Abstract: Various technologies described herein pertain to suggesting context dependent keywords for advertising. A set of seed queries can be identified from a context, where the context is a source keyword, a search query, a category, or a landing page. Moreover, the set of seed queries can be inputted to a search engine. A predetermined number of web pages returned by the search engine upon executing the set of seed queries can be retrieved. Candidate keywords can be extracted from the web pages returned by the search engine. Further, keywords from the candidate keywords can be selected from the candidate keywords based on relevance scores of the candidate keywords.

Abstract: Browsing sequence phrase identification technique embodiments are presented that generally extract topically-related phrases from the pages visited by a user in a browsing session. The topically-related phrases can be used for a variety of purposes, including aiding a user in re-finding previously visited sites. This phrase identification task is performed by considering not just the pages of a user's browsing sequence individually, but also pages visited immediately before and immediately after each page. In this way, phrases found in a page can be analyzed in the context in which the page was viewed, rather than in isolation. The identified phrases are further filtered by picking those that appear on a pre-populated topic list, and then clustering to find the most informative ones.

Abstract: Keyword extraction technique embodiments are presented which extract topically related keywords from a set of topically related documents. In one general embodiment, this keyword extraction involves first accessing a set of topically related documents. A number of candidate keywords are then identified from the set of related documents. A weighted keyword candidate-document matrix is formed using these candidate keywords, and it is partitioned into multiple groups of keyword candidates. Dense clusters of keyword candidates whose density exceeds a prescribed density threshold are then identified in each of the groups of keyword candidates. Finally, the keyword candidates associated with each dense cluster are designated as topically related keywords.

Abstract: Various technologies described herein pertain to suggesting context dependent keywords for advertising. A set of seed queries can be identified from a context, where the context is a source keyword, a search query, a category, or a landing page. Moreover, the set of seed queries can be inputted to a search engine. A predetermined number of web pages returned by the search engine upon executing the set of seed queries can be retrieved. Candidate keywords can be extracted from the web pages returned by the search engine. Further, keywords from the candidate keywords can be selected from the candidate keywords based on relevance scores of the candidate keywords.

Abstract: Browsing sequence phrase identification technique embodiments are presented that generally extract topically-related phrases from the pages visited by a user in a browsing session. The topically-related phrases can be used for a variety of purposes, including aiding a user in re-finding previously visited sites. This phrase identification task is performed by considering not just the pages of a user's browsing sequence individually, but also pages visited immediately before and immediately after each page. In this way, phrases found in a page can be analyzed in the context in which the page was viewed, rather than in isolation. The identified phrases are further filtered by picking those that appear on a pre-populated topic list, and then clustering to find the most informative ones.

Abstract: Keyword extraction technique embodiments are presented which extract topically related keywords from a set of topically related documents. In one general embodiment, this keyword extraction involves first accessing a set of topically related documents. A number of candidate keywords are then identified from the set of related documents. A weighted keyword candidate-document matrix is formed using these candidate keywords, and it is partitioned into multiple groups of keyword candidates. Dense clusters of keyword candidates whose density exceeds a prescribed density threshold are then identified in each of the groups of keyword candidates. Finally, the keyword candidates associated with each dense cluster are designated as topically related keywords.