Abstract: The disclosed embodiments illustrate methods and systems for detecting personal life events of users. The method includes training classifiers based on a set of features extracted from each of an annotated first set of social media data. The first set of social media is associated with one or more first categories. Further, the first set of social media data are annotated by one or more crowdworkers based on one or more second categories. The method further includes extracting a second set of social media data of one or more users, associated with the one or more first categories, from the one or more social media platforms. The method further includes categorizing the extracted second set of social media data into the one or more second categories by use of the trained classifiers. The categorization is further utilized to detect the personal life events of the one or more users.

Abstract: In an example embodiment, a machine learning algorithm is used to train a query-based deep semantic similarity neural network to output a query context vector in a vector space that includes both query context vectors and document context vectors. Both the query context vectors and document context vectors are clustered using a clustering algorithm. When an input search query is obtained, the input search query is also passed into the query-based deep semantic similarity neural network and its output document context vector assigned to a first cluster based on the clustering algorithm. Documents within the first cluster are then retrieved in response to the input search query.

Abstract: In an example embodiment, a machine learning algorithm is used to train a. deep semantic similarity neural network to output a semantic similarity score between a candidate job search query and a candidate job search result. This semantic similarity score can then be used in a ranking phase to rank job search results in response to a first job search query.

Abstract: Method and system to generate multiple entity aware typeahead suggestions is provided. The system is configured to use multiple Finite State Transducers (FSTs) to examine an input string submitted by a user via a search box, and to generate one or more typeahead suggestions based on the results of the examination. Different FSTs are constructed with respect to strings identified as associated with different entity types. At least one of the typeahead suggestions includes a portion associated with one entity type and a portion associated with a different entity type.

Abstract: Method and system to generate index aware typeahead suggestions is provided. The system is configured to generate one or more typeahead suggestions that are index aware, by taking into account the number of valid search results that match a query that corresponds to a typeahead suggestion. The system detects an input string in the search box, generates a candidate typeahead suggestion string, interrogates an index of the electronic publications with the candidate typeahead suggestion string to generate a recall value that represents a number of electronic publications that include the candidate typeahead suggestion string, and includes the candidate typeahead suggestion string in a list of typeahead suggestions based on the recall value. The list of typeahead suggestions is communicated to a client system.

Abstract: The disclosed embodiments illustrate methods and systems for processing social media data for content recommendation to a user. The method includes extracting a set of entity data from the social media data of the user. The method further includes extracting semantic data of each entity data in the extracted set of entity data from one or more knowledge databases over a communication network. The method further includes generating a user-interest vector of the user. The user-interest vector of the user is generated based on at least a mapping of the extracted semantic data of each entity data with one or more leaf nodes in an interest taxonomy. The generated user-interest vector is further utilized for recommending targeted content to the user.

Abstract: The present disclosure discloses methods and systems for identifying a target profile of a source user on a target social network, based on a corresponding source profile at a source social network. The method includes extracting one or more matching profiles from the target social network, based on one or more static profile features of the source profile, determining one or more dynamic profile features of the source profile and each matching profile, based on real-time user activities on the source and target social networks, and identifying the target profile from the one or more matching profiles, based on a comparison of the one or more dynamic profile features of the source profile with corresponding one or more features of the one or more matching profiles.

Abstract: A method and a system are provided for correlation detection in multiple spatio-temporal datasets for event sensing in a geographical area. The method includes extracting datasets, comprising information about one or more events, from one or more data sources. The method further includes identifying a primary data source and secondary data sources from the one or more data sources. The method further includes extracting primary features from the datasets associated with the primary data source and secondary features from the datasets associated with the secondary data sources. The primary features are categorized into one or more categories. The method further includes training classifiers based on the primary features and/or the one or more categories. The method further includes detecting a correlation among the information associated with the one or more events based on a category transfer distribution from the primary data source to the secondary data sources.

Abstract: The disclosed embodiments illustrate methods and systems for detecting personal life events of users. The method includes training classifiers based on a set of features extracted from each of an annotated first set of social media data. The first set of social media is associated with one or more first categories. Further, the first set of social media data are annotated by one or more crowdworkers based on one or more second categories. The method further includes extracting a second set of social media data of one or more users, associated with the one or more first categories, from the one or more social media platforms. The method further includes categorizing the extracted second set of social media data into the one or more second categories by use of the trained classifiers. The categorization is further utilized to detect the personal life events of the one or more users.

Abstract: Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model.

Abstract: The technical solution under the present disclosure automatically analyzes conversations between users by receiving a training dataset having a text sequence including sentences of a conversation between the users; extracting feature(s) from the training dataset based on features; providing equation(s) for a plurality of tasks, the equation(s) being a mathematical function for calculating value of a parameter for each of the tasks based on the extracted feature; determining value of the parameter for tasks by processing the equation(s); assigning label(s) to each of the sentences based on the determined value of the parameter, a first label being selected from a plurality of first labels, and a second label being selected from a number of second labels; and storing and maintaining with the database a pre-defined value of the parameter, first labels, conversations, second labels, a test dataset, equation(s), and pre-defined features.

Abstract: A method for assigning a topic to a collection of microblog posts may include, by an acquisition module, receiving from at least one messaging service server, a plurality of posts, wherein each of the plurality of posts comprise post content; by a generation module, analyzing the posts and extract, from at least one of the posts, a link with an address to an external document; and, by the acquisition module, accessing the external document that is associated with the address and fetch external content associated with the document. The method may also include by the generation module: analyzing the post content to identify at least one label for each post, for each post that includes a link, analyzing the external content to identify a topic, and using a topic modeling technique to generate a trained topic model comprising a plurality of topics and a plurality of associated words.

Abstract: The disclosed embodiments illustrate methods and systems for searching for a first user. The one or more inputs pertaining to one or more first attributes of the first user are received. Further, the one or more first attributes of the first user are ranked based on at least a presence of the one or more first attributes among one or more second attributes pertaining to one or more second users. Thereafter, one or more search strings comprising at least one attribute selected from the ranked one or more first attributes are generated, wherein the one or more search strings are utilizable to search for the first user. Finally, a list of third users is obtained from one or more search engines in response to the one or more search strings.

Abstract: The disclosed embodiments illustrate methods and systems for identifying a set of users for a marketing campaign. The method includes retrieving one or more first keywords from one or more messages shared by one or more first users, or from a user profile of each of one or more first users. The one or more first keywords are indicative of one or more events associated with one or more first users, and one or more intents of said one or more first users. The method further includes receiving one or more second keywords, pertaining to marketing campaign, from a computing device of a second user. Thereafter, the method includes identifying said set of users from said one or more first users based on a correlation between said one or more first keywords and said one or more second keywords. The method is performed by one or more microprocessors.

Abstract: Systems and methods of data analytics, which in various embodiments enable business analysts to apply certain machine learning and analytics algorithms in a self-service manner by binding them to generic business questions that they can be used to answer in particular domains. The general approach may be to define the application of an algorithm to solve specific problems (questions) for particular combinations of a business domain and a data category. At design time, the algorithm may be linked to canonical data within a data category and programmed to run with this canonical data set. At runtime, given a dataset and its category, and a business domain, a user may choose from the corresponding questions and the system may run the algorithm bound to that question.

Abstract: The technical solution under the present disclosure automatically analyzes conversations between users by receiving a training dataset having a text sequence including sentences of a conversation between the users; extracting feature(s) from the training dataset based on features; providing equation(s) for a plurality of tasks, the equation(s) being a mathematical function for calculating value of a parameter for each of the tasks based on the extracted feature; determining value of the parameter for tasks by processing the equation(s); assigning label(s) to each of the sentences based on the determined value of the parameter, a first label being selected from a plurality of first labels, and a second label being selected from a number of second labels; and storing and maintaining with the database a pre-defined value of the parameter, first labels, conversations, second labels, a test dataset, equation(s), and pre-defined features.

Abstract: Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model.

Abstract: A process discovery system that includes an offline system training module configured to cluster similar process log traces using Non-negative Matrix Factorization (NMF) with each cluster representing a process model, and learn a Conditional Random Field (CRF) model for each process model and an online system usage module configured to decode new incoming log traces and construct a process graph in which transitions are shown or hidden according to a tuning parameter.

Abstract: A method, non-transitory computer readable medium, and apparatus for extracting text from a social media document are disclosed. For example, the method indexes a plurality of social media documents into a plurality of snippets, receives a query including one or more keywords and a purpose, identifies one or more of the plurality of snippets that include the one or more keywords in an index, ranks the one or more of the plurality of snippets in accordance with the purpose and provides the one or more plurality of snippets that are ranked in accordance with the purpose.

Abstract: A process discovery system that includes an offline system training module configured to cluster similar process log traces using Non-negative Matrix Factorization (NMF) with each cluster representing a process model, and learn a Conditional Random Field (CRF) model for each process model and an online system usage module configured to decode new incoming log traces and construct a process graph in which transitions are shown or hidden according to a tuning parameter.