A method and a device for generating a modified statement are provided. An embodiment thereof includes obtaining a target statement, and separating words in the target statement to obtain a word collection to be modified; determining whether a target storage portion comprises a predetermined word collection to be confirmed corresponding to a word to be modified in the word collection to be modified; in response to determine to include the predetermined word collection, based on the error probabilities and the usage probabilities of the words to be confirmed, determining modification probabilities of the words to be confirmed; according to a numerical sequence of the modification probabilities, selecting predetermined numbered words to be confirmed from the word collection to be confirmed as a word sub-collection to be confirmed; and based on the word sub-collection to be confirmed, generating the modified statement. The embodiment improves the accuracy to modify the statement.

A programmable device such as a smart phone allows a user an opportunity to make final corrections to textual data in a message after the user has instructed the device to send the message, but before transmittal of the message. The opportunity is temporary, to avoid impeding the flow of communication, and the textual data is transmitted unmodified if the opportunity to modify it is not accepted. Modifications made during the opportunity period may be used to adapt an autocorrect functionality of the programmable device.

A programmable device such as a smart phone allows a user an opportunity to make final corrections to textual data in a message after the user has instructed the device to send the message, but before transmittal of the message. The opportunity is temporary, to avoid impeding the flow of communication, and the textual data is transmitted unmodified if the opportunity to modify it is not accepted. Modifications made during the opportunity period may be used to adapt an autocorrect functionality of the programmable device.

This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.

This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.

Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.

Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.

Methods, systems, and computer programs are presented for formatting raw text. One method includes an operation for accessing raw text comprising words corresponding to one or more sentences. The raw text is lowercase text without any punctuation. Further, the method includes operations for creating a plurality of sub-words corresponding to the raw text, and for generating, by a machine-learning (ML) model, an output for each sub-word based on the created sub-words. The output for each sub-word indicates a formatting operation for the corresponding sub-word. The method further includes an operation for generating, based on the formatting operations in the outputs for the sub-words, formatted text corresponding to the raw text. The formatted text is text with correct grammar, proper punctuation, and proper capitalization according to a meaning of words spoken by a speaker associated with the raw text.

A method for removing text noise according to an embodiment of the present disclosure includes inspecting quality of the text, correcting the text based on a result of inspection; selecting a noise candidate based on each type of sentences included in the corrected text, wherein the noise candidate is selected for each sentence included in the text and removing at least some of the sentences included in the noise candidate based on the purpose of the text.

Methods and systems for correcting transcribed text. One method includes receiving audio data from one or more audio data sources and transcribing the audio data based on a voice model to generate text data. The method also includes making the text data available to a plurality of users over at least one computer network and receiving corrected text data over the at least one computer network from the plurality of users. In addition, the method can include modifying the voice model based on the corrected text data.