Abstract: Techniques for determining similarity between error reports received by an error reporting service. An error report may be compared to other previously-received error reports to determine similarity and facilitate diagnosing and resolving an error that generated the error report. In some implementations, the similarity may be determined by comparing frames included in a callstack of an error report to frames included in callstacks in other error reports to determine an edit distance between the callstacks, which may be based on the number and type of frame differences between callstacks. Each type of change may be weighted differently when determining the edit distance. Additionally or alternatively, the comparison may be performed by comparing a type of error, process names, and/or exception codes for the errors contained in the error reports. The similarity may be expressed as a probability that two error reports were generated as a result of a same error.

Abstract: Techniques for determining similarity between error reports received by an error reporting service. An error report may be compared to other previously-received error reports to determine similarity and facilitate diagnosing and resolving an error that generated the error report. In some implementations, the similarity may be determined by comparing frames included in a callstack of an error report to frames included in callstacks in other error reports to determine an edit distance between the callstacks, which may be based on the number and type of frame differences between callstacks. Each type of change may be weighted differently when determining the edit distance. Additionally or alternatively, the comparison may be performed by comparing a type of error, process names, and/or exception codes for the errors contained in the error reports. The similarity may be expressed as a probability that two error reports were generated as a result of a same error.

Abstract: The present disclosure is directed to systems and methods for determining semantically related terms based on sequences of search queries. Generally, a semantically related term tool examines search logs to associate search queries with a user submitting the search query. The semantically related term tool establishes a plurality of sequences of search queries, each sequence of search queries comprising one or more search queries associated with a common user and relating to a common concept. The semantically related term tool receives one or more seed terms and determines one or more terms related to the received seed terms based on the established plurality of sequences of search queries.

Abstract: The present invention relates to systems and methods for identifying one or more queries related to a given query. The method of the present invention comprises receiving a query written according to one or more writing systems of a language with multiple writing systems. A candidate set of queries written according to one or more writing systems of the language with multiple writing systems is identified. A score is calculated for the one or more queries in the candidate set indicating the similarity of the one or more queries with respect to the query received.

Abstract: Systems and methods for determining semantically related terms are disclosed. Generally, a semantically related term tool trains a model to predict a degree of relevance between a candidate term and one or more seed terms. The model may be trained based on data such as a plurality of seed sets, a plurality of semantically related term sets, and a plurality of modular optimized dynamic sets (“MODS”), where each semantically related term set is related to a seed set of the plurality of seed sets and each MODS is related to a seed set of the plurality of seed sets. The semantically related term tool then determines a plurality of terms that are semantically related to one or more terms in a new seed set based on the model, the one or more terms in the seed set, and a plurality of candidate terms.

Abstract: Systems and methods for determining semantically related terms are disclosed. Generally, a semantically related term tool receives a seed set and identifies a plurality of terms that constitute the seed set. For each term of the seed set, the semantically related term tool identifies one or more concept terms associated with terms of the seed set other than the term being processed, determines a plurality of concept terms based on at least one of combinations and permutations of the concept terms associated with terms of the seed set other than the term being processed, and adds the resulting terms to a plurality of semantically related terms.

Abstract: Systems and methods for determining semantically related terms are disclosed. Generally, a semantically related term tool receives a seed set and identifies a plurality of terms that constitute the seed set. For each term of the seed set, the semantically related term tool identifies concept terms associated with terms of the seed set other than the term being processed, joins the term being processed with each of the identified concept terms, and adds the resulting terms to a plurality of semantically related terms. The semantically related term tool removes invalid terms from the plurality of semantically related terms based on a language model and ranks at least a portion of the remaining terms of the plurality of semantically related terms based on a metric indicating a degree of semantical relationship between a term of the plurality of semantically related terms and one or more terms of the set seed.

Abstract: The present disclosure is directed to systems and methods for determining semantically related terms based on sequences of search queries. Generally, a semantically related term tool examines search logs to associate search queries with a user submitting the search query. The semantically related term tool establishes a plurality of sequences of search queries, each sequence of search queries comprising one or more search queries associated with a common user and relating to a common concept. The semantically related term tool receives one or more seed terms and determines one or more terms related to the received seed terms based on the established plurality of sequences of search queries.

Abstract: Systems and methods for predicting a proper casing variation of a term are disclosed. Generally, a term casing suggestion tool receives a term. The term casing suggestion tool determines whether a database of editorial decisions includes a proper casing variation of the received term. If the database of editorial decisions includes a proper casing variation, the term casing suggestion tool may suggest the proper casing variation to an editor or export the proper casing variation to a system of an online advertisement service provider. If the database of editorial decisions does not include a proper casing variation, one or more digital sources are searched for the term. A set of casting variations of the term is recorded a number of times each casing variation occurs in the search is recorded. At least a portion of the set of casing variations of the term is suggested to an editor.

Abstract: Systems and methods for predicting a displayable form of a term is disclosed. Generally, a displayable form suggestion tool receives a term. The displayable form suggestion tool canonicalizes the term and applies a displayable form model based on search logs of an online advertisement service provider to the received term to determine a set of potential displayable forms of the term. The set of potential displayable forms of the term are suggested to an editor and the editor selects one of the suggested displayable forms of the term. The displayable form suggestion tool may then export the selected displayable form of the term to a system of the online advertisement service provider such as an advertisement campaign management system for insertion into a digital ad such as a graphical banner ad or a sponsored search listing.

Abstract: The present invention relates to systems and methods for identifying one or more queries related to a given query. The method of the present invention comprises receiving a query written according to one or more writing systems of a language with multiple writing systems. A candidate set of queries written according to one or more writing systems of the language with multiple writing systems is identified. A score is calculated for the one or more queries in the candidate set indicating the similarity of the one or more queries with respect to the query received.

Abstract: Methods and systems for determining semantically related terms are disclosed. Generally, seed terms are received from a user. One or more potential terms semantically related to the seed terms are determined based on vectors comprising entries regarding a plurality of terms, a plurality of universal resource locators (“URLs”) associated with each term of the plurality of terms, and for each URL in a search log, a number of times that one or more users searched for each of the terms in the search log and clicked on the URL. At least a portion of the potential terms is then suggested to the user.