Learning to recognize webpage genres

Kanaris, Ioannis; Stamatatos, Efstathios

doi:10.1016/j.ipm.2009.05.003

Cited by 40 publications

(36 citation statements)

References 22 publications

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“…frequency-based, chi-square, information gain, mutual information) and the classification model (e.g., SVM, decision trees, neural networks, etc.) [3,5,6,10,11,9,12,7]. To the best of our knowledge, all published studies consider AGI as a closed-set classification approach.…”

Section: Related Workmentioning

confidence: 99%

“…Many studies underline the effectiveness of the character n-grams for this task [5,12,3]. This type of feature has been used in combination with classification methods able to handle very high number of features such as SVM as well as similarity-based methods that construct one representation vector per genre [12].…”

Section: Related Workmentioning

confidence: 99%

“…This type of feature has been used in combination with classification methods able to handle very high number of features such as SVM as well as similarity-based methods that construct one representation vector per genre [12]. The combination of variable length n-grams, where a LocalMaxs algorithm was selecting the proper mixture of n-gram lengths, has also been proposed [5]. Character n-gram features seem to be very effective in combination with the binary term weighting scheme [5,3].…”

Section: Related Workmentioning

confidence: 99%

“…The combination of variable length n-grams, where a LocalMaxs algorithm was selecting the proper mixture of n-gram lengths, has also been proposed [5]. Character n-gram features seem to be very effective in combination with the binary term weighting scheme [5,3].…”

Section: Related Workmentioning

confidence: 99%

“…It seems that structural information is useful as a complement to textual features. Combining structural with textual information usually improves the classifiers performance [5].…”

Section: Related Workmentioning

confidence: 99%

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Open-Set Classification for Automated Genre Identification

Pritsos

Stamatatos

2013

Lecture Notes in Computer Science

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Abstract. Automated Genre Identification (AGI) of web pages is a problem of increasing importance since web genre (e.g. blog, news, eshops, etc.) information can enhance modern Information Retrieval (IR) systems. The state-of-the-art in this field considers AGI as a closed-set classification problem where a variety of web page representation and machine learning models have intensively studied. In this paper, we study AGI as an open-set classification problem which better formulates the real world conditions of exploiting AGI in practice. Focusing on the use of content information, different text representation methods (words and character n-grams) are tested. Moreover, two classification methods are examined, one-class SVM learners, used as a baseline, and an ensemble of classifiers based on random feature subspacing, originally proposed for author identification. It is demonstrated that very high precision can be achieved in open-set AGI while recall remains relatively high.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…It seems that structural information is useful as a complement to textual features. Combining structural with textual information usually improves the classifiers performance [5].…”

Section: Related Workmentioning

confidence: 99%

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Open-Set Classification for Automated Genre Identification

Pritsos

Stamatatos

2013

Lecture Notes in Computer Science

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Laypeople's source selection in online health information‐seeking process

Chi

Jeng

2020

Asso for Info Science & Tech

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For laypeople, searching online health information resources can be challenging due to topic complexity and the large number of online sources with differing quality. The goal of this article is to examine, among all the available online sources, which online sources laypeople select to address their health‐related information needs, and whether or how much the severity of a health condition influences their selection. Twenty‐four participants were recruited individually, and each was asked (using a retrieval system called HIS) to search for information regarding a severe health condition and a mild health condition, respectively. The selected online health information sources were automatically captured by the HIS system and classified at both the website and webpage levels. Participants' selection behavior patterns were then plotted across the whole information‐seeking process. Our results demonstrate that laypeople's source selection fluctuates during the health information‐seeking process, and also varies by the severity of health conditions. This study reveals laypeople's real usage of different types of online health information sources, and engenders implications to the design of search engines, as well as the development of health literacy programs.

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