CN110019928B - Video title optimization method and device - Google Patents
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Abstract
The present disclosure relates to a method and apparatus for optimizing a video title. The method comprises the following steps: performing word segmentation processing on an original title of the video to obtain a word segmentation result; determining word vectors corresponding to all words in the word segmentation result; determining a compressed vector corresponding to the original title according to the word vector corresponding to each word; and determining the optimized title corresponding to the original title according to the compressed vector and the word vector corresponding to each word. The method comprises the steps of performing word segmentation processing on an original title of a video to obtain word segmentation results, determining word vectors corresponding to words in the word segmentation results, determining compressed vectors corresponding to the original title according to the word vectors corresponding to the words, and determining an optimized title corresponding to the original title according to the compressed vectors and the word vectors corresponding to the words, so that the original title of the video can be optimized, and the click rate of the video is improved.
Description
Technical Field
The present disclosure relates to the field of video technologies, and in particular, to a method and an apparatus for optimizing a video title.
Background
Currently, the title of a UGC (User Generated Content) video is edited by a UGC video producer. Most inexperienced UGC video producers have difficulty editing a good quality video title, resulting in a video title that is difficult to attract users, resulting in a low click rate for the video.
Disclosure of Invention
In view of this, the present disclosure provides a method and an apparatus for optimizing a video title.
According to an aspect of the present disclosure, there is provided a method of optimizing a video title, including:
performing word segmentation processing on an original title of the video to obtain a word segmentation result;
determining word vectors corresponding to all words in the word segmentation result;
determining a compressed vector corresponding to the original title according to the word vector corresponding to each word;
and determining the optimized title corresponding to the original title according to the compressed vector and the word vector corresponding to each word.
In a possible implementation manner, determining a compressed vector corresponding to the original header according to the word vector corresponding to each word includes:
determining a hidden layer node value corresponding to an ith word according to a word vector corresponding to the ith word in the word segmentation result and a hidden layer node value corresponding to an i-1 word, wherein i is a positive integer;
and determining the hidden node value corresponding to the last word in the word segmentation result as the compressed vector corresponding to the original title.
In a possible implementation manner, determining an optimized title corresponding to the original title according to the compressed vector and the word vector corresponding to each word includes:
determining a t-th hidden state value according to the compressed vector, the t-1 th hidden state value and the t-1 th optimized word, wherein t is a positive integer;
determining a tth optimized word according to the tth hidden layer state value, the t-1 th optimized word and the word vector corresponding to each word;
and determining the optimized title corresponding to the original title according to the T optimized words, wherein T is the total number of the optimized words, and T is less than or equal to T.
In a possible implementation manner, determining an optimized title corresponding to the original title according to T optimized words includes:
calculating scores corresponding to all candidate titles generated by the T optimized words;
and determining the candidate title with the highest score as the optimized title corresponding to the original title.
According to another aspect of the present disclosure, there is provided an optimization apparatus of a video title, including:
the word segmentation module is used for carrying out word segmentation processing on the original title of the video to obtain a word segmentation result;
the first determining module is used for determining word vectors corresponding to all words in the word segmentation result;
a second determining module, configured to determine, according to the word vector corresponding to each word, a compressed vector corresponding to the original header;
and a third determining module, configured to determine, according to the compressed vector and the word vector corresponding to each word, an optimized title corresponding to the original title.
In one possible implementation manner, the second determining module includes:
a first determining submodule, configured to determine, according to a word vector corresponding to an ith word in the word segmentation result and an implicit layer node value corresponding to an i-1 th word, an implicit layer node value corresponding to the ith word, where i is a positive integer;
and the second determining submodule is used for determining the hidden node value corresponding to the last word in the word segmentation result as the compressed vector corresponding to the original title.
In one possible implementation manner, the third determining module includes:
the third determining submodule is used for determining the tth hidden state value according to the compressed vector, the tth hidden state value and the tth optimized word, wherein t is a positive integer;
a fourth determining submodule, configured to determine a tth optimized word according to the tth hidden state value, the tth-1 optimized word, and the word vector corresponding to each word;
and the fifth determining submodule is used for determining the optimized title corresponding to the original title according to the T optimized words, wherein T is the total number of the optimized words, and T is smaller than or equal to T.
In one possible implementation, the fifth determining submodule is configured to:
calculating scores corresponding to all candidate titles generated by the T optimized words;
and determining the candidate title with the highest score as the optimized title corresponding to the original title.
According to another aspect of the present disclosure, there is provided an optimization apparatus of a video title, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the above method.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.
The method and the device for optimizing the video title in each aspect of the disclosure perform word segmentation processing on the original title of the video to obtain word segmentation results, determine word vectors corresponding to words in the word segmentation results, determine compressed vectors corresponding to the original title according to the word vectors corresponding to the words, and determine an optimized title corresponding to the original title according to the compressed vectors and the word vectors corresponding to the words, so that the original title of the video can be optimized, and the click rate of the video is improved.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 illustrates a flowchart of an optimization method of a video title according to an embodiment of the present disclosure.
Fig. 2 shows an exemplary flowchart of the step S13 of the method for optimizing a video title according to an embodiment of the present disclosure.
Fig. 3 shows an exemplary flowchart of the step S14 of the method for optimizing a video title according to an embodiment of the present disclosure.
Fig. 4 shows an exemplary flowchart of the step S143 of the method for optimizing a video title according to an embodiment of the present disclosure.
Fig. 5 illustrates a schematic diagram of an optimization method of a video title according to an embodiment of the present disclosure.
Fig. 6 illustrates a block diagram of an apparatus for optimizing a video title according to an embodiment of the present disclosure.
Fig. 7 illustrates an exemplary block diagram of an apparatus for optimizing a video title according to an embodiment of the present disclosure.
Fig. 8 is a block diagram illustrating an apparatus 800 for optimization of a video title according to an example embodiment.
Fig. 9 is a block diagram illustrating an apparatus 1900 for optimization of a video title according to an example embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 illustrates a flowchart of an optimization method of a video title according to an embodiment of the present disclosure. The method can be applied to terminal equipment. As shown in fig. 1, the method includes steps S11 through S14.
In step S11, a word segmentation process is performed on the original title of the video to obtain a word segmentation result.
The video in this embodiment may be any video. For example, it may be a short video.
For example, the original title of the video is "singing in the husky meeting", and the segmentation result is X ═ X1,x2,x3In which x1x-Husky2X is ═ Hui3As singing.
It should be noted that, in this embodiment, any word segmentation tool in the related art may be used to perform word segmentation processing on the original title of the video, and this embodiment does not limit this. For example, the original title may be word-segmented by the longest matching method.
In step S12, word vectors corresponding to the respective words in the word segmentation result are determined.
As an example of this embodiment, a word vector corresponding to each word in the word segmentation result may be determined by using equation 1:
si＝W1μ(xi) In the formula 1, the compound is shown in the specification,
wherein s isiRepresenting the word vector corresponding to the ith word in the word segmentation result, i is more than or equal to 1 and less than or equal to N, N represents the total word number in the word segmentation result, W1A real matrix of K columns and N rows, W1Can be updated by a gradient descent algorithm to obtain xiDenotes the ith word, μ (x) in the word segmentation resulti) Can be one-hot coded, mu (x)i) Is a K-dimensional vector.
In step S13, a compressed vector corresponding to the original title is determined from the word vector corresponding to each word.
The compressed vector corresponding to the original header may reflect the characteristics of the original header. For example, the compressed vector corresponding to the original title may reflect the characteristics of all words in the original title.
In step S14, an optimized title corresponding to the original title is determined based on the compressed vector and the word vector corresponding to each word.
For example, the original title is "singing with husky songs", and the optimized title corresponding to the original title is "racing up with violent songs" in two huskies.
The method and the device have the advantages that the original title of the video is subjected to word segmentation processing to obtain word segmentation results, word vectors corresponding to words in the word segmentation results are determined, compressed vectors corresponding to the original title are determined according to the word vectors corresponding to the words, and optimized titles corresponding to the original title are determined according to the compressed vectors and the word vectors corresponding to the words, so that the original title of the video can be optimized, and the click rate of the video is improved.
Fig. 2 shows an exemplary flowchart of the step S13 of the method for optimizing a video title according to an embodiment of the present disclosure. As shown in fig. 2, step S13 may include step S131 and step S132.
In step S131, a hidden layer node value corresponding to the ith word is determined according to a word vector corresponding to the ith word in the word segmentation result and a hidden layer node value corresponding to the (i-1) th word, where i is a positive integer.
As an example of this embodiment, equation 2 may be adopted to determine the hidden layer node value corresponding to the ith word:
wherein s isiRepresenting the word vector corresponding to the ith word in the word segmentation result, i is more than or equal to 1 and less than or equal to N, N represents the total word number in the word segmentation result, hi-1Represents the hidden layer node value corresponding to the i-1 th word,
a hidden node function of the LSTM (Long-Short Term Memory) network may be represented. h is0May be a zero vector.
In step S132, the hidden node value corresponding to the last word in the word segmentation result is determined as the compressed vector corresponding to the original header.
As an example of this embodiment, the hidden node value corresponding to the last word in the word segmentation result can be represented as
Fig. 3 shows an exemplary flowchart of the step S14 of the method for optimizing a video title according to an embodiment of the present disclosure. As shown in fig. 3, step S14 may include steps S141 to S143.
In step S141, a tth hidden state value is determined according to the compressed vector, the tth hidden state value and the tth optimized word, where t is a positive integer.
As an example of this embodiment, the tth hidden state value may be determined using equation 3:
zt＝τ(hN,yt-1,zt-1) In the formula 3, the first step is,
where τ may be a nonlinear activation function, hNFor compressed vectors corresponding to the original header, zt-1Is t-1 hidden layer stateValue, yt-1For the t-1 th optimized word, z0May be a zero vector. y is0May be a random vector and may be trained with subsequent data to converge.
In step S142, the tth optimized word is determined according to the tth hidden state value, the t-1 th optimized word and the word vector corresponding to each word.
As an example of this embodiment, the tth optimized word may be determined using equation 4:
p(yt|yt-1,X)＝softmax(W2zt+ b) a compound of formula 4,
wherein, ytCandidate word representing the t-th optimized word, yt-1Denotes the t-1 th optimized word, X denotes the original title, p (y)t|yt-1X) indicates that y is obtained given the original title and the t-1 st optimized wordtProbability of (1), softmax denotes the softmax function, W2A real matrix of K columns and N rows, W2Z can be updated by a gradient descent algorithmtRepresents the t-th hidden state value and b represents the offset of the softmax function.
In this example, p (y) may be madet|yt-1X) maximum ytAs the t-th optimized word.
E.g. y1Two hah, y2After "competitive", y3When being equal to "can", y4Biao, y5"Tian xi", the total number of the optimization words is 5.
In step S143, an optimized title corresponding to the original title is determined according to T optimized words, where T is the total number of the optimized words, and T is less than or equal to T.
As an example of this embodiment, T optimized words may be connected in sequence to obtain an optimized title corresponding to an original title.
Fig. 4 shows an exemplary flowchart of the step S143 of the method for optimizing a video title according to an embodiment of the present disclosure. As shown in fig. 4, step S143 may include step S1431 and step S1432.
In step S1431, a score corresponding to each candidate title generated from the T optimized words is calculated.
In one possible implementation, an N-Gram language model may be used to calculate scores corresponding to each candidate title generated from the T optimized words.
As an example of this implementation, a trigram in the N-Gram language model may be used to calculate scores corresponding to respective candidate headings generated from the T optimized words. For example, the score corresponding to each candidate title may be calculated using equation 5,
wherein, yjIndicating the jth candidate title. p (y)t|yt-1,yt-2) The calculation method(s) can be determined according to the N-Gram language model, and will not be described herein.
In step S1432, the candidate title with the highest score is determined as the optimized title corresponding to the original title.
And determining the candidate title with the highest score as the optimized title corresponding to the original title, so that the title which best meets the language habit can be screened from all the candidate titles.
In one possible implementation, the method may further include: a training data set is obtained, wherein the training data set comprises a plurality of groups of title pairs, and each group of title pairs comprises an original title and an optimized title. The title pairs in the training data set can be obtained from titles edited by the video website operation, titles of videos pushed by the video website pushing system, titles of home pages of the video website and the like. After the training data set is obtained, the LSTM model may be trained according to each set of titles, resulting in an LSTM model for optimizing video titles. According to different styles of the optimized titles in the training data set, the LSTM model for generating the optimized titles of different styles can be obtained through training.
Fig. 5 illustrates a schematic diagram of an optimization method of a video title according to an embodiment of the present disclosure. As shown in fig. 5, the method for optimizing a video title, which may include an encoding process and a decoding process, may be implemented by the LSTM model. The encoding process may be used to determine a compressed vector corresponding to the original header, and the compressed vector may be used as the monitoring information of the decoding process. The EOS in the encoding process may indicate that the encoding of the original title is finished, and the EOS in the decoding process may indicate that the last optimized word has been determined.
Fig. 6 illustrates a block diagram of an apparatus for optimizing a video title according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus includes: the word segmentation module 61 is used for performing word segmentation processing on the original title of the video to obtain a word segmentation result; a first determining module 62, configured to determine a word vector corresponding to each word in the word segmentation result; a second determining module 63, configured to determine, according to the word vector corresponding to each word, a compressed vector corresponding to the original header; and a third determining module 64, configured to determine, according to the compressed vector and the word vector corresponding to each word, an optimized title corresponding to the original title.
Fig. 7 illustrates an exemplary block diagram of an apparatus for optimizing a video title according to an embodiment of the present disclosure. As shown in fig. 7:
in one possible implementation, the second determining module 63 includes: the first determining sub-module 631 is configured to determine, according to a word vector corresponding to an ith word in the word segmentation result and a hidden layer node value corresponding to an (i-1) th word, a hidden layer node value corresponding to the ith word, where i is a positive integer; the second determining submodule 632 is configured to determine a hidden node value corresponding to the last word in the word segmentation result as the compressed vector corresponding to the original header.
In one possible implementation, the third determining module 64 includes: a third determining submodule 641, configured to determine a tth hidden state value according to the compressed vector, the tth hidden state value, and the tth optimized word, where t is a positive integer; the fourth determining submodule 642 is configured to determine a tth optimized word according to the tth hidden state value, the tth-1 optimized word, and the word vector corresponding to each word; a fifth determining sub-module 643, configured to determine, according to T optimized words, an optimized title corresponding to the original title, where T is a total number of the optimized words, and T is smaller than or equal to T.
In one possible implementation, the fifth determining sub-module 643 is configured to: calculating scores corresponding to all candidate titles generated by the T optimized words; and determining the candidate title with the highest score as the optimized title corresponding to the original title.
The method and the device have the advantages that the original title of the video is subjected to word segmentation processing to obtain word segmentation results, word vectors corresponding to words in the word segmentation results are determined, compressed vectors corresponding to the original title are determined according to the word vectors corresponding to the words, and optimized titles corresponding to the original title are determined according to the compressed vectors and the word vectors corresponding to the words, so that the original title of the video can be optimized, and the click rate of the video is improved.
Fig. 8 is a block diagram illustrating an apparatus 800 for optimization of a video title according to an example embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 8, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
Fig. 9 is a block diagram illustrating an apparatus 1900 for optimization of a video title according to an example embodiment. For example, the apparatus 1900 may be provided as a server. Referring to fig. 9, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A method for optimizing a video title, comprising:
performing word segmentation processing on an original title of the video to obtain a word segmentation result;
determining word vectors corresponding to all words in the word segmentation result;
determining a compressed vector corresponding to the original title according to the word vector corresponding to each word;
determining an optimized title corresponding to the original title according to the compressed vectors and the word vectors corresponding to the words;
wherein, the determining the optimized title corresponding to the original title according to the compressed vector and the word vector corresponding to each word includes:
determining a t-th hidden state value according to the compressed vector, the t-1 th hidden state value and the t-1 th optimized word, wherein t is a positive integer;
determining a tth optimized word according to the tth hidden layer state value, the t-1 th optimized word and the word vector corresponding to each word;
and determining the optimized title corresponding to the original title according to the T optimized words, wherein T is the total number of the optimized words, and T is less than or equal to T.
2. The method of claim 1, wherein determining the compressed vector corresponding to the original header according to the word vector corresponding to each word comprises:
determining a hidden layer node value corresponding to an ith word according to a word vector corresponding to the ith word in the word segmentation result and a hidden layer node value corresponding to an i-1 word, wherein i is a positive integer;
and determining the hidden node value corresponding to the last word in the word segmentation result as the compressed vector corresponding to the original title.
3. The method of claim 1, wherein determining the optimized headings corresponding to the original headings according to the T optimized words comprises:
calculating scores corresponding to all candidate titles generated by the T optimized words;
and determining the candidate title with the highest score as the optimized title corresponding to the original title.
4. An apparatus for optimizing a video title, comprising:
the word segmentation module is used for carrying out word segmentation processing on the original title of the video to obtain a word segmentation result;
the first determining module is used for determining word vectors corresponding to all words in the word segmentation result;
a second determining module, configured to determine, according to the word vector corresponding to each word, a compressed vector corresponding to the original header;
a third determining module, configured to determine, according to the compressed vector and the word vector corresponding to each word, an optimized title corresponding to the original title;
wherein the third determining module comprises:
the third determining submodule is used for determining the tth hidden state value according to the compressed vector, the tth hidden state value and the tth optimized word, wherein t is a positive integer;
a fourth determining submodule, configured to determine a tth optimized word according to the tth hidden state value, the tth-1 optimized word, and the word vector corresponding to each word;
and the fifth determining submodule is used for determining the optimized title corresponding to the original title according to the T optimized words, wherein T is the total number of the optimized words, and T is smaller than or equal to T.
5. The apparatus of claim 4, wherein the second determining module comprises:
a first determining submodule, configured to determine, according to a word vector corresponding to an ith word in the word segmentation result and an implicit layer node value corresponding to an i-1 th word, an implicit layer node value corresponding to the ith word, where i is a positive integer;
and the second determining submodule is used for determining the hidden node value corresponding to the last word in the word segmentation result as the compressed vector corresponding to the original title.
6. The apparatus of claim 4, wherein the fifth determination submodule is configured to:
calculating scores corresponding to all candidate titles generated by the T optimized words;
and determining the candidate title with the highest score as the optimized title corresponding to the original title.
7. An apparatus for optimizing a video title, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 3.
8. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 3.
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| AU2003228455A1 (en) * | 2002-04-04 | 2003-10-27 | Sonic Solutions | Optimizing the recording on a rewritable interactive medium of revisions to an existing project on that medium |
| CN103544264A (en) * | 2013-10-17 | 2014-01-29 | 常熟市华安电子工程有限公司 | Commodity title optimizing tool |
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| US9143823B2 (en) * | 2012-10-01 | 2015-09-22 | Google Inc. | Providing suggestions for optimizing videos to video owners |
| CN106502985B (en) * | 2016-10-20 | 2020-01-31 | 清华大学 | neural network modeling method and device for generating titles |
| CN106557563B (en) * | 2016-11-15 | 2020-09-25 | 北京百度网讯科技有限公司 | Query statement recommendation method and device based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| AU2003228455A1 (en) * | 2002-04-04 | 2003-10-27 | Sonic Solutions | Optimizing the recording on a rewritable interactive medium of revisions to an existing project on that medium |
| CN103544264A (en) * | 2013-10-17 | 2014-01-29 | 常熟市华安电子工程有限公司 | Commodity title optimizing tool |
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