Abstract: A passage sentiment classifier that can be used to assign a score that indicates the polarity and magnitude of sentiment expressed by a piece of text using information about similar passages. A passage of text may be a few words, a sentence, a paragraph or an entire document. The invention described classifies automatically passages by first looking up the most similarly classified passage in a storage system, which contains passages that have been classified manually by a human.

Abstract: A system for and method for providing pages, more specifically a system includes a server configured to receive a query and an engine that is able to retrieve a set of pages by first retrieving a partition that contains them.

Abstract: Systems and methods for predicting a target page associated with a search query are disclosed. Generally, a predictive user click model module defines a set of sessions and builds a model to predict whether a webpage is a target page associated with a search query based on a number of times, over the set of sessions, that a user as defined in the session clicks within the same session on a given webpage associated with a given search query versus a number of sessions in the set of sessions.

Abstract: A passage sentiment classifier that can be used to assign a score that indicates the polarity and magnitude of sentiment expressed by a piece of text using information about similar passages. A passage of text may be a few words, a sentence, a paragraph or an entire document. The invention described classifies automatically passages by first looking up the most similarly classified passage in a storage system, which contains passages that have been classified manually by a human.

Abstract: A system for and method for providing pages, more specifically a system includes a server configured to receive a query and an engine that is able to retrieve a set of pages by first retrieving a partition that contains them.

Abstract: An automated technique for tagging documents includes using a semantic tagger to generate an annotation that associates a standard tag with a first text fragment of the user-defined document, wherein the tagger is trained on a standard document annotated with a standard tag, associating the first user-defined tag with a second text fragment of the user-defined document in response to the second text fragment matching a value associated with the first user-defined tag, and establishing a mapping between the standard tag and the first user-defined tag in response to existence of a requisite correlation between the standard tag and the user-defined tag. The technique may further include selecting from the user-defined document a tagged text fragment that is associated with a second user-defined tag, and providing the tagged text fragment and a standard tag associated by the mapping with the second user-defined tag to the tagger as additional training input.

Abstract: Methods of providing a document relevance score to a document on a network are disclosed. Computer readable medium having stored thereon computer-executable instructions for performing a method of providing a document relevance score to a document on a network are also disclosed. Further, computing systems containing at least one application module, wherein the at least one application module comprises application code for performing methods of providing a document relevance score to a document on a network are disclosed.

Abstract: Search results of a search query on a network are ranked according to an additional click distance property associated with each of the documents on the network. The click distance is measurement of the number clicks or user navigations from a page or pages on the network designated as highest authority or root pages on the network. The precision of the results is increased by the addition of the click distance term when the site or intranet where the search query takes place is hierarchically structured.

Abstract: Updated queries are maintained in a cache. A search engine receives a query from a user through a query entry field. The search engine determines search results corresponding to the user query. A new entry mapping the user query to the search results is generated in a cache of results. A web crawler retrieves a new batch of documents for a particular document collection. A search index associated with a search engine is updated to reflect new documents in the document collection. A search engine of queries receives documents from the new batch of documents as inputs. Based on the received documents, the search engine of queries determines which of the queries would have returned the documents as relevant in a search. These queries are determined to be stale and invalidated.

Abstract: Techniques for generating features that are used to rank documents in a search results page are provided. A query is received and may be modified before being compared to queries in a query log of previously-issued queries. The comparisons may be made in a variety of ways. The comparisons may allow query terms to be ordered and terms to be inserted. Relevance features are generated from the results of the comparisons. The documents that are referenced in a search results page (generated as a result of the query) are ranked based on the generated relevance features.

Abstract: Search results of a search query on a network are ranked according to an additional click distance property associated with each of the documents on the network. The click distance is measurement of the number clicks or user navigations from a page or pages on the network designated as highest authority or root pages on the network. The precision of the results is increased by the addition of the click distance term when the site or intranet where the search query takes place is hierarchically structured.

Abstract: Search results of a search query on a network are ranked according to an additional ranking function for the prior probability of relevance of a document based on document property. The ranking function can be adjusted based on a comparison of the language that a document is written in and the language that is associated with a search query. Both query-independent values and query-dependent values can be used to rank the document.

Abstract: Methods of providing a document relevance score to a document on a network are disclosed. Computer readable medium having stored thereon computer-executable instructions for performing a method of providing a document relevance score to a document on a network are also disclosed. Further, computing systems containing at least one application module, wherein the at least one application module comprises application code for performing methods of providing a document relevance score to a document on a network are disclosed.

Abstract: Search results of a search query on a network are ranked according to an additional click distance property associated with each of the documents on the network. The click distance is measurement of the number clicks or user navigations from a page or pages on the network designated as highest authority or root pages on the network. The precision of the results is increased by the addition of the click distance term when the site or intranet where the search query takes place is hierarchically structured.

Abstract: Embodiments of methods, apparatuses, devices and systems associated with updating stored internet search query result values are disclosed.

Abstract: Search results of a search query on a network are ranked according to a scoring function that incorporates anchor text as a term. The scoring function is adjusted so that a target document of anchor text reflect the use of terms in the anchor text in the target document's ranking. Initially, the properties associated with the anchor text are collected during a crawl of the network. A separate index is generated that includes an inverted list of the documents and the terms in the anchor text. The index is then consulted in response to a query to calculate a document's score. The score is then used to rank the documents and produce the query results.

Abstract: Methods and apparatus are described for facilitating discovery of information of interest in a document collection. A document model is proposed in which important terms and their semantic roles are represented. This document model is then used to facilitate searching and/or browsing of the document collection.

Abstract: An automated technique for tagging documents includes using a semantic tagger to generate an annotation that associates a standard tag with a first text fragment of the user-defined document, wherein the tagger is trained on a standard document annotated with a standard tag, associating the first user-defined tag with a second text fragment of the user-defined document in response to the second text fragment matching a value associated with the first user-defined tag, and establishing a mapping between the standard tag and the first user-defined tag in response to existence of a requisite correlation between the standard tag and the user-defined tag. The technique may further include selecting from the user-defined document a tagged text fragment that is associated with a second user-defined tag, and providing the tagged text fragment and a standard tag associated by the mapping with the second user-defined tag to the tagger as additional training input.

Abstract: Methods of providing a document relevance score to a document on a network are disclosed. Computer readable medium having stored thereon computer-executable instructions for performing a method of providing a document relevance score to a document on a network are also disclosed. Further, computing systems containing at least one application module, wherein the at least one application module comprises application code for performing methods of providing a document relevance score to a document on a network are disclosed.

Abstract: A field-weighted search combines statistical information for each term across document fields in a suitably weighted fashion. Both field-specific term frequencies and field and document lengths are considered to obtain a field-weighted document weight for each query term. Each field-weighted document weight can then be combined in order to generate a field-weighted document score that is responsive to the overall query.