A linguistic study of interview and conversation

The present thesis focuses on the overall structure of the language of two types of Speech Exchange Systems (SES) : Interview (INT) and Conversation (CON). The linguistic structure of INT and CON are quantitatively investigated on three different but interrelated levels of analysis : Lexis, Syntax and Information Structure. The corpus of data 1n vest1gated for the project consists of eight sessions of pairs of conversants in carefully planned interviews followed by unplanned, surreptitiously recorded conversational encounters of the same pairs of speakers. The data comprise a total of approximately 15.200 words of INT talk and of about 19.200 words in CON. Taking account of the debatable assumption that the language of SES might be complex on certain linguistic levels (e.g. syntax) (Halliday 1979) and might be simple on others (e.g. lexis) in comparison to written discourse, the thesis sets out to investigate this complexity using a statistical approach to the computation of the structures recurrent in the language of INT and CON. The findings indicate clearly the presence of linguistic complexity in both types. They also show the language of INT to be slightly more syntactically and lexically complex than that of CON. Lexical density seems to be relatively high in both types of spoken discourse. The language of INT seems to be more complex than that of CON on the level of information structure too. This is manifested in the greater use of Inferable and other linguistically complex entities of discourse. Halliday's suggestion that the language of SES is syntactically complex is confirmed but not the one that the more casual the conversation is the more syntactically complex it becomes. The results of the analysis point to the general conclusion that the linguistic complexity of types of SES is not only in the high recurrence of syntactic structures, but also in the combination of these features with each other and with other linguistic and extralinguistic features. The linguistic analysis of the language of SES can be useful in understanding and pinpointing the intricacies of spoken discourse in general and will help discourse analysts and applied linguists in exploiting it both for theoretical and pedagogical purposes.

The operation of repair in L2 learner conversation, group work and classroom discourse

The present study is an empirical investigation into repair in spoken discourse, specifically focusing on L2 learner conversation, group work and teacher-fronted classroom interaction. The core of the investigation concentrates on identification of the problem type, classification of repair strategies and examination of interaction in the repair process. A comparison between Conversation (CS), Group Work (GW), and Teacher-fronted classroom interaction (CR) suggests that more repair is undertaken in CS. The results of the study suggest that the fundamental differences between CS, GW and CR are of two types: in the frequency of repair and in the nature of the repair itself. It has been found that other-initiation for production problem repair occurs mainly in CR, other-completion is characteristic of GW and self-repair is most frequent in CS. Factors affecting the occurrence of repair in CS, GW and CR are related to content and social and communicative features of context. Importantly, the study shows the frequency of repair in GW falls between that of CS and CR in most of repair strategies. This result lends support to the argument that group work can assist L2 learners to develop their communicative competence. It is suggested that the analysis of the repair process in CS, GW and CR can be useful in throwing light on the intricacies of spoken discourse in general and can be exploited by applied linguists for both theoretical and pedagogical purposes.

Systems development of data retrieval for an engineering company's after-sales service

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Automatic indexing and retrieval of encounter-specific evidence for point-of-care support

Evidence-based medicine relies on repositories of empirical research evidence that can be used to support clinical decision making for improved patient care. However, retrieving evidence from such repositories at local sites presents many challenges. This paper describes a methodological framework for automatically indexing and retrieving empirical research evidence in the form of the systematic reviews and associated studies from The Cochrane Library, where retrieved documents are specific to a patient-physician encounter and thus can be used to support evidence-based decision making at the point of care. Such an encounter is defined by three pertinent groups of concepts - diagnosis, treatment, and patient, and the framework relies on these three groups to steer indexing and retrieval of reviews and associated studies. An evaluation of the indexing and retrieval components of the proposed framework was performed using documents relevant for the pediatric asthma domain. Precision and recall values for automatic indexing of systematic reviews and associated studies were 0.93 and 0.87, and 0.81 and 0.56, respectively. Moreover, precision and recall for the retrieval of relevant systematic reviews and associated studies were 0.89 and 0.81, and 0.92 and 0.89, respectively. With minor modifications, the proposed methodological framework can be customized for other evidence repositories. © 2010 Elsevier Inc.

The mosaic test:benchmarking colour-based image retrieval systems using image mosaics

Evaluation and benchmarking in content-based image retrieval has always been a somewhat neglected research area, making it difficult to judge the efficacy of many presented approaches. In this paper we investigate the issue of benchmarking for colour-based image retrieval systems, which enable users to retrieve images from a database based on lowlevel colour content alone. We argue that current image retrieval evaluation methods are not suited to benchmarking colour-based image retrieval systems, due in main to not allowing users to reflect upon the suitability of retrieved images within the context of a creative project and their reliance on highly subjective ground-truths. As a solution to these issues, the research presented here introduces the Mosaic Test for evaluating colour-based image retrieval systems, in which test-users are asked to create an image mosaic of a predetermined target image, using the colour-based image retrieval system that is being evaluated. We report on our findings from a user study which suggests that the Mosaic Test overcomes the major drawbacks associated with existing image retrieval evaluation methods, by enabling users to reflect upon image selections and automatically measuring image relevance in a way that correlates with the perception of many human assessors. We therefore propose that the Mosaic Test be adopted as a standardised benchmark for evaluating and comparing colour-based image retrieval systems.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Recent developments in information retrieval:preface

This paper summarizes the scientific work presented at the 32nd European Conference on Information Retrieval. It demonstrates that information retrieval (IR) as a research area continues to thrive with progress being made in three complementary sub-fields, namely IR theory and formal methods together with indexing and query representation issues, furthermore Web IR as a primary application area and finally research into evaluation methods and metrics. It is the combination of these areas that gives IR its solid scientific foundations. The paper also illustrates that significant progress has been made in other areas of IR. The keynote speakers addressed three such subject fields, social search engines using personalization and recommendation technologies, the renewed interest in applying natural language processing to IR, and multimedia IR as another fast-growing area.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Exploring the effectiveness of similarity-based visualisations for colour-based image retrieval

In April 2009, Google Images added a filter for narrowing search results by colour. Several other systems for searching image databases by colour were also released around this time. These colour-based image retrieval systems enable users to search image databases either by selecting colours from a graphical palette (i.e., query-by-colour), by drawing a representation of the colour layout sought (i.e., query-by-sketch), or both. It was comments left by readers of online articles describing these colour-based image retrieval systems that provided us with the inspiration for this research. We were surprised to learn that the underlying query-based technology used in colour-based image retrieval systems today remains remarkably similar to that of systems developed nearly two decades ago. Discovering this ageing retrieval approach, as well as uncovering a large user demographic requiring image search by colour, made us eager to research more effective approaches for colour-based image retrieval. In this thesis, we detail two user studies designed to compare the effectiveness of systems adopting similarity-based visualisations, query-based approaches, or a combination of both, for colour-based image retrieval. In contrast to query-based approaches, similarity-based visualisations display and arrange database images so that images with similar content are located closer together on screen than images with dissimilar content. This removes the need for queries, as users can instead visually explore the database using interactive navigation tools to retrieve images from the database. As we found existing evaluation approaches to be unreliable, we describe how we assessed and compared systems adopting similarity-based visualisations, query-based approaches, or both, meaningfully and systematically using our Mosaic Test - a user-based evaluation approach in which evaluation study participants complete an image mosaic of a predetermined target image using the colour-based image retrieval system under evaluation.