Yahoo! Inc.

Siemens Medical Solutions Usa, Inc.

Yumao Lu

Fuchun Peng

Nawaaz Ahmed

Marco J Zagha

Xin Li

Benoit F Dumoulin

Hong Shen

Lei Duan

Lin Hong

Xing Wei

Fan Li

A computer-implemented method for processing user entered query data to improve results of a search of pages using a database, when searching the internet, is disclosed. The method includes receiving the user entered query data and parsing each word of the query data and segmenting words using probability to determine a likelihood that the word is for a particular name. And, associating the particular names with a name tag to create one or more tagged name terms. Then, normalizing each of the tagged name terms and the normalizing including boosting information if found in the database and determining proximity between selected ones of the tagged name terms. The method then generates an optimized search query that incorporates normalized terms and operators. The optimized search query being applied to the internet to enable search results to be produced and displayed to the user in response to the entered query data.

Query processing for web search

In one embodiment, access a search query comprising one or more query words, at least one of the query words representing one or more query concepts; access a network document identified for a search query by a search engine, the network document comprising one or more document words, at least one of the document words representing one or more document concepts; semantic-text match the search query and the network document to determine one or more negative semantic-text matches; and construct one or more negative features based on the negative semantic-text matches.

Semantic and text matching techniques for network search

Techniques for determining when and how to transform words in a query to its plural or non-plural form in order to provide the most relevant search results while minimizing computational overhead are provided. A dictionary is generated based upon the words used in a specified number of previous most frequent search queries and comprises lists of transformations from plural to singular and singular to plural. Unnecessary transformations are removed from the dictionary based upon language modeling. The word to transform is determined by finding the last non-stop re-writable word of the query. The context of the transformed word is confirmed in the search documents and a version of the query is executed using both the original form of the word and the transformation of the word.

Word pluralization handling in query for web search

A computer-implemented method for processing user entered query data to improve results of a search of pages using a local search database, when searching the internet, is disclosed. The method includes receiving the user entered query data and parsing each word of the query data and segmenting words using a probabilistic dictionary to determine a likelihood that the word is for a particular name. And, associating the particular names with a name tag to create one or more tagged name terms. Then, normalizing each of the tagged name terms and the normalizing including boosting information if found in the local search database and determining proximity between selected ones of the tagged name terms. The method then generates an optimized search query that incorporates normalized terms and operators. The optimized search query being applied to the internet to enable search results to be produced and displayed to the user in response to the entered query data.

Query identification and normalization for web search

Techniques for determining when and how to transform words in a query to return the most relevant search results while minimizing computational overhead are provided. A dictionary is generated based upon words used in a specified number of previous most frequent search queries and comprises lists of transformations that may include variants based upon the stems of words, synonyms, and abbreviation expansions. When a query is received from a user, candidate queries are generated based upon replacing particular words in the query with a transformation of the particular words. Candidate queries are selected that have a high probability of returning relevant results by computing values of the query using language model scoring and translation scoring. The selected candidate queries and the original query are executed to return search results. The search results are displayed to the user with the words in the original query and the transformed words in bold.

Predictive stemming for web search with statistical machine translation models

Computer-implemented methods and systems for processing user entered query data to improve results of a search of pages using a local search database are provided, when searching the internet. The method includes receiving the user entered query data and parsing each word of the query data and examining each word to determine if the word is associated with one of a business name, a city name or a state name. The examining uses probabilistic dictionaries to determine a likelihood that the word is for a particular term or intent. The method further includes normalizing each of the tagged business terms. The normalizing includes boosting information if found in the local search database and determining proximity between selected ones of the tagged terms. Then, generating an optimized internal search query that incorporates constraints and ranking based on at least the boosting information and the determined proximity between the selected tagged terms. The optimized internal search query is applied to the internet to enable search results to be produced and displayed to the user in response to the entered query data.

Local query identification and normalization for web search

To accurately classify a query as navigational, thousands of available features are explored, extracted from major commercial search engine results, user Web search click data, query log, and the whole Web's relational content. To obtain the most useful features for navigational query identification, a three level system is used which integrates feature generation, feature integration, and feature selection in a pipeline. Because feature selection plays a key role in classification methodologies, the best feature selection method is coupled with the best classification approach to achieve the best performance for identifying navigational queries. According to one embodiment, linear Support Vector Machine (SVM) is used to rank features and the top ranked features are fed into a Stochastic Gradient Boosting Tree (SGBT) classification method for identifying whether or not a particular query is a navigational query.

Techniques for navigational query identification

Techniques for rewriting queries submitted to a query engine are provided. A query is submitted by a user and sent to a search mechanism. Based on the query, one or more query suggestions are generated. Features are generated based on the query and the query suggestions. Those features are input to a trained machine learning mechanism that generates a rewrite score. The rewrite score signifies a confidence score that indicates how confident the search mechanism is that the user intended to submit the original query. If the rewrite score is below a certain threshold, then the original query is rewritten to a second query. Results of executing the original query may be sent to the user along with a reference to the second query. Additionally or alternatively, results of executing the second query are sent to the user.

Query rewriting with spell correction suggestions using a generated set of query features

Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image point belonging to a ground glass nodule from the variation range of the background tissue probability distribution function by using a function corresponding to the variation range of the background tissue probability distribution function, and calculating a log likelihood function of the image from the background tissue probability distribution function and the estimated ground glass nodule probability distribution function, wherein the log likelihood function represents the confidence that a point in the image is not part of a ground glass nodule. The log likelihood function value for each point is compared to a confidence value of the background tissue, to determine if the point is a candidate ground glass nodule location.

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San Jose, CA, United States of America

Yumao Lu