For discovering the new URI of a missing web page, lexical signatures, which consist of a small number of words chosen to represent the "aboutness" of a page, have been previously proposed. However, prior methods relied on computing the lexical signature before the page was lost, or using cached or archived versions of the page to calculate a lexical signature. We demonstrate a system of constructing a lexical signature for a page from its link neighborhood, that is the "backlinks", or pages that link to the missing page. After testing various methods, we show that one can construct a lexical signature for a missing web page using only ten backlink pages. Further, we show that only the first level of backlinks are useful in this effort. The text that the backlinks use to point to the missing page is used as input for the creation of a four-word lexical signature. That lexical signature is shown to successfully find the target URI in over half of the test cases.
@cite_12 explored the correlation between anchor text and page titles. They showed that the collective nature of anchor texts, since they are created by many people, adds a significant value. Anchor texts are created by a similar thought process to queries, and as such will use similar words to describe a topic. Since links can be made by many authors, they will use their own word preferences, which means that anchors can provide synonyms that the original page's author might not use. They even showed that anchor texts, since they are made by different authors and might be written in different languages, might be used to provide a bilingual corpus for machine learning of natural language translation.
@cite_12
155984473
In the Navigational Retrieval Subtask 2 (Navi-2) at the NTCIR-5 WEB Task, a hypothetical user knows a specific item (e.g., a product, company, and person) and requires to find one or more representative Web pages related to the item. This paper describes our system participated in the Navi-2 subtask and reports the evaluation results of our system. Our system uses three types of information obtained from the NTCIR5 Web collection: page content, anchor text, and link structure. Specifically, we exploit anchor text in two perspectives. First, we compare the effectiveness of two different methods to model anchor text. Second, we use anchor text to extract synonyms for query expansion purposes. We show the effectiveness of our system experimentally.