Web Page Classification*

Choi, Ben; Yao, Zhen

doi:10.1007/11362197_9

Cited by 36 publications

(9 citation statements)

References 93 publications

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“…Earlier work has shown some indications that other elements may also have positive or negative effects on the relevance of their contents, such as text style elements (bold, italic, etc.) or anchor text [11,30]. Additionally, we could experiment with different weights for the different sizes of headers (h1 to h6).…”

Section: Discussionmentioning

confidence: 99%

“…Although previous research on unsupervised keyphrase extraction from web pages is limited, it has repeatedly been shown that the HTML element in which a word or phrase appears correlates with its relevance for the topic of a page [11][12][13]. Thomaidou and Vazirgiannis proposed an unsupervised keyphrase recommendation system for web pages that used hypertext location information [14].…”

Section: Hypertext Documentsmentioning

confidence: 99%

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Unsupervised Keyphrase Extraction for Web Pages

Haarman

Zijlema²,

Wiering

2019

MTI

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Keyphrase extraction is an important part of natural language processing (NLP) research, although little research is done in the domain of web pages. The World Wide Web contains billions of pages that are potentially interesting for various NLP tasks, yet it remains largely untouched in scientific research. Current research is often only applied to clean corpora such as abstracts and articles from academic journals or sets of scraped texts from a single domain. However, textual data from web pages differ from normal text documents, as it is structured using HTML elements and often consists of many small fragments. These elements are furthermore used in a highly inconsistent manner and are likely to contain noise. We evaluated the keyphrases extracted by several state-of-the-art extraction methods and found that they did not transfer well to web pages. We therefore propose WebEmbedRank, an adaptation of a recently proposed extraction method that can make use of structural information in web pages in a robust manner. We compared this novel method to other baselines and state-of-the-art methods using a manually annotated dataset and found that WebEmbedRank achieved significant improvements over existing extraction methods on web pages.

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Section: Discussionmentioning

confidence: 99%

Section: Hypertext Documentsmentioning

confidence: 99%

Unsupervised Keyphrase Extraction for Web Pages

Haarman

Zijlema²,

Wiering

2019

MTI

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“…More recently the ability of genre to improve retrieval from large collections of documents including the web has made it of considerable interest for IR and automatic classification. Genre theory informs one of the two approaches to automatic webpage classification, complementing classification based on content or subject (Choi and Yao, 2005). One of genres' major potentials in webpage classification is that they are especially suitable for collections made up of different types of documents.…”

Section: Introductionmentioning

confidence: 99%

Genre analysis of bookmarked webpages

Montesi

2010

Online Information Review

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PurposeA total of 17 user‐compiled collections of webpages, comprising 833 bookmarked links in terms of genre, are studied. The purpose of this paper is to find out whether users tend to bookmark certain web genres more than others. Genre theory helps to make sense of the different pages included in these collections, and to classify them, according to their communicative purpose and salient non‐topical features, into blogs, search interfaces, articles, tutorials.Design/methodology/approachA total of 17 participants took part in the research by providing their collections of bookmark links. They were also interviewed about the reasons for bookmarking and to comment on their collections. Relying on the interview results and on the previous literature, the bookmarks were classified into four super‐genres: main or access pages, transactional pages, navigational pages, and content pages.FindingsThe results of the classification into web genres revealed a clear tendency to bookmark main pages, such as homepages, which accounted for 42 per cent of all bookmarked web links. Moreover, some aspects of relevance were highlighted such as the connections to use, time, and context, as well as to the main web activity (browsing or searching).Originality/valuePreviously, bookmarks have mostly been studied as tools for information reuse, but very rarely as sources of implicit relevance feedback. In addition, from the point of view of genre theory, this research shows the importance of relating web genres to users' intentions behind queries.

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“…We are interested in cluster analysis that can be used to organize Web pages into clusters based on their contents or genres (Choi & Yao, 2005). Clustering is an unsupervised discovery process for partitioning a set of data into clusters such that data in the same cluster is more similar to one another than classification methods, cluster analysis has the advantage that it does not require any training data (i.e., the labeled data), but can achieve the same goal in that it can classify similar Web pages into groups.…”

Section: Introductionmentioning

confidence: 99%

Clustering Web Pages into Hierarchical Categories

Yao

Choi

2007

International Journal of Intelligent Information Technologies

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Clustering is well suited for Web mining by automatically organizing Web pages into categories each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain, until now there is no such a method suitable for Web page clustering. To address this problem, we discovered a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page datasets. We also propose a new Bidirectional Hierarchical Clustering algorithm, which arranges individual Web pages into clusters and then arranges the clusters into larger clusters and so on until the average inter-cluster similarity approaches the constant factor. Having the new constant factor together with the new algorithm, we have developed a clustering system suitable for mining the Web.

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Web Page Classification*

Cited by 36 publications

References 93 publications

Unsupervised Keyphrase Extraction for Web Pages

Unsupervised Keyphrase Extraction for Web Pages

Genre analysis of bookmarked webpages

Clustering Web Pages into Hierarchical Categories

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