Other affiliations: University of Manchester
Bio: Keeley Crockett is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Semantic similarity & Fuzzy classification. The author has an hindex of 19, co-authored 145 publications receiving 2243 citations. Previous affiliations of Keeley Crockett include University of Manchester.
Papers published on a yearly basis
TL;DR: Experiments demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition and can be used in a variety of applications that involve text knowledge representation and discovery.
Abstract: Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition
TL;DR: A generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style is proposed.
Abstract: This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100% Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%
TL;DR: The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.
Abstract: The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.
••26 Mar 2008
TL;DR: A comparative study of STASIS and LSA is described, which shows measures of semantic similarity can be applied to short texts for use in Conversational Agents (CAs), and a benchmark data set of 65 sentence pairs with human-derived similarity ratings is presented.
Abstract: This paper describes a comparative study of STASIS and LSA These measures of semantic similarity can be applied to short texts for use in Conversational Agents (CAs) CAs are computer programs that interact with humans through natural language dialogue Business organizations have spent large sums of money in recent years developing them for online customer selfservice, but achievements have been limited to simple FAQ systems We believe this is due to the labour-intensive process of scripting, which could be reduced radically by the use of short-text semantic similarity measures "Short texts" are typically 10-20 words long but are not required to be grammatically correct sentences, for example spoken utterances and text messages We also present a benchmark data set of 65 sentence pairs with human-derived similarity ratings This data set is the first of its kind, specifically developed to evaluate such measures and we believe it will be valuable to future researchers
01 Jan 2017-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables.
Abstract: Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Jan 2009
TL;DR: This article proposes a framework for representing the meaning of word combinations in vector space in terms of additive and multiplicative functions, and introduces a wide range of composition models that are evaluated empirically on a phrase similarity task.
Abstract: Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.
TL;DR: This first of its kind, comprehensive literature review of the diverse field of affective computing focuses mainly on the use of audio, visual and text information for multimodal affect analysis, and outlines existing methods for fusing information from different modalities.
Abstract: First review on affective computing that is dealing with both unimodal and multimodal analysis.The survey takes into account recent approaches, e.g., embeddings, which are missing from previous reviews.It covers and compares all state-of-the-art methods in details, while most available surveys just quickly describes them. Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field.