1688 results about "Question answer" patented technology

Diffusing evidence among candidate answers during question answering may identify a relationship between a first candidate answer and a second candidate answer, wherein the candidate answers are generated by a question-answering computer process, the candidate answers have associated supporting evidence, and the candidate answers have associated confidence scores. All or some of the evidence may be transferred from the first candidate answer to the second candidate answer based on the identified relationship. A new confidence score may be computed for the second candidate answer based on the transferred evidence.

A system, method and computer program product for providing answers to questions based on any corpus of data. The method facilitates generating a number of candidate passages from the corpus that answer an input query, and finds the correct resulting answer by collecting supporting evidence from the multiple passages. By analyzing all retrieved passages and that passage's metadata in parallel, there is generated an output plurality of data structures including candidate answers based upon the analyzing. Then, by each of a plurality of parallel operating modules, supporting passage retrieval operations are performed upon the set of candidate answers, and for each candidate answer, the data corpus is traversed to find those passages having candidate answer in addition to query terms. All candidate answers are automatically scored causing the supporting passages by a plurality of scoring modules, each producing a module score. The modules scores are processed to determine one or more query answers; and, a query response is generated for delivery to a user based on the one or more query answers.

A question answering system includes a question input unit, a search unit, an answer candidate extraction unit, an answer candidate inspection unit and an answer output unit. The search unit executes search processing based on an input question. The answer candidate extraction unit extracts an initial answer candidate based on a result of the search processing. The answer candidate inspection unit inspects the initial answer candidate. The answer candidate inspection unit executes search processing with using a query including the initial answer candidate. The answer candidate inspection unit determines whether each word of a sentence obtained as a result of the search processing has a similar lexical meaning to that of each word of the input question. The answer candidate inspection unit selects an initial answer candidate contained in a query determined to have the similar lexical meaning, as a secondary answer candidate.

An intranet providing a multiple-carrel public-access kiosk is disclosed. The intranet provides free access to foreign and domestic informational e-commerce intranet sites as well as e-mail and public service educational and informational materials. The kiosk accepts anonymous pre-paid cards issued by a local franchisee of a network of c-commerce intranets that includes the local intranet. The franchisee owns or leases kiosks and also provides a walk-in e-commerce support center where e-commerce support services and goods, such as pre-paid accounts for access to paid services at a kiosk, can be purchased. The paid services provided by the carrel include video-conference and chat room time, playing and / or copying audio-visual materials such as computer games and music videos, and international e-commerce purchase support services such as customs and currency exchange. The third-party sponsored public service materials include audio-visual instructional materials in local dialects introducing the user to the use of the kiosks services and providing training for using standard business software programs. Sponsors include pop-up market research questions in the sponsored public service information and receive clickstream date correlated with the user's intranet ID and answers to the original demographic questions answered by the user.

The invention relates to a deep learning-based question and answer matching method. The method comprises the following steps of: 1) sufficiently learning word orders and sentence local features of a question text and an answer text by utilizing two underlying deep neural networks: a long short-term memory network LSTM and a convolutional neural network CNN; and 2) selecting a keyword with best semantic matching on the basis of a pooling manner of an attention mechanism AM. Compared with existing methods, the method has the advantages of being in low in feature engineering workload, strong in cross-field performance and relatively high in correctness, and can be effectively applied to the fields of commercial intelligent customer service robots, automatic driving, internet medical treatment, online forum and community question answering.

The invention provides a question and answer method based on a knowledge graph, and an agricultural encyclopedia question and answer system. A natural language question raised by a user can be automatically analyzed; a topological structure based on a syntax tree is formed; retrieval and comparison are carried out through the topological structure and a question template in a grammar library; according to a mapping relation between the topological structure and a predicate nominatum, and a mapping relation between a synonym set and a relation or an attribute in the knowledge graph, a question-mapped predicate is obtained; in combination with an entity identified in the question, a final structured knowledge graph query statement is generated; retrieval is carried out in the knowledge graphaccording to the query statement; and a final result is returned. When the relevant topological structure cannot be retrieved in a question template library, the question answering is carried out bycalling common question-answer pairs of an FAQ question library. The question and answer system can give accurate answer retrieval for the question posed by the user, so that the satisfaction degree of the user to the agricultural encyclopedia question retrieval is improved.

The disclosed database-based Chinese natural language question-answering method comprises mainly analyzing Web structure to extract internet FAQ and question-answering from opening community to build database. Unlike to prior art, this invention can accept natural language query and return the answer directly without user screening, and overcomes defects in a certain extent.

In order to provide a non-factoid question answering system with improved precision, the question answering system (160) includes: a candidate retrieving unit (222), responsive to a question, extracting answer candidates from a corpus storage (178); a feature vector generating unit (232) for generating features from combinations of a question with each of the answer candidates; SVMs (176) trained to calculate a score of how correct a combination of the question with an answer candidate is, upon receiving the feature vector therefor; and an answer ranker unit (234) outputting the answer candidate with the highest calculated score as the answer. The features are generated on the basis of the results of morphological analysis and parsing of the question, a phrase in the question evaluated as being positive or negative as well as its polarity, and the semantic classes of nouns in the features.