High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic

Lee, Lam Hong; Isa, Dino; Choo, Wou Onn; Chue, Wen Yeen

doi:10.1016/j.eswa.2011.07.116

Cited by 29 publications

(10 citation statements)

References 11 publications

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“…CA research is an active and complex field with the key challenge of adequately understanding a user input to produce an appropriate response (O'Shea et al 2011). There are two main approaches to understanding user input:  Semantic-based CAs endeavour to understand the meaning of an utterance by analysing the constructs and meaning of natural language (Lee, Isa, Choo & Chue 2012) or by scoring the semantic similarity of phrases (Li, Bandar, McLean & O'Shea 2004).  Pattern matching CAs (Wallace 2009;Convagent Ltd 2005) use an algorithm to match key words and phrases within an utterance to a knowledge base of pattern-based stimulus-response pairs rather than endeavouring to understand the input.…”

Section: Conversational Agentsmentioning

confidence: 99%

An adaptation algorithm for an intelligent natural language tutoring system

Latham

Crockett

McLean

2014

Computers & Education

100

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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.

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Section: Conversational Agentsmentioning

confidence: 99%

An adaptation algorithm for an intelligent natural language tutoring system

Latham

Crockett

McLean

2014

Computers & Education

100

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“…It can merge two adjacent concepts and forms an upper layer (or thicker granularity) concept. In cloud model, the concept merger operations are described as follows [ 7 , 8 ].…”

Section: Cloud Virtual Pan-concept-tree and Concept Jumping Upmentioning

confidence: 99%

“…In recent years, with the continued development and innovation of information technology and natural language processing (NLP) fields, it has laid a solid theory and practice basis for text classification. An increasing number of supervised classification approaches have been developed for various types of classification tasks, such as decision trees (DT) [ 2 , 3 ], neural networks (NN) [ 4 , 5 ], naive Bayes (NB) [ 6 – 8 ], support vector machines (SVM) [ 9 – 12 ], and k nearest neighbor( k NN) [ 13 – 15 ]. These classifiers have their own characteristics, but in the perspective of comprehensive performance, SVM, k NN, and NB methods are more excellent [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

Dai

Liu

2014

The Scientific World Journal

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The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC) is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.

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“…Desta forma, ela é apresentada no Capítulo 4 como base teórica.Na etapa de construção de atributos, o conjunto de dados devidamente categorizado é representado apropriadamente para o algoritmo de aprendizado. Duas representações bem conhecidas de dado textual são: Atributos simples (palavras-chave ou frases-chave) incluindo unigramas, bigramas e n-gramas(68,69,70,71,72); -Taxonomias ou ontologias de atributos(73,74,75,76,77,78,79,80); -Atributos de domínios específicos, como listas de palavras associadas a emoção ou a opinião(81,82,83); Essa questão de descobrir o valor numérico de um atributo, incluindo seu impacto na classificação de texto, é amplamente discutida na literatura. Os métodos existentes para isso podem ser agrupados em i) clássicos e ii) modernos.…”

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Segmentação Semântica De Vagas De Emprego: Estudo Comparativo De Algoritmos Clássicos De Aprendizado De Máquina

MARTINS¹

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This dissertation demonstrates how web mining, natural language processing, and machine learning can be combined to improve understanding of job openings by semantically segmenting the texts of their descriptions. To achieve this purpose, textual data were collected from three major job sites: Catho, LinkedIn and VAGAS.com.br. Based on the literature, this work proposes a simplified semantic structure in which each sentence of the job description can belong to one of these classes: Responsibilities, Requirements, Benefits and Others. With this idea, the semantic segmentation task can be rethought as a sentence segmentation followed by a classification. Using Python as a tool, some ways of constructing features from texts are tried out, both lexical and semantic, and four classic machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest. As a result, this work presents a classifier (Logistic Regression with binary representation) with 95.58% accuracy, without model overfitting and without degeneration by class unbalance, which is comparable to state-of-the-art for Text Classification. This classifier was trained and validated using Catho data, but was also tested on VAGAS.com.br (88.60%) and LinkedIn (91.14%) data, providing evidence that its learning is generalizable to data from other sites. In addition, the classifier was used for semantic segmentation of job openings and obtained a P k metric equals to 3.67% and a WindowDiff metric equals to 4.78%, which is comparable to state-of-the-art for Text Segmentation. Finally, it is worth highlighting two indirect contributions of this work: 1) a structure for thinking and analyzing job openings and 2) an indication that classical algorithms can also reach the state of the art and therefore should always be tried.

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High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic

Cited by 29 publications

References 11 publications

An adaptation algorithm for an intelligent natural language tutoring system

An adaptation algorithm for an intelligent natural language tutoring system

Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

Segmentação Semântica De Vagas De Emprego: Estudo Comparativo De Algoritmos Clássicos De Aprendizado De Máquina

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