1,684 research outputs found
Mobile content personalisation using intelligent user profile approach
As there are several limitations using mobile internet, mobile content personalisation seems to be an alternative to enhance the experience of using mobile internet. In this paper, we propose the mobile content personalisation framework to facilitate collaboration between the client and the server. This paper investigates clustering and classification techniques using K-means and Artificial Neural Networks (ANN) to predict user's desired content and WAP pages based on device's listed-oriented menu approach. We make use of the user profile and user's information ranking matrix to make prediction of the desired information for the user. Experimental results show that it can generate promising prediction. The results show that it works best when used for predicting 1 matched menu item on the screen
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
An IVR call performance classification system using computational intelligent techniques
Speech recognition adoption rate within Interactive Voice Response (IVR) systems is on
the increase. If implemented correctly, businesses experience an increase of IVR
utilization by customers, thus benefiting from reduced operational costs. However, it is
essential for businesses to evaluate the productivity, quality and call resolution
performance of these self-service applications. This research is concerned with the
development of a business analytics for IVR application that could assist contact centers
in evaluating these self-service IVR applications. A call classification system for a pay
beneficiary IVR application has been developed. The system comprises of field and call
performance classification components. âSay accountâ, âSay amountâ, âSelect beneficiaryâ
and âSay confirmationâ field classifiers were developed using Multi-Layer Perceptron
(MLP) Artificial Neural Network (ANN), Radial Basis Function (RBF) ANN, Fuzzy
Inference System (FIS) as well as Support Vector Machine (SVM). Call performance
classifiers were also developed using these computational intelligent techniques. Binary
and real coded Genetic Algorithm (GA) solutions were used to determine optimal MLP
and RBF ANN classifiers. These GA solutions produced accurate MLP and RBF ANN
classifiers. In order to increase the accuracy of the call performance RBF ANN classifier,
the classification threshold has been optimized. This process increased the classifier
accuracy by approximately eight percent. However, the field and call performance MLP
ANN classifiers were the most accurate ANN solutions. Polynomial and RBF SVM
kernel functions were most suited for field classifications. However, the linear SVM
kernel function is most accurate for call performance classification. When compared to
the ANN and SVM field classifiers, the FIS field classifiers did not perform well. The
FIS call performance classifier did outperform the RBF ANN call performance network.
Ensembles of MLP ANN, RBF ANN and SVM field classifiers were developed.
Ensembles of FIS, MLP ANN and SVM call performance classifiers were also
implemented. All the computational intelligent methods considered were compared in
relation to accuracy, sensitivity and specificity performance metrics. MLP classifier
solution is most appropriate for âSay accountâ field classification. Ensemble of field
classifiers and MLP classifier solutions performed the best in âSay amountâ field
classification. Ensemble of field classifiers and SVM classifier solutions are most suited
in âSelect beneficiaryâ and âSay confirmationâ field classifications. However, the
ensemble of call performance classifiers is the preferred classification solution for call
performance
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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