Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Automatic resource compilation by analyzing hyperlink structure and associated text
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The shark-search algorithm. An application: tailored Web site mapping
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Accelerated focused crawling through online relevance feedback
Proceedings of the 11th international conference on World Wide Web
Using web structure for classifying and describing web pages
Proceedings of the 11th international conference on World Wide Web
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Deriving link-context from HTML tag tree
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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Link context has been exploited extensively ever since the advent of the World Wide Web, but the approach to extracting precise link context has not been fully explored and many state-of-the-art extraction methods are based on simplistic heuristics and require ad-hoc parameters. In this paper, we propose a novel two-step extraction model, which aims to systematically derive link context of quality as high as anchor text. In the macroscopic analysis step, a systematic web page structure analysis is performed to locate the content cohesive text region and potential relevant header or header like tags. In the microscopic extraction step, an English parser is used to extract the relevant sentence fragments in the text region and the nearest heading text is encompassed if the need arises. Preliminary experimental results proved our approach's effectiveness.