Tencent Technology (shenzhen) Company Limited

Fen Lin

Rongyu Cao

Ping Luo

Juncen Li

Ganbin Zhou

Yue Shu

Bo Song Chen

Yan Xiong Lu

Yanxiong Lu

Qing He

Yanan Lu

The present application discloses an information processing method performed at a computing device. The method includes: collecting first information; executing an intent identification task on the first information to obtain an intent identification processing result; executing a slot identification task on the first information according to the intent identification processing result to obtain a slot identification processing result; narrowing a search range according to the slot identification processing result; and performing a database search for the first information within the narrowed search range.

Information processing method and terminal, and computer storage medium

The present application discloses an information processing method performed at a computing device. The method includes: collecting first information; executing an intent identification task on the first information to obtain an intent identification processing result; executing a slot identification task on the first information according to the intent identification processing result to obtain a slot identification processing result; and outputting a target result according to the slot identification processing result.

A semantic analysis method includes: inputting a word vector of each word in each sample sentence in a dialog flow into an encoder model, to obtain a sentence vector representing semantics of the sample sentence; inputting the sentence vector into a first decoder model corresponding to each context sentence of the sample sentence and a second decoder model corresponding to each word of the sample sentence, to obtain a first identifier corresponding to the context sentence and a second identifier corresponding to the word; obtaining a probability of the first identifier and a probability of the second identifier, and determining a value of a target function; performing parameter training on the encoder model according to the value of the target function; and inputting a word vector of each word in a test sentence into the trained encoder model, to obtain a sentence vector representing semantics of the test sentence.

Semantic analysis method and apparatus, and storage medium

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.

Method for determining candidate input, input prompting method and electronic device

This application discloses a session information processing method and device, and a storage medium. The method includes: extracting a to-be-analyzed sentence and preceding sentences of the to-be-analyzed sentence from a session; performing word segmentation on the to-be-analyzed sentence and the preceding sentences, to obtain a first feature set including a plurality of first features; extracting a second feature set including one or more second features from a first word set corresponding to the to-be-analyzed sentence and a second word set corresponding to the preceding sentences, one second feature including a phrase or sentence including a first word and a second word, the first word being one or more words in the first word set, and the second word being one or more words in the second word set; and determining, according to the first feature set and the second feature set, a sentence category to which the to-be-analyzed sentence belongs.

Session information processing method and device and storage medium

A chat message processing method is provided for a server. The method includes receiving a chat message sent by a sending terminal, where the chat message carries information about a receiving terminal, and forwarding the chat message to the receiving terminal. The method also includes, when determining that the chat message comprises an image, generating at least one group of descriptive texts used for describing the image; and sending the at least one group of descriptive texts of the image to the receiving terminal to cause the receiving terminal to, when displaying the image, simultaneously display the at least one group of descriptive texts as at least one candidate entering-item associated with the image.

Method and apparatus for generating descriptive texts corresponding to chat message images via a condition probability model

A dialog generation method, apparatus, and device, and a storage medium are provided. The method includes converting each term in a Kth round of a query sentence into a first word vector, and calculating a positive latent vector and a negative latent vector of each term according to the first word vector, where K denotes a positive integer greater than or equal to 2. A content topic of the Kth round of the query sentence is obtained, and converted into a second word vector. An initial latent vector output for the Kth round of the query sentence is determined according to the second word vector, the positive latent vector of a last term in the Kth round of the query sentence, a latent vector of a last term in a (K-1)th round of a reply sentence output for a (K-1)th round of the query sentence, and an initial latent vector of the (K-1)th round of the reply sentence output for the (K-1)th round of the query sentence. A reply sentence for the Kth round of the query sentence is generated according to the positive latent vector and the negative latent vector of each term in the Kth round of the query sentence and the initial latent vector output for the Kth round of the query sentence.

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Fen Lin