Abstract: The present disclosure includes acquiring a keyword of information to be replied to, as a first feature, and acquiring a keyword of a pending reply in a pending reply set as a second feature, calculating, according to a correlation between the first feature and the second feature, a match between the information to be replied to and the pending reply, where the correlation between the first feature and the second feature is obtained through multiple trainings according to an original text and a reply to the original text that are acquired from a corpus environment, where the corpus environment includes a microblog, a forum, and a post bar, repeating the foregoing steps, until matches between the information to be replied to and all pending replies are obtained, and selecting a best matched pending reply as a reply to the information to be replied to.

Abstract: A method for processing information by an intelligent agent and the intelligent agent, where the method comprises: a first intelligent agent sends a request message to a second intelligent agent, where the request message includes an invitation message or a recommendation message; the first intelligent agent receives a decision message fed back by the second intelligent agent, where the decision message is determined according to the invitation message or the recommendation message and a knowledge model of the second intelligent agent; and the first intelligent agent updates, according to the decision message, a knowledge model of the first intelligent agent or sends a notification message to a first user account corresponding to the first intelligent agent. By using these technical solutions, information on a social network may be learned and processed by means of interaction with another intelligent agent, thereby implementing mining of data on the social network.

Abstract: A method and an apparatus for determining a semantic matching degree. The method includes acquiring a first sentence and a second sentence, dividing the first sentence and the second sentence into x and y sentence fragments, respectively, performing a convolution operation on word vectors in each sentence fragment of the first sentence and word vectors in each sentence fragment of the second sentence, to obtain a three-dimensional tensor, performing integration and/or screening on adjacent vectors in the one-dimensional vectors of x rows and y columns, until the three-dimensional tensor is combined into a one-dimensional target vector, and determining a semantic matching degree between the first sentence and the second sentence according to the target vector.

Abstract: The present invention relates to the field of computer communications technologies and discloses an information retrieval method and apparatus. In this solution, even if result information does not include a retrieval keyword, a matching value between the result information and each local matching model can be determined in a local matching model set according to a retrieval keyword group and a result keyword group that is determined according to a result information group. Then, a relevance degree is obtained according to the matching value between the result information and all local matching models. Therefore, the result information that has relatively high relevance with the retrieval instruction but does not include the retrieval keyword can still be presented to a user, which improves retrieval accuracy.

Abstract: A “Name Disambiguator” provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names.

Abstract: A “Name Disambiguator” provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names.