Abstract: The present disclosure, belonging to the technical field of natural language processing, provides a text sequence segmentation method. The method can include acquiring n segmentation sub-results of the text sequence, the n segmentation sub-results being acquired by segmenting the text sequence by n segmentation models, and processing the n segmentation sub-results by a probability determination model branch in a result combination model to acquire a segmentation probability of the each segmentation position. The method can further include processing the segmentation probability of the each segmentation position by an activation function in the result combination model to acquire a segmentation result of the text sequence. According to the present disclosure, using each segmentation position in the text sequence as a unit, segmentation results of a plurality of segmentation models are combined, such that the accuracy of segmentation of a new text can be improved.

Abstract: A method for acquiring push information can be applied to a terminal device and include: transmitting a device model of the terminal device to a server; receiving a push software development kit corresponding to the device model, and a configuration file corresponding to the push software development kit transmitted by the server; initializing the push software development kit based on the configuration file, and completing a registration with the server; and receiving, through the push software development kit, push information transmitted by the server. The terminal device can therefore acquire its corresponding push software development kit according to the device model of the terminal device through a plug-in method, thereby increase push arrival rate and improve quality of operational data.

Abstract: The disclosure relates to a method and apparatus for acquiring spatial division information. The method includes controlling a sound source device to play a first sound signal; obtaining a second sound signal that is a sound signal collected by a sound collecting device when the first sound signal is propagated to the sound collecting device; obtaining direct intensity information based on the second sound signal, wherein the direct intensity information indicates an intensity of a direct sound signal in the second sound signal, wherein the direct sound signal is a sound signal that is generated by the sound source device and reaches the sound collecting device without physical reflection; and obtaining spatial division information based on the direct intensity information, wherein the spatial division information indicates whether the sound source device and the sound collecting device are in a same spatial zone.

Abstract: A method for optimizing a training set for text classification includes: the training set for text classification is acquired; part of samples are selected from the training set as a first initial training subset, and an incorrectly tagged sample in the first initial training subset is corrected to obtain a second initial training subset; a text classification model is trained according to the second initial training subset; the samples in the training set are predicted by the trained text classification model to obtain a prediction result; an incorrectly tagged sample set is generated according to the prediction result; a key incorrectly tagged sample is selected from the incorrectly tagged sample set, and a tag of the key incorrectly tagged sample is corrected to generate a correctly tagged sample corresponding to the key incorrectly tagged sample; and the training set is updated by using the correctly tagged sample.

Abstract: A method for object tracking includes: obtaining frames with a number of N of history images of the object; acquiring first predicted feature point information of each frame image by using first network models corresponding to each frame image in the frames with a number of N of history images, and acquiring second predicted feature point information of the each frame image by using second network models corresponding to each frame image; adjusting parameters of the first network model and parameters of the second network model based on the first predicted feature point information and the second predicted feature point information until the first network model and the second network model are trained completely; and performing tracking of the object by using the first completely trained network model and the second completely trained network model.

Abstract: A method for responding to a voice signal includes: receiving a voice signal by the plurality of electronic devices; determining, for each of the electronic devices, a target sound source point associated with the voice signal; determining, for each of the electronic devices, a direct sound energy associated with the voice signal according to the target sound source point; and selecting from the plurality of electronic devices at least one electronic device with a direct sound energy satisfying a predetermined condition, and responding to the voice signal by the at least one electronic device. A sound pickup decision system based on a plurality of electronic devices, can make more accurate, more reasonable, and more targeted response to a user's voice, improving voice interaction experience.

Abstract: A supernet construction method includes: setting a linear connection unit in at least one layer of a supernet, wherein an input end of the linear connection unit is connected to an upper layer of a home layer of the linear connection unit, and an output end is connected to a lower layer of the home layer of the linear connection unit; an output and an input of the linear connection unit form a linear relationship, where the linear relationship includes a linear relationship other than that the output is equal to the input.

Abstract: A method for processing untagged data includes: similarity comparison is performed on a semantic vector of untagged data and a semantic vector of each piece of tagged data to obtain similarities corresponding to respective pieces of tagged data; a preset number of similarities are selected according to a preset selection rule; the untagged data is predicted with a tagging model obtained by training through the tagged data, to obtain a prediction result of the untagged data; and the untagged data is divided into untagged data that can be tagged by a device or untagged data that cannot be tagged by the device according to the preset number of similarities and the prediction result.

Abstract: A clustering method includes: multiple data to be clustered are acquired, the data to be clustered including exact matching information; multiple data sets consisting of data to be clustered with same exact matching information are determined; a clustering center is determined according to the amount of data to be clustered in each of the multiple data sets; and the multiple data to be clustered are clustered according to the clustering center to obtain target clusters. Accordingly, multiple data sets each consisting of data to be clustered with same exact matching information are determined, and a clustering center is determined according to the amount of the data to be clustered in each of the multiple data sets, so that unreasonable determination of the clustering centers can be improved, and accuracy of a clustering algorithm can further be improved.

Abstract: A method includes: acquiring a second initial pose according to obstacle region information and geometric scale information of an indoor architectural structure drawing and a first initial pose; acquiring a first feature point location, a locating error and a first pose through a VSLAM algorithm during movement; acquiring a second pose according to the first pose, first initial pose and second initial pose; acquiring a distance to an obstacle according to the second pose, indoor architectural structure drawing and a ray casting algorithm; acquiring a third pose according to the distance, first feature point location, locating error, first pose and a filtering algorithm; acquiring a second feature point location according to the third pose and VSLAM algorithm; and creating an indoor environment map according to the second feature point location and a key frame set.

Abstract: A message processing method includes: acquiring an unread message in the communication software; identifying contents of the unread message to obtain summary information of the unread message; and outputting the summary information.

Abstract: A method includes: acquiring a second initial pose according to obstacle region information and geometric scale information of an indoor architectural structure drawing and a first initial pose; acquiring a first feature point location, a locating error and a first pose through a VSLAM algorithm during movement; acquiring a second pose according to the first pose, first initial pose and second initial pose; acquiring a distance to an obstacle according to the second pose, indoor architectural structure drawing and a ray casting algorithm; acquiring a third pose according to the distance, first feature point location, locating error, first pose and a filtering algorithm; acquiring a second feature point location according to the third pose and VSLAM algorithm; and creating an indoor environment map according to the second feature point location and a key frame set.

Abstract: The disclosure relates to a method and apparatus for acquiring spatial division information. The method includes controlling a sound source device to play a first sound signal; obtaining a second sound signal that is a sound signal collected by a sound collecting device when the first sound signal is propagated to the sound collecting device; obtaining direct intensity information based on the second sound signal, wherein the direct intensity information indicates an intensity of a direct sound signal in the second sound signal, wherein the direct sound signal is a sound signal that is generated by the sound source device and reaches the sound collecting device without physical reflection; and obtaining spatial division information based on the direct intensity information, wherein the spatial division information indicates whether the sound source device and the sound collecting device are in a same spatial zone.

Abstract: A text processing method includes: word segmentation is performed on a first text to be processed to determine multiple words of the first text; one word is removed from the first text to obtain one second text; each word is removed from the first text one by one to obtain a second text set; the first text and each second text in the second text set are analyzed respectively to obtain a first tree structure of the first text and a second tree structure of each second text; and a summary text of the first text is determined according to the first tree structure and each second tree structure.

Abstract: The present disclosure relates to a method and an apparatus based on a neural network model. The method includes acquiring a plurality of training samples, each of the training samples comprising an identifier of an input word, an identifier of an output word, and position information, wherein in one of the training samples, the output word is a context of the input word, and the position information indicates a relative position between the output word and the input word; calling a position relation-based Continuous Skip-gram Model (Skip-Gram), with each of the training samples as an input, to obtain an output result, the output result comprising a word matrix embedding of the input word, a word matrix embedding of the output word and a position matrix embedding of the position information; and updating the position relation-based Skip-Gram based on the output result to train the position relation-based Skip-Gram.

Abstract: A method and apparatus for controlling a device are disclosed. The method includes: performing voice recognition on a received sound signal to obtain a voice recognition result; determining keywords using the voice recognition result; determining a target intelligent device having attribute information matched with the keywords from intelligent devices, where relationships between the intelligent devices and attribute information of the intelligent devices are constructed in advance, and the attribute information characterizes a device operation provided by the intelligent device corresponding to the attribute information; and controlling the target intelligent device to perform an operation indicated by the voice recognition result.

Abstract: A keyword extraction method includes: extracting candidate words from an original document to form a first word set; acquiring the first correlation degree between each candidate word in the first word set and the original document, and based on which determining a second word set; generating predicted words forming a third word set through a prediction model; determining a union set of the second and third word sets, acquiring the second correlation degree between each of the candidate keywords in the union set and the original document, acquiring a divergence of each candidate keyword in the union set; and selecting candidate keywords from the union set as keywords based on the second correlation degree and the divergence. Keyword redundancy can be avoided through the divergence of keywords. The final keywords are not affected by the frequency of candidate words, and the expression mode of keywords can be enriched.

Abstract: A keyword extraction method includes: extracting candidate words from an original document to form a first word set; acquiring a first association degree between each first word thereof and the original document, and determining a second word set according to the first association degree; for each second word in the second word set, inquiring, in a word association topology, at least one node word satisfying a condition of association with the second word and forming a third word set, the word association topology indicating an association relation among multiple node words in a predetermined field; and determining a union set of the second and third word sets, acquiring a second association degree between each candidate keyword in the union set and the original document, and selecting, according to the second association degree, at least one candidate keyword from the union set, to form a keyword set of the original document.

Abstract: The disclosure relates to a method, an apparatus, a system and a device for controlling an intelligent device, and a storage medium. The method includes collecting voice data of a user; performing semantic recognition processing on the collected voice data of the user to obtain a recognition result; determining a target intelligent device based on device information in the recognition result; generating a control command based on operation instruction information in the recognition result; and sending the control command to the target intelligent device.