Using lexical chains for keyword extraction

Ercan, Gönenç; Çiçekli, İlyas

doi:10.1016/j.ipm.2007.01.015

Cited by 181 publications

(72 citation statements)

References 8 publications

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“…In this study, the keywords were assigned manually by the instructor to each item since the number of learning materials used was relatively small. An automatic keyword extraction can be used when the number of items is large (Ercan & Cicekli, 2007). However, the recommended items may differ between the case where human assigned keywords are used and the case where automatically extracted keywords are used, and as the similarities between items are heavily dependent on the number of the matched keywords, this may affect the recommendation accuracy.…”

Section: Resultsmentioning

confidence: 99%

Measuring learner's performance in e-learning recommender systems

Ghauth

Abdullah

2010

AJET

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A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.

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Section: Resultsmentioning

confidence: 99%

Measuring learner's performance in e-learning recommender systems

Ghauth

Abdullah

2010

AJET

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“…These keywords found in articles are important because they represent the concepts that authors want to communicate and draw the readers and the scientific community attention. According to Ercan and Cicekli (2007), the keywords are concise representations of a certain text, which allows the reader to identify the relevance of the theme in advance. In this sense, the following analysis presents the articles distribution of the Bibliographic Portfolio regarding the coverage of the subjects in relation to sustainability.…”

Section: International Journal Of Advanced Engineering Research and Smentioning

confidence: 99%

Sustainable Horticulture: A bibliometric Study

Alvarenga¹,

Rodriguez²,

Sartori³

et al. 2018

IJAERS

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“…The different approaches for the first technique -keyword extraction-can be grouped into three categories: systems based on machine learning; systems based on rules for patterns and systems supported by statistical criteria (Ercan and Cicekli, 2007;Giarlo, 2005;Kaur and Gupta, 2010). These different approaches can also be combined.…”

Section: Literature Reviewmentioning

confidence: 99%

A semi-automatic indexing system based on embedded information in HTML documents

et al. 2015

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Purpose -This paper describes and evaluates the tool DigiDoc MetaEdit which allows the semi-automatic indexing of HTML documents. The tool works by identifying and suggesting keywords from a thesaurus according to the embedded information in HTML documents. This enables the parameterization of keyword assignment based on how frequently the terms appear in the document, the relevance of their position, and the combination of both.Design/methodology/approach -In order to evaluate the efficiency of the indexing tool, the descriptors/keywords suggested by the indexing tool are compared to the keywords which have been indexed manually by human experts. To make this comparison a corpus of HTML documents are randomly selected from a journal devoted to Library and Information Science.Findings -The results of the evaluation show that there: (1) is close to a 50% match or overlap between the two indexing systems, however if you take into consideration the related terms and the narrow terms the matches can reach 73%; and (2) the first terms identified by the tool are the most relevant.Originality/value -The tool presented identifies the most important keywords in an HTML document based on the embedded information in HTML documents. Nowadays, representing the contents of documents with keywords is an essential practice in areas such as information retrieval and e-commerce.

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Using lexical chains for keyword extraction

Cited by 181 publications

References 8 publications

Measuring learner's performance in e-learning recommender systems

Measuring learner's performance in e-learning recommender systems

Sustainable Horticulture: A bibliometric Study

A semi-automatic indexing system based on embedded information in HTML documents

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