The present disclosure provides a question answering method and apparatus for query information. The method may include: receiving query information input by a user, and analyzing a query target comprised in the query information; recalling candidate answers from a pre-generated knowledge graph based on the query target, where the knowledge graph is constructed based on inherent data in a map database and dynamic data of historical users, and the dynamic data includes at least one of comment data, search data, or spatiotemporal big data; and returning, in response to that there is a target answer whose matching degree with the query target exceeds a preset threshold in the candidate answers, the target answer to the user.

A system and a method creating an intelligent memory and providing contextual intelligent recommendations is provided. The invention provides extracting electronic communications data associated with active user data. Further, the invention provides performing a keyword tagging operation on conversation data present in the extracted electronic communications data based on a pre-generated keywords map. The invention provides generating a multi-relational model representative of conversation data associated with the electronic communications data in the form of graph nodes based on the keywords stored as the first tag and the second tag. The invention provides transmitting one or more electronic Recommendation Action Communication (RAC) with embedded application program interface calls based on the multi-relational model, the embedded application program interface calls enabling actions to be taken on information units via a single click.

A mechanism is provided in a data processing system to implement a cognitive natural language processing (NLP) system with descriptor uniqueness identification to support named entity mention clustering. The mechanism annotates a set of documents from a corpus of documents for entity types and mentions, collects descriptor usages from all documents in the corpus of documents, analyzes the descriptor usages to classify the descriptors as base terms or modifier terms, generates compatibility scores for the descriptors, and performs entity merging of entity clusters based on the compatibility scores.

A search interface is displayed in a table format that includes one or more columns, each column including data items of an event attribute, the data items being of a set of events, and a plurality of rows forming cells with the one or more columns, each cell including one or more of the data items of the event attribute of a corresponding column. Based on a user selecting one or more of the cells, a list of options if displayed corresponding to the selection, and one or more commands are added to a search query that corresponds to the set of events, the one or more commands being based on at least an option that is selected from the list of options and the event attribute for each of the one or more of the data items of each of the selected one or more cells.

The automated collection of online data is enhanced by generating and saving a context between a document and a related named entity, as well as a credibility level of the online source. The context, credibility level, and quality and quantity of collected data are used to enhance the use of the collected data in automated decision-making. Both the quality and the quantity may be continuously updated and honed through machine learning. Three new algorithms—DUPES, CORRAL, and ONTO—have been introduced to support the above, improving current state-of-the-art engineering practice by sharpening the strategy for named-entity searching, for ensuring that topic modeling produces relevant topic tags, and for handling sentiment which may be NEGATIVE, POSITIVE, and NEUTRAL (which includes MISSING and INCONCLUSIVE).

In various example embodiments, a system and method for computing a query feature score that can be used by a machine learning algorithm to rank search results is described. A query is received. The assigned probabilities for the arbitrary query is retrieved. The assigned probabilities is based on a probability model for a query feature. A score is computed for the query feature based on the assigned probabilities for the arbitrary query.

A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.

Introduced here are various embodiments for selectively assigning a query to an expert. A network-accessible server system may receive a query from a client device indicating a question or project proposal. The query text may be parsed and attributes of the query may be determined by inspecting the parsed query text. The query attributes may be compared with attributes associated with a pool of experts with various specialties and expertise in various fields. The network-accessible server system may match the query attributes with attributes associated with a first expert with a similarity that exceeds a threshold similarity level to identify that an expertise of the first expert matches the requested expertise in the query. The first expert may be assigned to the query and prompted to provide a response to the query.

An information search system, including: a database (12); a query sentence acceptance unit (26) that accepts a query sentence; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a shared keyword dictionary (30) in which relevant keywords are registered in association with each other; a local keyword dictionary (102) in which district keywords used in particular districts are registered; a candidate search keyword reader (32) that reads out a keyword that is relevant to the inputted search keyword; and a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword, wherein, in a case in which the inputted search keyword is not registered in the local keyword dictionary, the candidate search keyword reader refers to the shared keyword dictionary, so as to read out a keyword that is relevant to the inputted search keyword.

The subject matter described herein relates to methods, systems, and computer readable media for dynamic cluster-based search and retrieval. An example method for dynamic cluster-based search and retrieval occurs at a server. The method includes: retrieving document data for a plurality of documents related to user input; performing keyword discovery on the document data for determining term related frequency metrics and document related frequency metrics; representing the plurality of documents as a term-document matrix based on the term related frequency metrics and the document related frequency metrics; reducing, using latent semantic analysis, the dimensionality of the matrix; clustering, using a k-means clustering algorithm and the dimensionally reduced matrix, the plurality of documents into clusters; and sending presentation information to a client device for displaying visual representations of the clusters, wherein each visual representation is associated with one or more of the plurality of documents.