Abstract: A method and an apparatus for correcting a query based on artificial intelligence, including: receiving a first query input by a user, and judging whether the first query satisfies an error correcting condition according to a preset error correcting strategy; determining a first segment to be corrected in the first query if the first query satisfies the error correcting condition; acquiring one or more first candidate results corresponding to the first segment according to a preset candidate recalling strategy; determining an error correcting result corresponding to the first segment according to quality feature values of the one or more first candidate results; and performing an error correction on the first query according to the error correcting result, and generating a second query.

Abstract: The present disclosure provides a method and a device for displaying multi-language typesetting, a browser, a terminal and a computer readable storage medium. The method includes: obtaining a text to be typeset; identifying embedded language content in a principal language text of the text to be typeset, wherein the embedded language content comprises at least one non-principal language content embedded in the principal language text; determining replacement content of the embedded language content, wherein the replacement content comprises a principal language text corresponding to the embedded language content or an abbreviation of a non-principal language text in the embedded language content; and replacing the embedded language content with the replacement content.

Abstract: Embodiments of the present disclosure disclose a method for modifying a segmentation model based on artificial intelligence, a device and a storage medium. The method may include: acquiring a model parameter of the segmentation model, and performing a training on a feature vector corresponding to a preset generalized feature of a first training corpus via a neural network so as to acquire a model parameter of the preset generalized feature; performing a word segmentation on the first training corpus according to the model parameter of the segmentation model and the model parameter of the preset generalized feature, so as to acquire a segmentation result; and comparing the segmentation result with the first training corpus according to a preset rule, and modifying the model parameter of the segmentation model and a parameter of the neural network according to a comparison result.

Abstract: Embodiments of the present disclosure disclose a word segmentation method based on artificial intelligence, a server and a storage medium. The word segmentation method may include: acquiring a corpus to be segmented and a segmentation model corresponding to a preset segmentation template; matching the corpus to be segmented with the segmentation model according to a preset matching algorithm, and acquiring a target phrase satisfying a first preset rule in the corpus to be segmented; modifying an emission matrix corresponding to the segmentation model and the corpus to be segmented according to the target phrase; and performing a word segmentation on the corpus to be segmented according to the emission matrix modified, to acquire a first segmentation result.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for customizing a word segmentation model based on artificial intelligence, a device and a medium. The method includes the followings. A customized segmentation training corpus is acquired. A first preset word segmentation model is rectified with an increment training method or a weight intervention method, based on the customized segmentation training corpus, to obtain a customized word segmentation model corresponding to the customized segmentation training corpus.

Abstract: The present disclosure provides a method and a device for displaying multi-language typesetting, a browser, a terminal and a computer readable storage medium. The method includes: obtaining a text to be typeset; identifying embedded language content in a principal language text of the text to be typeset, wherein the embedded language content comprises at least one non-principal language content embedded in the principal language text; determining replacement content of the embedded language content, wherein the replacement content comprises a principal language text corresponding to the embedded language content or an abbreviation of a non-principal language text in the embedded language content; and replacing the embedded language content with the replacement content.

Abstract: Embodiments of the disclosure disclose a method, apparatus, device, and storage medium for establishing an error correction model based on an error correction platform. The method comprises: determining a target error correction level based on an error correction need of a user; and selecting at least one error correction module from each of at least two error correcting portions of the error correction platform based on the target error correction level, and combining the selected error correction module to form an error correction model.

Abstract: Embodiments of the present disclosure disclose a method for modifying a segmentation model based on artificial intelligence, a device and a storage medium. The method may include: acquiring a model parameter of the segmentation model, and performing a training on a feature vector corresponding to a preset generalized feature of a first training corpus via a neural network so as to acquire a model parameter of the preset generalized feature; performing a word segmentation on the first training corpus according to the model parameter of the segmentation model and the model parameter of the preset generalized feature, so as to acquire a segmentation result; and comparing the segmentation result with the first training corpus according to a preset rule, and modifying the model parameter of the segmentation model and a parameter of the neural network according to a comparison result.

Abstract: Embodiments of the present disclosure disclose a word segmentation method based on artificial intelligence, a server and a storage medium. The word segmentation method may include: acquiring a corpus to be segmented and a segmentation model corresponding to a preset segmentation template; matching the corpus to be segmented with the segmentation model according to a preset matching algorithm, and acquiring a target phrase satisfying a first preset rule in the corpus to be segmented; modifying an emission matrix corresponding to the segmentation model and the corpus to be segmented according to the target phrase; and performing a word segmentation on the corpus to be segmented according to the emission matrix modified, to acquire a first segmentation result.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for customizing a word segmentation model based on artificial intelligence, a device and a medium. The method includes the followings. A customized segmentation training corpus is acquired. A first preset word segmentation model is rectified with an increment training method or a weight intervention method, based on the customized segmentation training corpus, to obtain a customized word segmentation model corresponding to the customized segmentation training corpus.

Abstract: A method and an apparatus for correcting a query based on artificial intelligence, including: receiving a first query input by a user, and judging whether the first query satisfies an error correcting condition according to a preset error correcting strategy; determining a first segment to be corrected in the first query if the first query satisfies the error correcting condition; acquiring one or more first candidate results corresponding to the first segment according to a preset candidate recalling strategy; determining an error correcting result corresponding to the first segment according to quality feature values of the one or more first candidate results; and performing an error correction on the first query according to the error correcting result, and generating a second query.