WO2020155769A1 - Procédé et dispositif d'établissement d'un modèle de génération de mots-clés - Google Patents

Procédé et dispositif d'établissement d'un modèle de génération de mots-clés Download PDF

Info

Publication number
WO2020155769A1
WO2020155769A1 PCT/CN2019/118329 CN2019118329W WO2020155769A1 WO 2020155769 A1 WO2020155769 A1 WO 2020155769A1 CN 2019118329 W CN2019118329 W CN 2019118329W WO 2020155769 A1 WO2020155769 A1 WO 2020155769A1
Authority
WO
WIPO (PCT)
Prior art keywords
text
sample
keyword
model
target
Prior art date
Application number
PCT/CN2019/118329
Other languages
English (en)
Chinese (zh)
Inventor
王健宗
贾雪丽
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2020155769A1 publication Critical patent/WO2020155769A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities

Definitions

  • This application relates to the field of intelligent decision-making, and more specifically, to a method and device for modeling keyword generation models in the field of intelligent decision-making.
  • a key word is a short summary content that expresses the main semantic meaning of a longer text.
  • the typical usage of keywords is to provide the core information of the paper in scientific publications. High-quality key phrases can help understand, organize, and access document content.
  • the first idea is to use statistical information, typically the TF-IDF method, which determines the criticality of words by calculating the frequency of occurrence of words in the text and the frequency of occurrence in the entire document library;
  • the second idea is to use semantic information
  • the keygraph algorithm finds the clusters of words in the text by establishing the graphical representation of the text, so as to obtain the words that best represent the text as keywords.
  • the RNN recurrent neural network
  • the Encoder-Decoder model provides a powerful tool for modeling variable length sentences, and has a wide range of applications in the field of natural language processing.
  • the present application provides a modeling method and device for a keyword generation model, which can establish a model for effectively extracting text keywords.
  • this application provides a method for modeling a keyword generation model, including the following content:
  • sample data including sample text and M i sample keywords of the sample text, the sample text including and each sample keyword is a sequence of words, and M i is an integer greater than 1;
  • the text of the sample and the samples keywords M i determining the sample data corresponding to text M i - of keywords, the text M i - keywords of the i th text - Image including the sample text keywords and the i-th sample, i is greater than 0 and less than or equal to M i is an integer;
  • M i according to the text - and keywords of the encoder - decoder model, a model generating the keyword, wherein said coder - decoder model model includes an encoder and a decoder model, the keyword
  • the generative model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keywords.
  • this application provides a method for generating text keywords, including the following content:
  • sample data including sample text and M i sample keywords of the sample text, the sample text including and each sample keyword is a sequence of words, and M i is an integer greater than 1;
  • the text of the sample and the samples keywords M i determining the sample data corresponding to text M i - of keywords, the text M i - keywords of the i th text - Image including the sample text keywords and the i-th sample, i is greater than 0 and less than or equal to M i is an integer;
  • M i according to the text - and keywords of the encoder - decoder model, a model generating the keyword, wherein said coder - decoder model model includes an encoder and a decoder model, the keyword
  • the generative model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keywords.
  • Target text where the target text is a sequence of words
  • a target keyword of the target text is generated.
  • this application also provides a modeling device for a keyword generation model, which specifically includes:
  • An acquisition unit configured to acquire the sample data, the sample includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, greater than 1 M i Integer
  • Determining means for, according to the text sample and the samples keywords M i, determining the sample data corresponding to text M i - of keywords, the text M i - i-th keyword pair text - text keywords including the sample and the i-th sample keywords, i is greater than 0 and less than or equal to M i is an integer;
  • the keyword generation model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keyword.
  • this application also provides a device for generating text keywords, which specifically includes:
  • An acquisition unit configured to acquire the sample data, the sample includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, greater than 1 M i Integer
  • Determining means for, according to the text sample and the samples keywords M i, determining the sample data corresponding to text M i - of keywords, the text M i - i-th keyword pair text - text keywords including the sample and the i-th sample keywords, i is greater than 0 and less than or equal to M i is an integer;
  • the keyword generation model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keyword.
  • the acquiring unit is also used to acquire target text, where the target text is a sequence of words;
  • the generating unit is configured to generate the target keyword of the target text according to the target text and the keyword generation model.
  • the present application also provides a computer device, including a memory, a processor, a communication interface, and a computer program stored in the memory and running on the processor, wherein the memory, the The processor and the communication interface communicate with each other through an internal connection path, and the processor implements the following steps when executing the computer program:
  • sample data includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is an integer greater than 1;
  • the text of the sample and the samples keywords M i determining the sample data corresponding to text M i - of keywords, the text M i - keywords of the i th text - Image including the sample text keywords and the i-th sample, i is greater than 0 and less than or equal to M i is an integer;
  • M i according to the text - and keywords of the encoder - decoder model, a model generating the keyword, wherein said coder - decoder model model includes an encoder and a decoder model, the keyword
  • the generative model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keywords; and/or,
  • Target text where the target text is a sequence of words
  • a target keyword of the target text is generated.
  • the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • sample data includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is an integer greater than 1;
  • the text of the sample and the samples keywords M i determining the sample data corresponding to text M i - of keywords, the text M i - keywords of the i th text - Image including the sample text keywords and the i-th sample, i is greater than 0 and less than or equal to M i is an integer;
  • M i according to the text - and keywords of the encoder - decoder model, a model generating the keyword, wherein said coder - decoder model model includes an encoder and a decoder model, the keyword
  • the generative model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keywords; and/or,
  • Target text where the target text is a sequence of words
  • a target keyword of the target text is generated.
  • FIG. 1 is a schematic flowchart of a method for modeling a keyword generation model provided by an embodiment of the present application
  • FIG. 2 is a schematic flowchart of a method for generating text keywords provided by an embodiment of the present application
  • FIG. 3 is a schematic block diagram of a modeling device for a keyword generation model provided by an embodiment of the present application.
  • FIG. 4 is a schematic block diagram of a device for generating text keywords provided by an embodiment of the present application
  • FIG. 5 is a schematic block diagram of another keyword generation model modeling apparatus provided by an embodiment of the present application.
  • Fig. 6 is a schematic block diagram of another apparatus for generating text keywords according to an embodiment of the present application.
  • FIG. 1 shows a schematic flowchart of a method 100 for modeling a keyword generation model provided by an embodiment of the present application. It should be understood that the method 100 can be executed by a modeling device of a keyword generation model.
  • the device may be a device with a computing function and a communication interface, for example, a mobile terminal.
  • the device may be a functional module in the mobile terminal.
  • the method 100 includes the following steps:
  • sample data includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is an integer greater than 1;
  • text keyword data set includes several "text-keyword” pairs for training the model, where text refers to the title and abstract of the publication, and keywords refer to keywords assigned by the author of the publication.
  • the method further includes: acquiring a target text, the target text being a sequence of words; and generating a target keyword of the target text according to the target text and the keyword generation model.
  • generating the target keywords of the target text according to the target text and the keyword generation model includes: encoding the target text according to the encoder model to obtain a hidden expression Formula; According to the hidden expression and the preset nonlinear function, the context vector is obtained; the context vector is decoded according to the decoder model to obtain the target keyword.
  • the keyword generation model in the embodiment of the present application may be a variety of deep learning models, which is not limited in the embodiment of the present application.
  • both the encoder model and the decoder model are RNN (recursive neural network).
  • a given keyword data set comprising N data samples, wherein, the i-th sample data (x (i), P (i)), comprising a text sample x (i) and M i samples Key words
  • the sample text x (i) and the sample keywords p (i, j) are both word sequences, as shown in formula (1) and formula (2):
  • L x (i) and L p (i, j) represent the length of the word sequence x (i) and p (i, j) , respectively.
  • (x, y) is used to represent sample data below, where x represents sample text and y represents sample keywords.
  • the basic idea of the keyword generation model is to compress the content of the word sequence of the sample text into a hidden representation with an encoder model, and based on the hidden representation and the decoder model to generate the corresponding word sequence of the sample keywords.
  • the following formula (4) is obtained by iterating along time t:
  • formula (4) is a non-linear equation.
  • the context vector c is obtained as a representation of the entire input x through the non-linear function q.
  • the context vector c is shown in formula (5):
  • s t f(y t-1 ,s t-1 ,c), p(y t
  • y 1,...,t-1 ,x) g(y t-1 ,s t ,c ), st is the hidden state of the decoder model at time t, and the nonlinear function g is a softmax classifier, which outputs the probability of all words in the vocabulary.
  • y t is the predicted word at time t, usually the word with the highest probability after g( ⁇ ).
  • the encoder model and decoder model network are jointly trained to maximize the conditional probability of the target sequence. After training, use beam search to generate keywords, and reserve the largest heap to obtain the predicted keyword sequence with the highest probability.
  • the encoder model is a GRU
  • the decoding model is a forward GRU
  • GRU bidirectional gated recurrent unit
  • GRU can be used as an encoder to replace a simple RNN.
  • LSTM long short-term memory, long-term short-term memory networks
  • it is usually It can provide better language modeling performance than simple RNN and simpler structure. Therefore, GRU can be used to replace the aforementioned f function, and the forward GRU is used as a decoder.
  • an attention mechanism is used to improve performance. The attention mechanism makes the model dynamically pay attention to the important part of the input.
  • generating the target keywords of the target text according to the target text and the keyword generation model includes: encoding the target text according to the encoder model , Obtain a hidden expression; obtain a context vector according to the hidden expression and the weight of each word sequence in the hidden expression; decode the context vector according to the decoder model to obtain the target key word.
  • a(s i-1 ,h j ) is a soft alignment function used to measure the similarity between s i-1 and h j , that is, the degree to which the input around position j matches the output at position i.
  • the RNN model usually considers a certain number of frequent words, but a large number of long-tail keywords may be ignored, that is, the RNN cannot recall any words that contain extra-word words. Key words.
  • a replication mechanism can be used to enable RNN to predict words other than vocabulary words by selecting appropriate words from the text.
  • long tail keyword is a combination keyword that is not a target keyword but is related to the target keyword and has the same or similar meaning.
  • car is the target keyword
  • “convertible car” is a long-tail keyword developed based on the characteristics of the product.
  • hand cream as the target keyword
  • hand cream direct sales, hand cream group purchase, hand cream online shopping are all long-tail keywords based on the sales model.
  • weight loss as the target keyword, "I want to lose weight, what kind of medicine to lose weight” are long-tail keywords that are expanded through Internet users' search intentions.
  • the probability of predicting each new word y t consists of two parts.
  • the first term is the probability of generating the word
  • the second term is the probability of copying it from the target text, as shown in formula (7):
  • ⁇ is the set of all unique words in the text x
  • ⁇ is a nonlinear function
  • W c ⁇ R is the learning parameter matrix
  • z is the sum of all scores, used for normalization.
  • the replication mechanism weights the importance of each word in the target text with the measure of position attention. But unlike the generated RNN that predicts the next word of all words in the vocabulary, the copy part p c (y t
  • RNN with a copy mechanism can predict words whose etymology is beyond the vocabulary but in the target text; on the other hand, the model may give priority to the words that appear, and these words cater to most keywords. The facts that appear in the target text.
  • FIG. 2 shows a schematic flowchart of a method 200 for generating text keywords provided by an embodiment of the present application.
  • the method 200 includes the following steps:
  • sample data includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is an integer greater than 1.
  • S240 Acquire target text, where the target text is a sequence of words.
  • FIG. 3 shows a schematic block diagram of an apparatus 300 for modeling a keyword generation model provided by an embodiment of the present application.
  • the device 300 includes:
  • Obtaining unit 310 for obtaining the sample data the sample includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is greater than 1 Integer
  • Determination unit 320 determines the sample data corresponding to text M i - of keywords, the text M i - a first pair of keywords i text - text keywords including the sample and the i-th sample keywords, i is greater than 0 and less than or equal to M i is an integer;
  • the keyword generation model is used to represent the mapping relationship between the word sequence in the sample text and the word sequence in the sample keyword.
  • the device further includes a generating unit; the obtaining unit is also used to obtain target text, and the target text is a sequence of words; the generating unit is used to obtain the target text according to the target text and the The keyword generation model generates the target keywords of the target text.
  • the generating unit is specifically configured to encode the target text according to the encoder model to obtain a hidden expression; according to the hidden expression and a preset nonlinear function, obtain Context vector; decode the context vector according to the decoder model to obtain the target keyword.
  • the generating unit is specifically configured to encode the target text according to the encoder model to obtain a hidden expression; according to each of the hidden expression and the hidden expression The weight of the word sequence obtains a context vector; the context vector is decoded according to the decoder model to obtain the target keyword.
  • both the encoder model and the decoder model are RNNs.
  • the encoder model is a GRU
  • the decoder model is a forward GRU
  • FIG. 4 shows a schematic block diagram of an apparatus 400 for generating text keywords according to an embodiment of the present application.
  • the device 400 includes:
  • Obtaining unit 410 for obtaining the sample data the sample includes a sample of text data and the sample text keywords M i samples, and each sample of the sample text keywords are the words in the sequence, M i is greater than 1 Integer
  • Determination unit 420 determines the sample data corresponding to text M i - of keywords, the text M i - a first pair of keywords i text - text keywords including the sample and the i-th sample keywords, i is greater than 0 and less than or equal to M i is an integer;
  • the acquiring unit 410 is also configured to acquire target text, and the target text is a sequence of words.
  • the generating unit 440 is configured to generate target keywords of the target text according to the target text and the keyword generation model.
  • the generating unit is specifically configured to encode the target text according to the encoder model to obtain a hidden expression; according to the hidden expression and a preset nonlinear function, obtain Context vector; decode the context vector according to the decoder model to obtain the target keyword.
  • the generating unit is specifically configured to encode the target text according to the encoder model to obtain a hidden expression; according to each of the hidden expression and the hidden expression The weight of the word sequence obtains a context vector; the context vector is decoded according to the decoder model to obtain the target keyword.
  • both the encoder model and the decoder model are RNNs.
  • the encoder model is a GRU
  • the decoder model is a forward GRU
  • FIG. 5 shows a schematic block diagram of an apparatus 500 for modeling a keyword generation model provided by an embodiment of the present application.
  • the device 500 may be the device 300 described in FIG. 3, and the device 500 may adopt the hardware architecture shown in FIG.
  • the device 500 may include a processor 510, a communication interface 520, and a memory 530.
  • the processor 510, the communication interface 520, and the memory 530 communicate with each other through an internal connection path.
  • the related functions implemented by the determining unit 320 and the establishing unit 330 in FIG. 3 may be implemented by the processor 510.
  • the related functions implemented by the acquiring unit 310 in FIG. 3 may be implemented by the processor 510 controlling the communication interface 520.
  • the processor 510 may include one or more processors, for example, include one or more central processing units (central processing units, CPUs).
  • CPUs central processing units
  • the processor may be a single-core CPU or It can be a multi-core CPU.
  • the communication interface 520 is used to input and/or output data.
  • the communication interface may include a sending interface and a receiving interface, the sending interface is used for outputting data, and the receiving interface is used for inputting data.
  • the memory 530 includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable memory (erasable read only memory, EPROM), read-only memory A compact disc (read-only memory, CD-ROM).
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable memory
  • read-only memory A compact disc read-only memory, CD-ROM.
  • the memory 530 is used to store related instructions and data.
  • the memory 530 is used to store program codes and data of the device, and may be a separate device or integrated in the processor 510.
  • the processor 510 is configured to control the communication interface 520 to call the code instructions stored in the memory 530 and execute the code instructions.
  • the processor 510 is configured to control the communication interface 520 to call the code instructions stored in the memory 530 and execute the code instructions.
  • Figure 5 only shows a simplified design of the device.
  • the device 500 may also include other necessary components, including but not limited to any number of communication interfaces, processors, controllers, memories, etc., and all devices that can implement the application are protected by the application. Within range.
  • the device 500 may be replaced with a chip device, for example, a chip that can be used in the device to implement related functions of the processor 510 in the device.
  • the chip device can be a field programmable gate array, a dedicated integrated chip, a system chip, a central processing unit, a network processor, a digital signal processing circuit, a microcontroller, and a programmable controller or other integrated chips for realizing related functions.
  • the chip may optionally include one or more memories for storing program codes, and when the codes are executed, the processor realizes corresponding functions.
  • FIG. 6 shows a schematic block diagram of an apparatus 600 for generating text keywords according to an embodiment of the present application.
  • the device 600 may be the device 400 described in FIG. 4, and the device 600 may adopt the hardware architecture shown in FIG.
  • the device 600 may include a processor 610, a communication interface 620, and a memory 630, and the processor 610, the communication interface 620, and the memory 630 communicate with each other through an internal connection path.
  • the related functions implemented by the determining unit 420, the establishing unit 430, and the generating unit 440 in FIG. 4 may be implemented by the processor 610.
  • the related functions implemented by the acquiring unit 410 in FIG. 4 may be implemented by the processor 610 controlling the communication interface 620.
  • the processor 610 may include one or more processors, such as one or more central processing units (CPU).
  • processors such as one or more central processing units (CPU).
  • CPU central processing units
  • the CPU may be a single-core CPU, or It can be a multi-core CPU.
  • the communication interface 620 is used to input and/or output data.
  • the communication interface may include a sending interface and a receiving interface, the sending interface is used for outputting data, and the receiving interface is used for inputting data.
  • the memory 630 includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable memory (erasable read only memory, EPROM), read-only memory A compact disc (read-only memory, CD-ROM).
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable memory
  • read-only memory A compact disc read-only memory, CD-ROM.
  • the memory 630 is used to store related instructions and data.
  • the memory 630 is used to store program codes and data of the device, and may be a separate device or integrated in the processor 610.
  • the processor 610 is configured to control the communication interface 620 to call the code instructions stored in the memory 630 and execute the code instructions.
  • the processor 610 is configured to control the communication interface 620 to call the code instructions stored in the memory 630 and execute the code instructions.
  • Fig. 6 only shows a simplified design of the device.
  • the device 600 may also include other necessary elements, including but not limited to any number of communication interfaces, processors, controllers, memories, etc., and all devices that can implement the application are protected by the application. Within range.
  • the device 600 can be replaced with a chip device, for example, a chip that can be used in the device to implement related functions of the processor 610 in the device.
  • the chip device can be a field programmable gate array, a dedicated integrated chip, a system chip, a central processing unit, a network processor, a digital signal processing circuit, a microcontroller, and a programmable controller or other integrated chips for realizing related functions.
  • the chip may optionally include one or more memories for storing program codes, and when the codes are executed, the processor realizes corresponding functions.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Machine Translation (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Procédé et dispositif d'établissement d'un modèle de génération de mots-clés. Le procédé comporte les étapes consistant à: obtenir des données d'échantillon, les données d'échantillon comportant un texte d'échantillon et Mi mots-clés d'échantillon du texte d'échantillon (S110); déterminer, d'après le texte d'échantillon et les Mi mots-clés d'échantillon, Mi paires texte/mot-clé correspondant aux données d'échantillon, la i-ème paire texte/mot-clé parmi les Mi paires texte/mot-clé comportant le texte d'échantillon et le i-ème mot-clé d'échantillon (S120); et établir un modèle de génération de mots-clés d'après les Mi paires texte/mot-clé et un modèle codeur-décodeur, le modèle codeur-décodeur comportant un modèle codeur et un modèle décodeur, et le modèle de génération de mots-clés étant utilisé pour représenter la relation d'association entre une suite de mots figurant dans le texte d'échantillon et des suites de mots figurant dans les mots-clés d'échantillon (S130). Le procédé et le dispositif d'établissement d'un modèle de génération de mots-clés peuvent établir un modèle capable d'extraire efficacement des mots-clés de texte.
PCT/CN2019/118329 2019-01-30 2019-11-14 Procédé et dispositif d'établissement d'un modèle de génération de mots-clés WO2020155769A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910090894.0A CN109902273B (zh) 2019-01-30 2019-01-30 关键词生成模型的建模方法和装置
CN201910090894.0 2019-01-30

Publications (1)

Publication Number Publication Date
WO2020155769A1 true WO2020155769A1 (fr) 2020-08-06

Family

ID=66944415

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/118329 WO2020155769A1 (fr) 2019-01-30 2019-11-14 Procédé et dispositif d'établissement d'un modèle de génération de mots-clés

Country Status (2)

Country Link
CN (1) CN109902273B (fr)
WO (1) WO2020155769A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109902273B (zh) * 2019-01-30 2024-05-07 平安科技（深圳）有限公司 关键词生成模型的建模方法和装置
CN111191428B (zh) * 2019-12-27 2022-02-25 北京百度网讯科技有限公司 评论信息处理方法、装置、计算机设备和介质
CN114692613A (zh) * 2020-12-29 2022-07-01 阿里巴巴集团控股有限公司 一种关键词抽取方法及装置和编码器和解码器

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107783960A (zh) * 2017-10-23 2018-03-09 百度在线网络技术（北京）有限公司 用于抽取信息的方法、装置和设备
CN108334489A (zh) * 2017-01-19 2018-07-27 百度在线网络技术（北京）有限公司 文本核心词识别方法和装置
CN108376131A (zh) * 2018-03-14 2018-08-07 中山大学 基于seq2seq深度神经网络模型的关键词抽取方法
CN108763213A (zh) * 2018-05-25 2018-11-06 西南电子技术研究所（中国电子科技集团公司第十研究所） 主题特征文本关键词提取方法
CN109255118A (zh) * 2017-07-11 2019-01-22 普天信息技术有限公司 一种关键词提取方法及装置
CN109902273A (zh) * 2019-01-30 2019-06-18 平安科技（深圳）有限公司 关键词生成模型的建模方法和装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106095749A (zh) * 2016-06-03 2016-11-09 杭州量知数据科技有限公司 一种基于深度学习的文本关键词提取方法
CN108304365A (zh) * 2017-02-23 2018-07-20 腾讯科技（深圳）有限公司 关键词提取方法及装置
CN107862058B (zh) * 2017-11-10 2021-10-22 北京百度网讯科技有限公司 用于生成信息的方法和装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108334489A (zh) * 2017-01-19 2018-07-27 百度在线网络技术（北京）有限公司 文本核心词识别方法和装置
CN109255118A (zh) * 2017-07-11 2019-01-22 普天信息技术有限公司 一种关键词提取方法及装置
CN107783960A (zh) * 2017-10-23 2018-03-09 百度在线网络技术（北京）有限公司 用于抽取信息的方法、装置和设备
CN108376131A (zh) * 2018-03-14 2018-08-07 中山大学 基于seq2seq深度神经网络模型的关键词抽取方法
CN108763213A (zh) * 2018-05-25 2018-11-06 西南电子技术研究所（中国电子科技集团公司第十研究所） 主题特征文本关键词提取方法
CN109902273A (zh) * 2019-01-30 2019-06-18 平安科技（深圳）有限公司 关键词生成模型的建模方法和装置

Also Published As

Publication number Publication date
CN109902273B (zh) 2024-05-07
CN109902273A (zh) 2019-06-18

Similar Documents

Publication Publication Date Title
US11900056B2 (en) Stylistic text rewriting for a target author
US11816442B2 (en) Multi-turn dialogue response generation with autoregressive transformer models
JP7406606B2 (ja) テキスト認識モデルの訓練方法、テキスト認識方法及び装置
JP7098698B2 (ja) テキスト要約システム、方法、コンピュータ・プログラム及び記憶媒体
CN107836000B (zh) 用于语言建模和预测的改进的人工神经网络方法、电子设备
US11113479B2 (en) Utilizing a gated self-attention memory network model for predicting a candidate answer match to a query
US11216510B2 (en) Processing an incomplete message with a neural network to generate suggested messages
US11468239B2 (en) Joint intent and entity recognition using transformer models
CN112487182A (zh) 文本处理模型的训练方法、文本处理方法及装置
CN110134971B (zh) 一种机器翻译的方法、设备以及计算机可读存储介质
WO2019118256A1 (fr) Génération de texte à partir de données structurées
WO2020155769A1 (fr) Procédé et dispositif d'établissement d'un modèle de génération de mots-clés
WO2023134083A1 (fr) Procédé et appareil de classification de sentiments basée sur texte, dispositif informatique et support de stockage
CN110263218B (zh) 视频描述文本生成方法、装置、设备和介质
JP2023002690A (ja) セマンティックス認識方法、装置、電子機器及び記憶媒体
Wang et al. Data augmentation for internet of things dialog system
CN112711943B (zh) 一种维吾尔文语种识别方法、装置及存储介质
CN114417823A (zh) 一种基于句法和图卷积网络的方面级情感分析方法及装置
WO2023137903A1 (fr) Procédé et appareil de détermination de déclaration de réponse basés sur une sémantique grossière, et dispositif électronique
CN115270719A (zh) 基于多模态信息的文本摘要生成方法、训练方法及其装置
CN110442706B (zh) 一种文本摘要生成的方法、系统、设备及存储介质
CN116306612A (zh) 一种词句生成方法及相关设备
CN115982343B (zh) 摘要生成方法、训练摘要生成模型的方法及装置
US12032910B2 (en) Joint intent and entity recognition using transformer models
CN113284498B (zh) 客户意图识别方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19913599

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19913599

Country of ref document: EP

Kind code of ref document: A1