Robert Lothian

Robert Gordon University | RGU · Institute for Innovation, Design and Sustainability (IDEAS)

Purpose Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this pa...

The lexicon-based approaches to opinion mining involve the extraction of term polarities from sentiment lexicons and the aggregation of such scores to predict the overall sentiment of a piece of text. It is typically preferred where sentiment labelled data is difficult to obtain or algorithm robustness across different domains is essential. A major...

The lexicon-based approach to opinion mining is typically preferred where training data is difficult to obtain or cross domain robustness of algorithms is of essence. However, this approach suffers from the semantic gap between the polarity with which a sentiment-bearing term appears in the text (i.e. contextual polarity) and its prior polarity cap...

Sentiment lexicon is a crucial resource for opinion mining from social media content. However, standard off-the-shelve lexicons are static and typically do not adapt, in content and context, to a target domain. This limitation, adversely affects the effectiveness of sentiment analysis algorithms. In this paper, we introduce the idea of distant-supe...

Indexing of textual cases is commonly affected by the problem of variation in vocabulary. Semantic indexing is commonly used to address this problem by discovering semantic or conceptual relatedness between individual terms and using this to improve textual case representation. However, representations produced using this approach are not optimal f...

General knowledge sentiment lexicons have the advantage of wider term coverage. However, such lexicons typically have inferior performance for sentiment classification compared to using domain focused lexicons or machine learning classifiers. Such poor performance can be attributed to the fact that some domain-specific sentiment-bearing terms may n...

The variation in natural language vocabulary remains a challenge for text representation as the same idea can be expressed in many different ways. Thus document representations often rely on generalisation to map low-level lexical expressions to higher level concepts in order to capture the inherent semantics of the documents. Term-relatedness meas...

An important application domain for Machine learning is sentiment classification. Here, the traditional approach is to represent documents using a Bag-Of-Words (BOW) model, where individual terms are used as features. However, the BOW model is unable to sufficiently model the variation inherent in natural language text. Term-relatedness metrics are...

Automatically generated sentiment lexicons offer sentiment information for a large number of terms and often at a more granular level than manually generated ones. While such rich information has the potential of enhancing sentiment analysis, it also presents the challenge of finding the best possible strategy to utilising the information. In Senti...

Textual case-based reasoning (TCBR) solves new problems by reusing previous similar problem-solving experiences documented as text. During reuse, TCBR identifies reusable textual constructs in the retrieved solution content and differentiates from the rest that need revision. However, reuse is heavily influenced by the quality of retrieval since TC...

Textual Case-Based Reasoning (TCBR) aims at effective reuse of past problem-solving experiences that are predominantly captured in unstructured form. The absence of structure and a well-defined feature space makes comparison of these experiential cases difficult. Since reasoning is primarily dependent on retrieval of similar cases, the acquisition...

Objectives: Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Partin tables to support their decisions. However, Partin tables are based on logistic regression (LR)...

Expressiveness of natural language is a challenge for text representation since the same idea can be expressed in many different ways. Therefore, terms in a document should not be treated independently of one another since together they help to disambiguate and establish meaning. Term-similarity measures are often used to improve representation by...

The need for automated text evaluation is common to several AI disciplines. In this work, we explore the use of Machine Translation (MT) evaluation metrics for Textual Case Based Reasoning (TCBR). MT and TCBR typically propose textual solutions and both rely on human reference texts for evaluation purposes. Current TCBR evaluation metrics such as...

Textual reuse is an integral part of textual case-based reasoning (TCBR) which deals with solving new problems by reusing previous similar problem-solving experiences documented as text. We investigate the role of text reuse for text authoring applications that involve feedback or review generation. Generally providing feedback in the form of assig...

This paper proposes textual reuse as the identification of reusable textual constructs in a retrieved solution text. This is done by annotating a solution text so that reusable sections are identifiable from those that need revision. We present a novel and generic architecture, Case Retrieval Reuse Net (CR2N), that can be used to generate these ann...

The advent of Web 2.0 has created a proliferation of resource sharing sites where individual users tag resources. Retrieval performance is good when users share the same vocabulary, but deteriorates when users have diverging vo-cabularies. In this paper we propose a novel method of reusing search experi-ence to transform the underlying representati...

This paper deals with two relatively less well studied problems in Textual CBR, namely visualizing and evaluating complexity of textual case bases. The first is useful in case base maintenance, the second in making informed choices regarding case base representation and tuning of parameters for the TCBR system, and also for explaining the behaviour...

We present a novel approach to mine word similarity in Textual Case Based Reasoning. We exploit indirect associations of words, in addition to direct ones for estimating their similarity. If word A co-occurs with word B, we say A and B share a first order association between them. If A co-occurs with B in some documents, and B with C in some others...

Patterns of successive saccades and fixations (scan paths) that are made while viewing images are often spatially restricted in schizophrenia, but the relation with cannabis-induced psychosis has not been examined. We used higher-order statistical methods to examine spatiotemporal characteristics of scan paths to determine whether viewing behaviour...

Sentiment Analysis aims to determine the overall sentiment orientation of a given input text. One motivation for research in this area is the need for consumer related industries to extract public opinion from online portals such as blogs, discussion boards, and reviews. Esti- mating sentiment orientation in text involves extraction of sentiment ri...

Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and pol- ysemy in text retrieval applications. However, since LSI ignores class labels of training documents, LSI generated representations are not as effective in classification tasks. To address this limitation, a process called 'sprinkling' is presented. Sp...

The Robert Gordon University (RGU) participated in the Opinion Re- trieval Task of the Trec 2007 Blog Track. At the core of the system we developed is a set of training documents labeled with respect to opinion. These documents are used to train a classifier in order to classify the documents that are rele vant to the given Trec topics. However, a...

Feature selection for unsupervised tasks is particularly challenging, especially when dealing with text data. The increase in online documents and email communication creates a need for tools that can operate without the su- pervision of the user. In this paper we look at novel feature selection techniques that address this need. A distributional s...

Case Retrieval Networks (CRNs) facilitate flexible and efficient retrieval in Case-Based Reasoning (CBR) systems. While CRNs scale up well to handle large numbers of cases in the case-base, the retrieval efficiency is still critically determined by the number of feature values (referred to as Information Entities) and by the nature of similarity re...

Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and polysemy in text retrieval applications. However, since LSI ignores class labels of training documents, LSI generated representations are not as effective in classification tasks. To address this limitation, a process called ‘sprinkling’ is presented. Sprin...

This paper addresses the task of learning concept descriptions from streams of data. As new data are obtained the concept description has to be updated regularly to include the new data. In this case we can face the problem that the concept changes over time. Hence the old data become irrelevant to the current concept and have to be removed from th...

Problem solving with experiences that are recorded in text form requires a mapping from text to structured cases, so that case comparison can provide informed feedback for reasoning. One of the challenges is to acquire an indexing vocabulary to describe cases. We explore the use of machine learning and statistical techniques to automate aspects of...

Coronal field structures corresponding to specified normal fields on the photosphere are investigated using a force-free model. The force-free parameter is insufficient to determine the field uniquely, and so a second parameter representing the contribution of the complementary Green's function is included. For isolated loops represented by a singl...

An M1.3 solar flare was observed on April 12th, 2000 in EUV by TRACE. Photospheric magnetograms were also obtained from observations by SOHO/MDI around the time of the flare. The magnetic field of the active region in which the flare occurred has been reconstructed from the MDI data, using a Green's function approach. A constant-alpha force-free ap...

A model is developed to describe a coronal loop, which may originate from a photospheric source of smaller size than the coronal radius of the loop. The energy and relative helicity of the loop are evaluated, as are two alternative estimates of the energy available for coronal heating. Both of these estimates are strongly dependent on the size of t...

A model of the equilibrium structure of the coronal magnetic field is developed, taking account of the fact that field lines are rooted in the photosphere, where field is concentrated into isolated flux tubes. The field is force-free, described by B = B, with constant; this field has special physical significance, being the state of mininum energy...

The loss of equilibrium in coronal magnetic field structures is a possible source of energy for coronal heating and solar flares. We investigate whether such a loss of equilibrium occurs when a coronal loop is progressively twisted by photospheric motions. In studies of 2-D cylindrical equilibria, long loops have been found to be of constant cross-...

The evolution of coronal loops in response to slow photospheric twisting motions is investigated using a variety of methods. Firstly, by solving the time-dependent equations it is shown that the field essentially evolves through a sequence of 2-D equilibria with no evidence of rapid dynamic evolution. Secondly, a sequence of 1-D equilibria are show...

The shape of a magnetic flux tube is investigated when photospheric motion causes small twist at the magnetic footpoints. Using a Fourier-Bessel series expansion, the previous results of Zweibel and Boozer (1985) and Steinolfson and Tajima (1987), when the twist is small, are substantiated. A twisting motion that is restricted to a finite region is...

This paper looks at feature selection for ordinal text clas-sification. Typical applications are sentiment and opinion classification, where classes have relationships based on an ordinal scale. We show that standard feature selection using Information Gain (IG) fails to identify discriminatory features, particularly when they are distributed over...

We present an approach to visualize textual case bases by "stacking" similar cases and features close to each other in an image derived from the case-feature matrix. We propose a complexity measure called GAME that exploits regularities in stacked images to evaluate the alignment between problem and solution components of cases. GAME class , a coun...