Abstract: A news interaction method, apparatus, device and computer storage medium are proposed. Input information input by a user upon reading current news content is obtained; parsing information of the input information is obtained based on the current news content, where the parsing information includes intent information of the input information; the input information is distributed to at least one news interactive service subsystem according to the intent information of the input information, and a return result returned by the at least one news interactive service subsystem is received; and a display result is selected from the return result according to a preset policy, and provided to the user.

Abstract: A news interaction method, apparatus, device and computer storage medium are proposed. The method includes: obtaining input information input by a user upon reading current news content; obtaining parsing information of the input information based on the current news content, where the parsing information includes intent information of the input information; distributing the input information to at least one news interactive service subsystem according to the intent information of the input information, and receiving a return result returned by the at least one news interactive service subsystem; and selecting a display result from the return result according to a preset policy, and providing the display result obtained by selection to the user. The present disclosure may enrich the interaction function of the current news application, satisfy the user's diverse interaction demands, and achieve provision of more diverse return results to the user.

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: 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 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: 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.