Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

A system for retrieval of text includes a processor which identifies grammar rules associated with text fragments of a text string that is retrieved from an associated storage medium, and retrieves text strings from the storage medium which satisfy the grammar rules. A display displays retrieved text strings. A user input device in communication with the processor enables a user to select text fragments of the displayed text strings for generating a query. The processor identifies grammar rules associated with the user-selected text fragments and retrieves text strings from the storage medium which satisfy the grammar rules.

Linguistic user interface

A system and method for automatically analyzing and characterizing Internet search results and annotating a search results page according to specific characteristics of each webpage located at a URL corresponding to a search result. Such characteristics include the composition of each search results webpage as well as which search term elements are present in a webpage located at a URL corresponding to a search result out of those search term elements that were submitted to a search engine to produce the search results webpage. Further, search results are annotated to indicate which search term elements are present in a descendent webpage of a webpage located at a URL corresponding to a search result. Search results may also be optionally filtered according to specific characteristics of a webpage located at a search results URL such that certain categories of webpage are excluded from being referenced in the displayed search results.

Automatic webpage characterization and search results annotation

Mobile communication device display with graphical user interface comprising a dashboard

A query phrase may be automatically classified to one or more topics of interest (e.g., categories) to assist in routing the query phrase to one or more appropriate backend databases. A selectional preference query classification technique may be used to classify the query phrase based on a comparison between the query phrase and patterns of query phrases. Additionally, or alternatively, a combination of query classification techniques may be used to classify the query phrase. Topical classification of a query phrase also may be used to assist a search system in delivering auxiliary information to a user who entered the query phrase. Advertisements, for instance, may be tailored based on classification rather than query keywords.

Web query classification

A stopword detection component detects stopwords (also stop-phrases) in search queries input to keyword-based information retrieval systems. Potential stopwords are initially identified by comparing the terms in the search query to a list of known stopwords. Context data is then retrieved based on the search query and the identified stopwords. In one implementation, the context data includes documents retrieved from a document index. In another implementation, the context data includes categories relevant to the search query. Sets of retrieved context data are compared to one another to determine if they are substantially similar. If the sets of context data are substantially similar, this fact may be used to infer that the removal of the potential stopword(s) is not material to the search. If the sets of context data are not substantially similar, the potential stopword can be considered material to the search and should not be removed from the query.

Locating meaningful stopwords or stop-phrases in keyword-based retrieval systems

Generating context-based spell corrections of entity names

Graphical user interface

Grapical user interface for a display screen of a communications terminal

One embodiment of the present invention provides a system that selectively deletes clusters of conceptually-related words from a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and contains one or more cluster nodes representing clusters of conceptually related words. Nodes in the current model are coupled together by weighted links, so that if an incoming link from a node that has fired causes a cluster node to fire with a probability proportionate to a weight of the incoming link, an outgoing link from the cluster node to another node causes the other node to fire with a probability proportionate to the weight of the outgoing link. Next, the system processes a given cluster node in the current model for possible deletion. This involves determining a number of outgoing links from the given cluster node to terminal nodes or cluster nodes in the current model. If the determined number of outgoing links is less than a minimum value, or if the frequency with which the given cluster node fires is less than a minimum frequency, the system deletes the given cluster node from the current model.

Selectively deleting clusters of conceptually related words from a generative model for text

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