A corpus-based analysis of transfer effects and connected speech processes in Vietnamese English

This paper presents a corpus-based descriptive analysis of the most prevalent transfer effects and connected speech processes observed in a comparison of 11 Vietnamese English speakers (6 females, 5 males) and 12 Australian English speakers (6 males, 6 females) over 24 grammatical paraphrase items. The phonetic processes are segmentally labelled in terms of IPA diacritic features using the EMU speech database system with the aim of labelling departures from native-speaker pronunciation. An analysis of prosodic features was made using ToBI framework. The results show many phonetic and prosodic processes which make non-native speakers’ speech distinct from native ones. The corpusbased methodology of analysing foreign accent may have implications for the evaluation of non-native accent, accented speech recognition and computer assisted pronunciation- learning.

Research cultures and the pragmatic functions of humor in academic research presentations:A corpus-assisted analysis

Based on a corpus of English, German, and Polish spoken academic discourse, this article analyzes the distribution and function of humor in academic research presentations. The corpus is the result of a European research cooperation project consisting of 300,000 tokens of spoken academic language, focusing on the genres research presentation, student presentation, and oral examination. The article investigates difference between the German and English research cultures as expressed in the genre of specialist research presentations, and the role of humor as a pragmatic device in their respective contexts. The data is analyzed according to the paradigms of corpus-assisted discourse studies (CADS). The findings show that humor is used in research presentations as an expression of discourse reflexivity. They also reveal a considerable difference in the quantitative distribution of humor in research presentations depending on the educational, linguistic, and cultural background of the presenters, thus confirming the notion of different research cultures. Such research cultures nurture distinct attitudes to genres of academic language: whereas in one of the cultures identified researchers conform with the constraints and structures of the genre, those working in another attempt to subvert them, for example by the application of humor. © 2012 Elsevier B.V.

A statistical analysis of regional variation in adverb position in a corpus of written Standard American English

This paper investigates whether the position of adverb phrases in sentences is regionally patterned in written Standard American English, based on an analysis of a 25 million word corpus of letters to the editor representing the language of 200 cities from across the United States. Seven measures of adverb position were tested for regional patterns using the global spatial autocorrelation statistic Moran’s I and the local spatial autocorrelation statistic Getis-Ord Gi*. Three of these seven measures were indentified as exhibiting significant levels of spatial autocorrelation, contrasting the language of the Northeast with language of the Southeast and the South Central states. These results demonstrate that continuous regional grammatical variation exists in American English and that regional linguistic variation exists in written Standard English.

On the F word:a corpus-based analysis of the media representation of feminism in British and German press discourse, 1990-2009

Research in social psychology has shown that public attitudes towards feminism are mostly based on stereotypical views linking feminism with leftist politics and lesbian orientation. It is claimed that such attitudes are due to the negative and sexualised media construction of feminism. Studies concerned with the media representation of feminism seem to confirm this tendency. While most of this research provides significant insights into the representation of feminism, the findings are often based on a small sample of texts. Also, most of the research was conducted in an Anglo-American setting. This study attempts to address some of the shortcomings of previous work by examining the discourse of feminism in a large corpus of German and British newspaper data. It does so by employing the tools of Corpus Linguistics. By investigating the collocation profiles of the search term feminism, we provide evidence of salient discourse patterns surrounding feminism in two different cultural contexts. © The Author(s) 2012.

A Curated Corpus for Sentiment-Topic Analysis

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Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.

Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

In vitro and in vivo analysis of co-electrospun scaffolds made of medical grade poly(epsilon-caprolactone) and porcine collagen

In this study, a nanofiber mesh made by co-electrospinning medical grade poly(epsilon-caprolactone) and collagen (mPCL/Col) was fabricated and studied. Its mechanical properties and characteristics were analyzed and compared to mPCL meshes. mPCL/Col meshes showed a reduction in strength but an increase in ductility when compared to PCL meshes. In vitro assays revealed that mPCL/Col supported the attachment and proliferation of smooth muscle cells on both sides of the mesh. In vivo studies in the corpus cavernosa of rabbits revealed that the mPCL/Col scaffold used in conjunction with autologous smooth muscle cells resulted in better integration with host tissue when compared to cell free scaffolds. On a cellular level preseeded scaffolds showed a minimized foreign body reaction.

Analysis of the effect of negation on information retrieval of medical data

Most information retrieval (IR) models treat the presence of a term within a document as an indication that the document is somehow "about" that term, they do not take into account when a term might be explicitly negated. Medical data, by its nature, contains a high frequency of negated terms - e.g. "review of systems showed no chest pain or shortness of breath". This papers presents a study of the effects of negation on information retrieval. We present a number of experiments to determine whether negation has a significant negative affect on IR performance and whether language models that take negation into account might improve performance. We use a collection of real medical records as our test corpus. Our findings are that negation has some affect on system performance, but this will likely be confined to domains such as medical data where negation is prevalent.

Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

Allegations of child sexual abuse in family court cases : a qualitative analysis of psychiatric evidence

Allegations of child sexual abuse in Family Court cases have gained increasing attention. The study investigates factors involved in Family Court cases involving allegations of child sexual abuse. A qualitative methodology was employed to examine Records of Judgement and Psychiatric Reports for 20 cases distilled from the data corpus of 102 cases. A seven-stage methodology was developed utilising a thematic analysis process informed by principles of grounded theory and phenomenology. The explication of eight thematic clusters was undertaken. The findings point to complex issues and dynamics in which child sexual abuse allegations have been raised. The alleging parent’s allegations of sexual abuse against their ex-partner may be: the expression of unconscious deep fears for their children’s welfare, or an action to meet their needs for personal affirmation in the context of the painful upheaval of a relationship break-up. Implications of the findings are discussed.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

Examining the Assessment Opportunities for Cultural Connectedness for Student Learning : A Sociocultural Analysis

The disparity that exists between the highest and lowest achievers together with deficit approaches to teaching, learning and assessment raise serious equity issues related to fairness, validity, culture and access, which were analysed in a recent Australian Research Council funded project. This chapter explores the potential that exists for teachers to work with Indigenous Teacher Assistants (ITAs) to secure cultural connectedness in teaching, learning and assessment of Indigenous students. The study was a design experiment, which was conducted in seven Catholic and Independent primary schools in northern Queensland and involved semi-structured focus group interviews with Year 4 and 6 Indigenous students, principals, teachers and Indigenous Teacher Assistants. Classroom observations and document analyses were also conducted. This corpus of data was analysed using a sociocultural theoretical lens. The use of a sociocultural analysis helped to identify cultural influences, Indigenous students’ funds of knowledge and values. The information from this analysis was made explicit to teachers to demonstrate how they could enhance their pedagogic and assessment practices by embracing and extending the cultural spaces for learning and teaching of Indigenous students. The way in which teachers construct their interactions for greater cultural connectedness and enhanced learning would appear to rely on relationship building with Indigenous staff, Indigenous students’ cultural knowledge, and improved understanding of assessment and related equity issues.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.