language Processing techniques. Language Learning, 67(S1), 180–208. doi: 10.1111/lang.12232

Large-scale learner corpora collected from online language learning platforms, such as the EF-Cam... more Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a task and its functional requirements influence task-based linguistic performance? This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.

Murakami, A., & Alexopoulou, T. (2016). Longitudinal L2 development of the English article in individual learners. In A. Papafragou, D. Grodner, J. Trueswell, & D. Mirman (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 1050–1055) . . .

We investigate the accuracy development of the English article by learners of English as a second... more We investigate the accuracy development of the English article by learners of English as a second language. The study focuses on individual learners, tracking their learning trajectories through their writings in the EF-Cambridge Open Language Database (EFCAMDAT), an open access learner corpus. We draw from 17,859 writings by 1,280 learners and ask whether article accuracy in individual learners fluctuates randomly or whether learners can be clustered according to their developmental trajectories. In particular, we apply kmeans clustering to automatically cluster in a bottom up fashion learners with similar learning curves. We follow learners for a period covering one CEFR level. Given the relatively short learning window, the majority of learners follow a horizontal line. Nevertheless, we also identify groups of learners showing a power-function and U-shaped curve. Crucially, these groups are ‘hidden’ when the aggregate of learners is considered, a finding highlighting the importance of individual level analysis.

Download

Murakami, A. (2016). Modeling systematicity and individuality in nonlinear second language development: The case of English grammatical morphemes. Language Learning, 66(4). 834–871. doi: 10.1111/lang.12166

This article introduces two sophisticated statistical modeling techniques that allow researchers ... more This article introduces two sophisticated statistical modeling techniques that allow researchers to analyze systematicity, individual variation, and nonlinearity in second language (L2) development. Generalized linear mixed-effects models can be used to quantify individual variation and examine systematic effects simultaneously, and generalized additive mixed models allow for the examination of systematicity, individuality, and nonlinearity within a single model. Based on a longitudinal learner corpus, this article illustrates the usefulness of these models in the context of L2 accuracy development of English grammatical morphemes. I discuss the strengths of each technique and the ways in which these techniques can benefit L2 acquisition research, further highlighting the importance of accounting for individual variation in modeling L2 development.

Download

Murakami, A., & Alexopoulou, T. (2016). L1 influence on the acquisition order of English grammatical morphemes: A learner corpus study. Studies in Second Language Acquisition, 38(3), 365–401. doi: 10.1017/S0272263115000352 [Recipient of the 2016 Albert Valdman Award]

We revisit morpheme studies to evaluate the long-standing claim for a universal order of acquisit... more We revisit morpheme studies to evaluate the long-standing claim for a universal order of acquisition. We investigate the L2 acquisition order of six English grammatical morphemes by learners from seven L1 groups across five proficiency levels. Data are drawn from approximately 10,000 written exam scripts from the Cambridge Learner Corpus. The study establishes clear L1 influence on the absolute accuracy of morphemes and their acquisition order, therefore challenging the widely held view that there is a universal order of acquisition of L2 morphemes. Moreover, we find that L1 influence is morpheme specific, with morphemes encoding language-specific concepts most vulnerable to L1 influence.

Download

Salamoura, A., Littlemore, J., Murakami, A., & Jaworska, S. (2015). In. J. Harrison & F. Baker (Eds.), English Profile in practice (pp. 106-119). Cambridge: Cambridge University Press.

Murakami, A. (2013). Cross-linguistic influence on the accuracy order of L2 English grammatical morphemes. In S. Granger, S. Gaëtanelle, & F. Meunier (Eds.), Twenty years of learner corpus research. Looking back, moving ahead: Corpora and language in use - Proceedings 1 (pp. 325–334).

In S. Granger, S. Gaëtanelle & F. Meunier (Eds.), Twenty years of learner corpus research: Looking back, moving ahead: Corpora and language in use - Proceedings 1 (pp. 325-334). Louvain-la-Neuve: Presses universitaires de Louvain.

Abstract: Contrary to the accepted notion of the ‘natural order’ that claims for the fixed L2 acq... more Abstract: Contrary to the accepted notion of the ‘natural order’ that claims for the fixed L2 acquisition order of English grammatical morphemes, Luk and Shirai (2009) reviewed the literature and argued that the order may differ depending on learners’ L1. The present study empirically investigates whether the accuracy order of L2 English grammatical morphemes varies across L1 groups. By targeting over 3,000 essays across seven L1 groups in the Cambridge Learner Corpus, the study computed the accuracy of six morphemes in each L1 group and clustered them through statistical bootstrapping. The study, then, compared the accuracy order of the morphemes between L1 groups and demonstrated clear L1 influence. Overall, the groups whose L1s do not obligatorily mark the morpheme tend to have a lower accuracy order with respect to the morpheme compared to those whose L1s mark it. This was particularly the case for articles.

Download

基準特性の抽出 [Extraction of criterial features]

In 投野由紀夫 [Y. Tono]、金子朝子 [T. Kaneko]、杉浦正利 [M. Sugiura]、和泉絵美 [E. Izumi] （編 [Eds.]）英語学習者コーパス活用ハンドブック [A handbook of learner corpus research]. 東京 [Tokyo]: 大修館書店 [Taishukanshoten], 2013

Murakami, A. (2009). A corpus-based study of English textbooks in Japan and Asian countries: Multidimensional approach. Proceedings of the International Conference on Multi-Development and Application of Languages and Linguistics [CD-ROM].

This paper aims to identify textual characteristics and differences between English textbooks in ... more This paper aims to identify textual characteristics and differences between English textbooks in Japan and other major Asian countries (China, Korea, and Taiwan). To this end, the author first reviewed the related literature and pointed out that the English textbook research so far has dealt with a very limited scope in terms of the coverage of English textbooks as a whole. Very little has been done on the analysis of English textbooks from genre/text type perspectives. In order to fill the gap, the present study employed Biber’s multidimensional (MD) analysis in the comparison of English textbooks in Japan, China, Korea, and Taiwan. Three dimensions showing different textual characteristics were extracted and functional interpretations were given. The four countries were then compared along these dimensions. The experiment shows that there are previously unrevealed abstract differences among English textbooks in different countries.

Download

Developing, Analyzing and Sharing Multivariate and Multifactorial Datasets for Open Science: Individual Differences in the Dynamic System of L2 Speech Learning Revisited

by Meltem Ilkan and Akira Murakami

Download

Murakami, A., Hunston, S., Thompson, P., & Vajn, D. (2017). 'What is this corpus about?' Using topic modelling to explore a specialized corpus. Corpora, 12(2), 243–277. doi: 10.3366/cor.2017.0118

Download

Salamoura, A., Littlemore, J., Murakami, A., & Jaworska, S. (2015). In. J. Harrison & F. Baker (Eds.), English Profile in practice (pp. 106-119). Cambridge: Cambridge University Press.

Download

基準特性の抽出 [Extraction of criterial features]

Download

Akira Murakami

Uploads

Papers by Akira Murakami

Log In