Yahoo! Inc.

Emre Velipasaoglu

Deepayan Chakrabarti

Srinivas Vadrevu

Swapnil Hajela

Lei Duan

Fan Li

Manav Ratan Mital

Larry Lai

David (Ciemo) Ciemiewicz

Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems. In an embodiment, an index of segmented portions may be used by a search engine to respond to a search query. In an embodiment, one or more machine learned models may be used to identify one or more feature properties of a plurality of segmented portions within one or more files, or otherwise inferable from the one or more files. In an embodiment, one or more machine learned models may be used to classify one or more of a plurality of segmented portions as being at least one of a plurality of segment types.

Automatic classification of segmented portions of web pages

Exemplary techniques are provided which may be implemented using various methods, apparatuses, and/or articles of manufacture to provide or otherwise support segment sensitive query matching based on segmented portions of web pages and/or providing related information for use in information extraction and/or information retrieval systems. In certain example implementations techniques may be provided for determining whether a query match exists between a document and obtained query terms based, at least in part, on labeled portion information associated with a plurality of segmented portions of a document.

Segment sensitive query matching

Example systems, methods, apparatuses, or articles of manufacture, etc. are disclosed in connection with assisted search results.

Assisted searching

Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems.

To provide valuable information regarding a webpage, the webpage must be divided into distinct semantically coherent segments for analysis. A set of heuristics allow a segmentation algorithm to identify an optimal number of segments for a given webpage or any portion thereof more accurately. A first heuristic estimates the optimal number of segments for any given webpage or portion thereof. A second heuristic coalesces segments where the number of segments identified far exceeds the optimal number recommended. A third heuristic coalesces segments corresponding to a portion of a webpage with much unused whitespace and little content. A fourth heuristic coalesces segments of nodes that have a recommended number of segments below a certain threshold into segments of other nodes. A fifth heuristic recursively analyzes and splits segments that correspond to webpage portions surpassing a certain threshold portion size.

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San Francisco, CA, United States of America

Emre Velipasaoglu