Abstract: A named entity input is received and a target sense for which the named entity input is to be extracted from a set of documents is identified. An extraction complexity feature is generated based on the named entity input, the target sense, and the set of documents. The extraction complexity feature indicates how difficult or complex it is deemed to be to identify the named entity input for the target sense in the set of documents.

Abstract: A method, system, and medium are provided for enhancing a search result snippet that is associated with a URL. Potentially descriptive terms of the URL are identified by reconciling multiple sources of terms related to the URL. One aspect includes comparing search terms of historically relevant queries to anchor text for the URL. The search result snippet might be intelligently enhanced by causing common terms to be visibly distinguished when the snippet is displayed.

Abstract: A named entity input is received and a target sense for which the named entity input is to be extracted from a set of documents is identified. An extraction complexity feature is generated based on the named entity input, the target sense, and the set of documents. The extraction complexity feature indicates how difficult or complex it is deemed to be to identify the named entity input for the target sense in the set of documents.

Abstract: A named entity input is received and a target sense for which the named entity input is to be extracted from a set of documents is identified. An extraction complexity feature is generated based on the named entity input, the target sense, and the set of documents. The extraction complexity feature indicates how difficult or complex it is deemed to be to identify the named entity input for the target sense in the set of documents.

Abstract: A named entity input is received and a target sense for which the named entity input is to be extracted from a set of documents is identified. An extraction complexity feature is generated based on the named entity input, the target sense, and the set of documents. The extraction complexity feature indicates how difficult or complex it is deemed to be to identify the named entity input for the target sense in the set of documents.

Abstract: Certain types of entities may be extracted from a document. In one example, the entities to be recognized are cultural entities, such as the names of movies, video games, books, etc. For each such entity, a concept graph may be built that shows the relationship between the entity itself and other entities, such as the relationship between a movie and the actor(s) who act in the movie. When a candidate entity name is detected in the document, the concept graph may be used to look for other entities that appear in the context of the candidate entity. The presence of related entities in the context of the candidate may be used to disambiguate the meaning of the candidate. For example, a common word like “up” might be recognized as the name of a movie if the names of actors or characters in that movie appear near the word “up”.

Abstract: A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

Abstract: A method, system, and medium are provided for enhancing a search result snippet that is associated with a URL. Potentially descriptive terms of the URL are identified by reconciling multiple sources of terms related to the URL. One aspect includes comparing search terms of historically relevant queries to anchor text for the URL. The search result snippet might be intelligently enhanced by causing common terms to be visibly distinguished when the snippet is displayed.

Abstract: A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).