Introduction

Corpora—large collections of written and/or spoken text stored and accessed electronically—provide the means of investigating language that is of growing importance academically and professionally. Corpora are now routinely used in the following fields: •the production of dictionaries and other reference materials; •the development of aids to translation; •language teaching materials; •the investigation of ideologies and cultural assumptions; •natural language processing; and •the investigation of all aspects of linguistic behaviour, including vocabulary, grammar and pragmatics.

A unification-based natural language interface to a database.

An implementation of a Lexical Functional Grammar (LFG) natural language front-end to a database is presented, and its capabilities demonstrated by reference to a set of queries used in the Chat-80 system. The potential of LFG for such applications is explored. Other grammars previously used for this purpose are briefly reviewed and contrasted with LFG. The basic LFG formalism is fully described, both as to its syntax and semantics, and the deficiencies of the latter for database access application shown. Other current LFG implementations are reviewed and contrasted with the LFG implementation developed here specifically for database access. The implementation described here allows a natural language interface to a specific Prolog database to be produced from a set of grammar rule and lexical specifications in an LFG-like notation. In addition to this the interface system uses a simple database description to compile metadata about the database for later use in planning the execution of queries. Extensions to LFG's semantic component are shown to be necessary to produce a satisfactory functional analysis and semantic output for querying a database. A diverse set of natural language constructs are analysed using LFG and the derivation of Prolog queries from the F-structure output of LFG is illustrated. The functional description produced from LFG is proposed as sufficient for resolving many problems of quantification and attachment.

Teaching and learning German language and culture in higher education in Britain:problems, challenges and didactical implications. A case study

The aim of this thesis is to explore key aspects and problems of the institutionalised teaching and learning of German language and culture in the context of German Studies in British Higher Education (HE). This investigation focuses on teaching and learning experiences in one department of German Studies in the UK, which is the micro-context of the present study, in order to provide an in-depth insight into real-life problems, strengths and weaknesses as they occur in the practice of teaching and learning German. Following Lamb (2004) and Holliday (1994), the present study acts on the assumption that each micro-context does not exist in vacuo but is always embedded in a wider socio-political and education environment, namely the macro-context, which largely determines how and what is taught. The macro-analysis of the present study surveys the socio-political developments that have recently affected the sector of modern languages and specifically the discipline of German Studies in the UK. It demonstrates the impact they have had on teaching and learning German at the undergraduate level in Britain. This context is interesting inasmuch as the situation in Britain is to a large extent a paradigmatic example of the developments in German Studies in English-speaking countries. Subsequently, the present study explores learning experiences of a group of thirty-five first year students. It focuses on their previous experiences in learning German, exposure to the target language, motivation, learning strategies and difficulties encountered, when learning German at the tertiary level. Then, on the basis of interviews with five lecturers of German, teaching experience in the context under study is explored, problems and successful teaching strategies discussed.

Collocation:applications and implications

All aspects of the concept of collocation – the phenomenon whereby words naturally tend to occur in the company of a restricted set of other words – are covered in this book. It deals in detail with the history of the word collocation, the concepts associated with it and its use in a linguistic context. The authors show the practical means by which the collocational behaviour of words can be explored using illustrative computer programs and examine applications in teaching, lexicography and natural language processing that use collocation in formation. The book investigates the place that collocation occupies in theories of language and provides a thoroughly comprehensive and up-to-date survey of the current position of collocation in language studies and applied linguistics. This text presents a comprehensive description of collocation, covering both the theoretical and practical background and the implications and applications of the concept as language model and analytical tool. It provides a definitive survey of currently available techniques and a detailed description of their implementation.

Extracting significant words from corpora for ontology extraction

We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.

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A weakly-supervised Bayesian model for violence detection from social media

Social streams have proven to be the mostup-to-date and inclusive information on cur-rent events. In this paper we propose a novelprobabilistic modelling framework, called violence detection model (VDM), which enables the identification of text containing violent content and extraction of violence-related topics over social media data. The proposed VDM model does not require any labeled corpora for training, instead, it only needs the in-corporation of word prior knowledge which captures whether a word indicates violence or not. We propose a novel approach of deriving word prior knowledge using the relative entropy measurement of words based on the in-tuition that low entropy words are indicative of semantically coherent topics and therefore more informative, while high entropy words indicates words whose usage is more topical diverse and therefore less informative. Our proposed VDM model has been evaluated on the TREC Microblog 2011 dataset to identify topics related to violence. Experimental results show that deriving word priors using our proposed relative entropy method is more effective than the widely-used information gain method. Moreover, VDM gives higher violence classification results and produces more coherent violence-related topics compared toa few competitive baselines.

Conversational interaction in the scanner:mentalizing during language processing as revealed by MEG

Humans are especially good at taking another's perspective-representing what others might be thinking or experiencing. This "mentalizing" capacity is apparent in everyday human interactions and conversations. We investigated its neural basis using magnetoencephalography. We focused on whether mentalizing was engaged spontaneously and routinely to understand an utterance's meaning or largely on-demand, to restore "common ground" when expectations were violated. Participants conversed with 1 of 2 confederate speakers and established tacit agreements about objects' names. In a subsequent "test" phase, some of these agreements were violated by either the same or a different speaker. Our analysis of the neural processing of test phase utterances revealed recruitment of neural circuits associated with language (temporal cortex), episodic memory (e.g., medial temporal lobe), and mentalizing (temporo-parietal junction and ventromedial prefrontal cortex). Theta oscillations (3-7 Hz) were modulated most prominently, and we observed phase coupling between functionally distinct neural circuits. The episodic memory and language circuits were recruited in anticipation of upcoming referring expressions, suggesting that context-sensitive predictions were spontaneously generated. In contrast, the mentalizing areas were recruited on-demand, as a means for detecting and resolving perceived pragmatic anomalies, with little evidence they were activated to make partner-specific predictions about upcoming linguistic utterances.

Corpus linguistics

Corpora—large collections of written and/or spoken text stored and accessed electronically—provide the means of investigating language that is of growing importance academically and professionally. Corpora are now routinely used in the following fields: The production of dictionaries and other reference materials; The development of aids to translation; Language teaching materials; The investigation of ideologies and cultural assumptions; Natural language processing; and The investigation of all aspects of linguistic behaviour, including vocabulary, grammar and pragmatics.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

An investigation into the tension arising between natural language and mathematical language experienced by mechanical engineering students

This investigation is grounded within the concept of embodied cognition where the mind is considered to be part of a biological system. A first year undergraduate Mechanical Engineering cohort of students was tasked with explaining the behaviour of three balls of different masses being rolled down a ramp. The explanations given by the students highlighted the cognitive conflict between the everyday interpretation of the word energy and its mathematical use. The results showed that even after many years of schooling, students found it challenging to interpret the mathematics they had learned and relied upon pseudo-scientific notions to account for the behaviour of the balls.

Parsing mathematical constructs:results from a preliminary eye tracking study

The semantic model described in this paper is based on ones developed for arithmetic (e.g. McCloskey et al. 1985, Cohene and Dehaene 1995), natural language processing (Fodor 1975, Chomsky 1981) and work by the author on how learners parse mathematical structures. The semantic model highlights the importance of the parsing process and the relationship between this process and the mathematical lexicon/grammar. It concludes by demonstrating that for a learner to become an efficient, competent mathematician a process of top-down parsing is essential.

The development of a semantic model for the interpretation of mathematics including the use of technology

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.

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.

Helicopter vibration sensor selection using data visualisation

The main objective of the project is to enhance the already effective health-monitoring system (HUMS) for helicopters by analysing structural vibrations to recognise different flight conditions directly from sensor information. The goal of this paper is to develop a new method to select those sensors and frequency bands that are best for detecting changes in flight conditions. We projected frequency information to a 2-dimensional space in order to visualise flight-condition transitions using the Generative Topographic Mapping (GTM) and a variant which supports simultaneous feature selection. We created an objective measure of the separation between different flight conditions in the visualisation space by calculating the Kullback-Leibler (KL) divergence between Gaussian mixture models (GMMs) fitted to each class: the higher the KL-divergence, the better the interclass separation. To find the optimal combination of sensors, they were considered in pairs, triples and groups of four sensors. The sensor triples provided the best result in terms of KL-divergence. We also found that the use of a variational training algorithm for the GMMs gave more reliable results.