Abstract: A method for ranking a business object is provided. The method includes: obtaining a historical behavior record; extracting at least one of discrete feature information or continuous feature information of at least one business object from the historical behavior record; inputting at least one of discrete feature information or continuous feature information of each business object into a prediction model obtained by pre-training, and predicting a ranking score of each business object; and ranking each business object according to the ranking score of each business object.

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: 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 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: A method and an apparatus for word quality mining and evaluating are disclosed. The method includes: calculating a Document Frequency (DF) of a word in mass categorized data; evaluating the word in multiple single-aspects according to the DF of the word; and evaluating the word in multiple aspects according to the multiple single aspect evaluations to obtain an importance weight of the word. According to the solution of the present invention, the importance of the word in the mass categorized data may be evaluated, and words with high quality may be obtained through an integrated evaluation.

Abstract: A method and an apparatus for word quality mining and evaluating are disclosed. The method includes: calculating a Document Frequency (DF) of a word in mass categorized data; evaluating the word in multiple single-aspects according to the DF of the word; and evaluating the word in multiple aspects according to the multiple single aspect evaluations to obtain an importance weight of the word. According to the solution of the present invention, the importance of the word in the mass categorized data may be evaluated, and words with high quality may be obtained through an integrated evaluation.