Beijing Baidu Netcom Science and Technology Co., Ltd.

Qiugen Xiao

Jingzhou He

Zhihong Fu

Guyue Zhou

Liqun Zheng

Jinbo Zhan

Lei Shi

Pengkai Li

Zengfeng Zeng

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.

Method and apparatus for correcting query based on artificial intelligence

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.

Method and device for displaying multi-language typesetting, browser, terminal and computer readable storage medium

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.

Method for modifying segmentation model based on artificial intelligence, device and storage medium

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.

Word segmentation method based on artificial intelligence, server and storage medium

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.

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Beijing, China

Qiugen Xiao