Abstract: A method is provided for processing random interaction data. The method includes receiving random interaction data sent by a first terminal, and adding the random interaction data to a resource pool. The random interaction data carries user attribute information and inputted conversation information of a user of the first terminal. The method also includes, when no reply message for the random interaction data is received from one or more second terminals, activating a second interface and sending the random interaction data to an intelligent dialog system via the second interface, where the intelligent dialog system is configured to automatically generate reply information according to at least the inputted conversation information and the user attribute information. The method also includes receiving the reply information returned by the intelligent dialog system; and sending the reply information to the first terminal.

Abstract: This application provides a method for constructing a network structure optimizer performed by an electronic device.

Abstract: A method for determining a candidate input is provided. Text that precedes and/or follows a current text input location in an input interface is acquired. A candidate sentence is acquired based on the text which is contained in a current sentence that corresponds to the current text input location. A preset number of possible conjunctions for the candidate sentence are determined based on occurrence probabilities of the possible conjunctions that are determined for the candidate sentence. The preset number of possible conjunctions are connected to the candidate sentence to obtain predicted candidate sentences. Occurrence probabilities of the predicted candidate sentences are calculated according to the text that precedes and/or follows the current text input location. Further, a preset number of the predicted candidate sentences are provided based on the calculated occurrence probabilities of the predicted candidate sentences as candidate inputs.

Abstract: Natural language question answering method and apparatus in the field of information retrieval and processing are provided. The method includes: acquiring a natural language question N; converting the natural language question N into a query semantic graph Qs, each edge in the query semantic graph Qs representing one semantic relation in the natural language question N; searching an RDF graph G for a subgraph matching the query semantic graph Qs; and obtaining an answer to the natural language question N according to the subgraph.

Abstract: A method is provided for processing random interaction data. The method includes receiving random interaction data sent by a first terminal, and adding the random interaction data to a resource pool. The random interaction data carries user attribute information and inputted conversation information of a user of the first terminal. The method also includes, when no reply message for the random interaction data is received from one or more second terminals, activating a second interface and sending the random interaction data to an intelligent dialog system via the second interface, where the intelligent dialog system is configured to automatically generate reply information according to at least the inputted conversation information and the user attribute information. The method also includes receiving the reply information returned by the intelligent dialog system; and sending the reply information to the first terminal.

Abstract: Natural language question answering method and apparatus in the field of information retrieval and processing are provided. The method includes: acquiring a natural language question N; converting the natural language question N into a query semantic graph Qs, each edge in the query semantic graph Qs representing one semantic relation in the natural language question N; searching an RDF graph G for a subgraph matching the query semantic graph Qs; and obtaining an answer to the natural language question N according to the subgraph.

Abstract: An information processing method and apparatus, the method including: training a Deep Neural Network (DNN) by using an evaluation object seed, an evaluation term seed and an evaluation relationship seed (101); at a first input layer, connecting vectors corresponding to a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship to obtain a first input vector (102); at a first hidden layer, compressing the first input vector to obtain a first middle vector, and at a first output layer, decoding the first middle vector to obtain a first output vector (103); and determining a first output vector whose decoding error value is less than a decoding error value threshold, and determining a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship corresponding to the determined first output vector as first opinion information (104).

Abstract: A natural language question answering method and apparatus belong to the field of information retrieval and processing. The method includes: acquiring a natural language question N; converting the natural language question N into a query semantic graph Qs, each edge in the query semantic graph Qs representing one semantic relation in the natural language question N; searching an RDF graph G for a subgraph matching the query semantic graph Qs; and obtaining an answer to the natural language question N according to the subgraph.

Abstract: A method for determining a candidate input is provided. Text that precedes and/or follows a current text input location in an input interface is acquired. A candidate sentence is acquired based on the text which is contained in a current sentence that corresponds to the current text input location. A preset number of possible conjunctions for the candidate sentence are determined based on occurrence probabilities of the possible conjunctions that are determined for the candidate sentence. The preset number of possible conjunctions are connected to the candidate sentence to obtain predicted candidate sentences. Occurrence probabilities of the predicted candidate sentences are calculated according to the text that precedes and/or follows the current text input location. Further, a preset number of the predicted candidate sentences are provided based on the calculated occurrence probabilities of the predicted candidate sentences as candidate inputs.

Abstract: A natural language question answering method and apparatus belong to the field of information retrieval and processing. The method includes: acquiring a natural language question N; converting the natural language question N into a query semantic graph Qs, each edge in the query semantic graph Qs representing one semantic relation in the natural language question N; searching an RDF graph G for a subgraph matching the query semantic graph Qs; and obtaining an answer to the natural language question N according to the subgraph.

Abstract: An information processing method and apparatus, the method including: training a Deep Neural Network (DNN) by using an evaluation object seed, an evaluation term seed and an evaluation relationship seed (101); at a first input layer, connecting vectors corresponding to a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship to obtain a first input vector (102); at a first hidden layer, compressing the first input vector to obtain a first middle vector, and at a first output layer, decoding the first middle vector to obtain a first output vector (103); and determining a first output vector whose decoding error value is less than a decoding error value threshold, and determining a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship corresponding to the determined first output vector as first opinion information (104).

Abstract: Disclosed are a search method, a search device and a storage medium. The method comprises obtaining all relevant documents of information to be sought; calculating a relevancy between each relevant document and the information to be sought based on a word matching algorithm and a semantics matching algorithm; performing sequencing processing on all the relevant documents according to the relevancy obtained through calculation, and displaying a sequencing result. Further disclosed is a search device. The present invention comprehensively considers matching between words, and matching of the semantics relationship between words, obtains an accurate relevancy calculation result, provides an ideal search result to a user, and improves satisfaction of the user.

Abstract: Disclosed are a search method, a search device and a storage medium. The method comprises obtaining all relevant documents of information to be sought; calculating a relevancy between each relevant document and the information to be sought based on a word matching algorithm and a semantics matching algorithm; performing sequencing processing on all the relevant documents according to the relevancy obtained through calculation, and displaying a sequencing result. Further disclosed is a search device. The present invention comprehensively considers matching between words, and matching of the semantics relationship between words, obtains an accurate relevancy calculation result, provides an ideal search result to a user, and improves satisfaction of the user.