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: The present disclosure provides a text error-correcting method, apparatus, electronic device and readable storage medium and relates to the field of natural language processing and deep learning. In the present disclosure, an implementation solution employed when performing text error correction is: obtaining a text to be processed, and an error-correcting type of the text to be processed; selecting a target error-correcting model corresponding to the error-correcting type; processing the text to be processed using the target error-correcting model, and regarding a processing result as an error-correcting result of the text to be processed. The present disclosure can enhance the flexibility and accuracy of text error correction.

Abstract: A bidirectional extraction socket may include a driven end configured to receive drive power from a driving tool, a drive end configured to interface with a fastener, and a body portion extending between the driven end and the drive end about an axis of the extraction socket. The drive end includes a fastener engagement recess extending into the body portion and coaxial with the body portion. The fastener engagement recess is configured to engage with the fastener such that the fastener is drivable in either a clockwise or a counterclockwise direction while avoiding contact with corner portions of the fastener.

Abstract: A bidirectional extraction socket may include a driven end configured to receive drive power from a driving tool, a drive end configured to interface with a fastener, and a body portion extending between the driven end and the drive end about an axis of the extraction socket. The drive end includes a fastener engagement recess extending into the body portion and coaxial with the body portion. The fastener engagement recess is configured to engage with the fastener such that the fastener is drivable in either a clockwise or a counterclockwise direction while avoiding contact with corner portions of the fastener.

Abstract: A hand tool includes a handle assembly, a jaw assembly, a clamping assembly, and an adjustment assembly. The handle assembly includes a first handle and a second handle that each include a proximal end and a distal end. The jaw assembly includes a first jaw and a second jaw. The jaw assembly is operably coupled to the proximal end of the first and second handles. The clamping assembly may be configured to enable the first and second jaws to be locked at a selected distance from each other in a locked position. The adjustment assembly may be configured to enable the selected distance to be modified. The adjustment assembly may be disposed between the first and second handles.

Abstract: A hand tool includes a handle assembly, a jaw assembly, a clamping assembly, and an adjustment assembly. The handle assembly may include a first handle and a second handle that each have a proximal end and a distal end. The jaw assembly may include a first jaw and a second jaw. The first jaw may be pivotally attached to a proximal end the first handle and the second jaw being pivotally attached to a proximal end of the second handle. The clamping assembly may be configured to enable the first and second jaws to be locked at a selected distance from each other in a locked position. The adjustment assembly may be disposed at an intersection of the jaw assembly and the handle assembly. The adjustment assembly may be configured to enable the selected distance to be modified.

Abstract: The technical solutions relate to the fields of artificial intelligence technologies and natural language processing technologies. According to an embodiment, entity detection is performed on a query text to acquire a target entity; a feature representation of the query text is generated by using a pre-trained context representation model; and based on the feature representation of the query text and pre-acquired feature representations of entity categories corresponding to the target entity, the target entity is linked to an entity category with the highest matching degree.

Abstract: A method and apparatus for correcting character errors, an electronic device and a storage medium are disclosed, which relates to the natural language processing field and the deep learning field. The method may include: for a character to be processed, acquiring the score of each character in a pre-constructed vocabulary, the score being a score of the reasonability of the character in the vocabulary at the position of the character to be processed; selecting top K characters as candidates of the character to be processed, K being a positive integer greater than one; selecting an optimal candidate from the K candidates; and replacing the character to be processed with the optimal candidate if the optimal candidate is different from the character to be processed. With the solution of the present application, the accuracy of an error correction result, or the like, may be improved.

Abstract: A hand tool (100) includes a head section (102) including a top jaw (106) and a bottom jaw (108), a handle section (104) including a top handle (112) and a bottom handle (114), and a joint assembly coupling the head section (102) to the handle section (104). The top jaw (106) is coupled to the bottom handle (114) by a first transition portion, and the bottom jaw (108) is operably coupled to the top handle (112) by a second transition portion. The first and second transition portions are pivotally coupled to each other by a pivot pin (140). The first transition portion is disposed on a first side of a longitudinal centerline of the hand tool (100), and at least a majority of the second transition portion is disposed on a second side of the longitudinal centerline such that a width of the first transition portion is less than a width of the second transition portion.

Abstract: A method and device for correcting an error in a text are provided. The method includes: preprocessing the text to obtain at least one segment of the text; generating a plurality of candidate segments for the segment; scoring the plurality of candidate segments with a tree model, to obtain respective first scoring results of the plurality of candidate segments; scoring the plurality of candidate segments with a deep neural network model, to obtain respective second scoring results of the plurality of candidate segments; for each candidate segment, calculating a scoring of the candidate segment based on the first scoring result and the second scoring result of the candidate segment; ranking the plurality of candidate segments according to the scorings of the candidate segments, to obtain a ranking result; and correcting the error in the text according to the ranking result.

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: A method and device for correcting an error in a text are provided. The method includes: preprocessing the text to obtain at least one segment of the text; generating a plurality of candidate segments for the segment; scoring the plurality of candidate segments with a tree model, to obtain respective first scoring results of the plurality of candidate segments; scoring the plurality of candidate segments with a deep neural network model, to obtain respective second scoring results of the plurality of candidate segments; for each candidate segment, calculating a scoring of the candidate segment based on the first scoring result and the second scoring result of the candidate segment; ranking the plurality of candidate segments according to the scorings of the candidate segments, to obtain a ranking result; and correcting the error in the text according to the ranking result.

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: The present disclosure discloses a method and apparatus for generating a parallel text in the same language. The method comprises: acquiring a source segmented word sequence and a pre-trained word vector table; determining a source word vector sequence corresponding to the source segmented word sequence, according to the word vector table; importing the source word vector sequence into a first pre-trained recurrent neural network model, to generate an intermediate vector of a preset dimension for characterizing semantics of the source segmented word sequence; importing the intermediate vector into a second pre-trained recurrent neural network model, to generate a target word vector sequence corresponding to the intermediate vector; and determining a target segmented word sequence corresponding to the target word vector sequence according to the word vector table, and determining the target segmented word sequence as a parallel text in the same language corresponding to the source segmented word sequence.

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 bidirectional extraction socket may include a driven end configured to receive drive power from a driving tool, a drive end configured to interface with a fastener, and a body portion extending between the driven end and the drive end about an axis of the extraction socket. The drive end includes a fastener engagement recess extending into the body portion and coaxial with the body portion. The fastener engagement recess is configured to engage with the fastener such that the fastener is drivable in either a clockwise or a counterclockwise direction while avoiding contact with corner portions of the fastener.