Native language identification (NLID) for forensic authorship analysis of weblogs

This chapter introduces Native Language Identification (NLID) and considers the casework applications with regard to authorship analysis of online material. It presents findings from research identifying which linguistic features were the best indicators of native (L1) Persian speakers blogging in English, and analyses how these features cope at distinguishing between native influences from languages that are linguistically and culturally related. The first chapter section outlines the area of Native Language Identification, and demonstrates its potential for application through a discussion of relevant case history. The next section discusses a development of methodology for identifying influence from L1 Persian in an anonymous blog author, and presents findings. The third part discusses the application of these features to casework situations as well as how the features identified can form an easily applicable model and demonstrates the application of this to casework. The research presented in this chapter can be considered a case study for the wider potential application of NLID.

Teaching English to young learners in Europe:teachers' attitudes and perspectives

The introduction of languages into the primary curriculum has been the major development in language-in-education policy around the world over the last 20-25 years. In the vast majority of countries the language taught is English and it is being taught at an ever-earlier age. A relatively large amount of research has been carried out in Asia into teaching English to young learners (TEYL) from the point of view of language policy and planning and of policy implementation, especially in terms of the gap between policy and practice caused by the introduction of new methodologies such as communicative language teaching. However, to date far less research has been carried out into the situation in Europe, particularly concerning the attitudes of those most closely involved in policy implementation - the teachers themselves. This chapter examines the attitudes of teachers in six European countries (Italy, Latvia, Macedonia, Poland, Spain and Ukraine), uncovering the challenges they face and the changes they would like to see enacted to improve English language learning and teaching in their countries. The implications for policy, planning and teacher education are also discussed.

West Midlands English:Birmingham and the Black Country

This chapter provides an overview, discussing firstly the boundaries of the West Midlands area today within which Birmingham and the Black Country are situated, taking account of how they have changed across time. It includes a section on the demographic make-up of the region across time, before moving on to consider issues relating to language, culture and identity in section 1.5 on the theoretical underpinnings of the research upon which much of this book is based, particularly in relation to Chapters 2, 3 and 4 is also included. Section 1.6 then considers issues relating to research design, and the different methodologies adopted in research design and data collection and analysis by three separate projects which inform the chapters of this book. The Geographical Limits of the west Midlands: Where does it begin and where does it end? The Local Government Boundary Commission for England (2010: http://www.lgbce.org.uk/) gives the geographical range of the west Midlands as the five counties of Herefordshire, Shropshire, Staffordshire, Warwickshire and Worcestershire. The boundaries of these five shire counties date back to at least the twelfth century, being ancient subdivisions established by the Normans for administration purposes after the 1066 conquest. The shire counties were, in most cases, based on earlier Anglo-Saxon divisions. In 1974, as a result of population density concentrated in parts of the shire counties, a sixth county, that of the West Midlands, was carved out from parts of the three shire counties of Staffordshire, Warwickshire and Worcestershire.

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

The construction and negotiation of identity and face in post observation feedback

Identity influences the practice of English language teachers and supervisors, their professional development and their ability to incorporate innovation and change. Talk during post observation feedback meetings provides participants with opportunities to articulate, construct, verify, contest and negotiate identities, processes which often engender issues of face. This study examines the construction and negotiation of identity and face in post observation feedback meetings between in-service English language teachers and supervisors at a tertiary institution in the United Arab Emirates. Within a linguistic ethnography framework, this study combined linguistic microanalysis of audio recorded feedback meetings with ethnographic data gathered from participant researcher knowledge, pre-analysis interviews and post-analysis participant interpretation interviews. Through a detailed, empirical description of situated ‘real life’ institutional talk, this study shows that supervisors construct identities involving authority, power, expertise, knowledge and experience while teachers index identities involving experience, knowledge and reflection. As well as these positive valued identities, other negative, disvalued identities are constructed. Identities are shown to be discursively claimed, verified, contested and negotiated through linguistic actions. This study also shows a link between identity and face. Analysis demonstrates that identity claims verified by an interactional partner can lead to face maintenance or support. However, a contested identity claim can lead to face threat which is usually managed by facework. Face, like identity, is found to be interactionally achieved and endogenous to situated discourse. Teachers and supervisors frequently risk face threat to protect their own identities, to contest their interactional partner’s identities or to achieve the feedback meeting goal i.e. improved teaching. Both identity and face are found to be consequential to feedback talk and therefore influence teacher development, teacher/supervisor relationships and the acceptance of feedback. Analysis highlights the evaluative and conforming nature of feedback in this context which may be hindering opportunities for teacher development.

Analysis and interpretation of serial position data

The representation of serial position in sequences is an important topic in a variety of cognitive areas including the domains of language, memory, and motor control. In the neuropsychological literature, serial position data have often been normalized across different lengths, and an improved procedure for this has recently been reported by Machtynger and Shallice (2009). Effects of length and a U-shaped normalized serial position curve have been criteria for identifying working memory deficits. We present simulations and analyses to illustrate some of the issues that arise when relating serial position data to specific theories. We show that critical distinctions are often difficult to make based on normalized data. We suggest that curves for different lengths are best presented in their raw form and that binomial regression can be used to answer specific questions about the effects of length, position, and linear or nonlinear shape that are critical to making theoretical distinctions. © 2010 Psychology Press.