WO2020215683A1 - Procédé et appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi que support de stockage lisible non volatil et dispositif informatique - Google Patents

Procédé et appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi que support de stockage lisible non volatil et dispositif informatique Download PDF

Info

Publication number
WO2020215683A1
WO2020215683A1 PCT/CN2019/117723 CN2019117723W WO2020215683A1 WO 2020215683 A1 WO2020215683 A1 WO 2020215683A1 CN 2019117723 W CN2019117723 W CN 2019117723W WO 2020215683 A1 WO2020215683 A1 WO 2020215683A1
Authority
WO
WIPO (PCT)
Prior art keywords
convolutional neural
neural network
loss function
named entity
text
Prior art date
Application number
PCT/CN2019/117723
Other languages
English (en)
Chinese (zh)
Inventor
金戈
徐亮
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 WO2020215683A1 publication Critical patent/WO2020215683A1/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
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G06F40/295—Named entity recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • This application relates to the technical field of processing herein, in particular to a method and device for semantic recognition based on convolutional neural networks, non-volatile readable storage media, and computer equipment.
  • the disadvantage of the prior art is that the two independent recognition models used to realize named entity recognition and entity relationship recognition are prone to information redundancy in the process of joint use.
  • the current solution is only It is limited to partially combining the above two independent recognition models based on the cyclic neural network to increase the calculation rate of the network model, thereby improving the efficiency of named entity recognition and entity relationship recognition, but the improvement effect is weak.
  • this application provides a semantic recognition method and device based on a convolutional neural network, a non-volatile readable storage medium, and computer equipment.
  • the main purpose is to solve the existing two independent methods for named entity recognition and entity relationship recognition.
  • information redundancy is easy to exist between each other, and the calculation rate of the adopted network model is low.
  • a semantic recognition method based on a convolutional neural network including:
  • the third convolutional neural network preset in the semantic recognition model is used to determine the entity relationship in the text to be recognized according to the obtained text vector and the determined named entity.
  • a semantic recognition device based on a convolutional neural network including:
  • the first convolutional neural network module is used to obtain the text vector of the text to be recognized by using the first convolutional neural network preset in the semantic recognition model;
  • the second convolutional neural network module is configured to use the second convolutional neural network preset in the semantic recognition model to determine the named entity in the text to be recognized according to the obtained text vector;
  • the third convolutional neural network module is used to use the preset third convolutional neural network in the semantic recognition model to determine the entity relationship in the text to be recognized according to the obtained text vector and the determined named entity.
  • a non-volatile readable storage medium having computer readable instructions stored thereon, and the program is executed by a processor to realize the above-mentioned semantic recognition method based on convolutional neural network.
  • a computer device including a non-volatile readable storage medium, a processor, and computer readable instructions stored on the non-volatile readable storage medium and running on the processor ,
  • the processor executes the program, the above semantic recognition method based on the convolutional neural network is realized.
  • the convolutional neural network-based semantic recognition method and device, non-volatile readable storage medium, and computer equipment provided in this application will be used for named entity recognition and entity relationship with existing recurrent neural networks.
  • this application uses the first convolutional neural network preset in the semantic recognition model to obtain the text vector of the text to be recognized, and uses the preset first convolutional neural network in the semantic recognition model.
  • the second convolutional neural network determines the named entity in the text to be recognized according to the acquired text vector, and uses the third convolutional neural network preset in the semantic recognition model, according to the acquired text vector and the determined named entity, Determine the entity relationship in the text to be recognized.
  • FIG. 1 shows a schematic flowchart of a semantic recognition method based on a convolutional neural network provided by an embodiment of the present application
  • FIG. 2 shows a schematic flowchart of another semantic recognition method based on a convolutional neural network provided by an embodiment of the present application
  • Fig. 3 shows a schematic structural diagram of a semantic recognition device based on a convolutional neural network provided by an embodiment of the present application.
  • the preprocessing can be specifically set according to the actual application scenario, for example, the preprocessing is set as word segmentation processing, that is, the text to be recognized is marked with words as the unit; or the preprocessing is set as word filtering processing, that is, After the word segmentation is performed on the text to be recognized, unimportant words are eliminated, such as auxiliary verbs such as "can, should", and unimportant words such as interjections such as "oh, ah", to improve the semantic recognition of the text to be recognized The efficiency is not specifically limited here.
  • the specific word segmentation processing of the text to be recognized is to use the SBME notation method to mark the words in the text to be recognized, that is, to mark the word as S and the beginning of the word as B.
  • the middle part of the word is marked as M
  • the end of the word is marked as E
  • the initial text vector is generated according to the marked text to be recognized.
  • the training sample set includes multiple phrase corpora.
  • the phrase corpus is in a short sentence format, that is, a short sentence is divided into a comma.
  • each phrase corpus includes two interrelated words, for example, "China, Shanghai” , And mark the relationship between the two words in each phrase corpus, for example, mark the relationship between the words "China, Shanghai” as the upper and lower relationship to construct a training sample set.
  • the relationship between two words in the phrase corpus can be set in various ways. For example, mark the relationship between “Copyright Office and Trademark Office” as a parallel relationship, and mark “Copyright Office, Trademark Office” in The term attribute of the Copyright Office and the Trademark Office of is a national institution; the relationship between the mark “Canine family, dog” is an inclusive relationship, and the word attribute of canine family and dog in the mark “Canine family, dog” is animal, etc., here There is no specific limitation on the mutual relationship.
  • the preset second convolutional neural network is used to identify the named entities contained in the text to be recognized, the output result of the preset first convolutional neural network is used as the input of the preset second convolutional neural network, and the preset is input
  • the output of the second convolutional neural network is the named entity contained in the text to be recognized.
  • the text to be recognized includes multiple words, and a named entity or named entity category is output for each word.
  • the named entity category includes person name, place name, organization name, Named entity categories such as product names and proper nouns.
  • the preset third convolutional neural network is used to identify the entity relationship contained in the text to be recognized, and the preset output results of the first convolutional neural network and the preset second convolutional neural network are used as presets
  • the input of the third convolutional neural network is input to the preset third convolutional neural network, and the output result is the entity relationship between the named entities contained in the text to be recognized.
  • the preset number of named entities output by the second convolutional neural network is two or three
  • the preset third convolutional neural network uses the preset third convolutional neural network to output two or three
  • the entity relationship between two named entities because the text to be recognized is in short sentence format, and the preset third convolutional neural network is only used for the recognition of the relationship between a small number of named entities, so that the recognition efficiency of the text to be recognized is obtained Significant improvement.
  • the acquired text to be recognized can be hierarchically recognized according to the constructed semantic recognition model, and different convolutional neural networks in the semantic recognition model can be used to realize named entities and entity relationships in the text to be recognized.
  • this embodiment can not only improve the recognition efficiency of the text to be recognized, Avoid the information redundancy problem caused by the joint use of the existing two independent recognition models.
  • the application scenarios of this embodiment are more broad, that is, it can be simultaneously applied to the recognition of named entities alone, the recognition of entity relationships alone, and At the same time, for the application scenarios of named entity and entity relationship recognition, there is no need to build different semantic recognition models for different needs.
  • the maintenance and optimization of the later model reduce the cost, and it does not affect the model at all while reducing the cost. Semantic recognition efficiency and semantic recognition accuracy.
  • this method include:
  • the loss functions of the second and third convolutional neural networks are constructed based on cross entropy.
  • the second The loss function of the convolutional neural network is the cross entropy used to identify named entities
  • the loss function of the third convolutional neural network is the cross entropy used to identify the relationship.
  • the first loss function, the second loss function, and the third loss function can be set differently according to the initialized first convolutional neural network, second convolutional neural network, and third convolutional neural network.
  • the same loss function can also be used.
  • the first loss function, the second loss function, and the third loss function are not specifically set here.
  • the first loss function and the second loss function are set Same as the third loss function, the calculation formula is:
  • x is the data sample in the sample set of the first convolutional neural network, the second convolutional neural network and the third convolutional neural network used for training initialization
  • p and q are the true probability distributions of the sample set respectively, not true Probability distributions.
  • the determined first loss function, second loss function, and third loss function train the initialized first convolutional neural network, second convolutional neural network, and third convolutional neural network to obtain a preset The first convolutional neural network, the second convolutional neural network and the third convolutional neural network.
  • step 202 may specifically include: determining the loss function of the semantic recognition model according to the determined first loss function, second loss function, and third loss function ; Use the loss function of the semantic recognition model to train the initialized first convolutional neural network, second convolutional neural network, and third convolutional neural network to obtain the preset first convolutional neural network and second convolutional neural network Network and third convolutional neural network.
  • the determined first loss function, second loss function, and third loss function are added and averaged to obtain the loss function of the semantic recognition model. Further, if the actual application scenario is If the number of named entities in the text to be recognized is large, the weight value of the second loss function should be increased accordingly. If there are more entity relationships in the text to be recognized in the actual application scenario, the weight value of the third loss function should be increased accordingly. There is no specific limitation on the calculation method of the loss function of the semantic recognition model.
  • the convex optimization algorithm is used to automatically update the network parameters in the hidden layer of the neural network.
  • the preset first convolutional neural network, second convolutional neural network, and third convolutional neural network are obtained.
  • convex optimization algorithm also known as convex optimization algorithm, or convex minimization algorithm, is a sub-field of mathematical optimization, which uses the idea of local optimal value that is global optimal value to update the network parameters in the hidden layer of neural network .
  • the adaptive moment estimation (Adam: Adaptive Moment Estimation) optimization algorithm is a first-order optimization algorithm that can replace the traditional stochastic gradient descent process.
  • the Adam optimization algorithm is used to update the network parameters in the hidden layer of the neural network .
  • Python's tensorflow library the loss function of the semantic recognition model is optimized by convex function. Specifically, the loss function is minimized as the goal, and the Adam optimization algorithm is used to iteratively update the network parameters in the semantic recognition model to obtain the preset
  • the first convolutional neural network, the second convolutional neural network and the third convolutional neural network specifically limits the number of convolutional layers in the semantic recognition model.
  • the specific training process is to compare the named entity recognition result output by the second convolutional neural network with the named entity or labeled word attributes in the training sample set. If the comparison results are inconsistent, it means that the recognition is wrong; and , According to the named entity recognition result output by the second convolutional neural network, compare the entity relationship recognition result output by the third convolutional neural network with the entity relationship marked by the corresponding output named entity recognition result in the training sample set. If the comparison results are inconsistent , It means the recognition error.
  • the loss function of the semantic recognition model is used to correct the erroneous recognition results, and then complete the training of the semantic recognition model, and obtain a semantic recognition model capable of simultaneously performing named entity recognition and entity relationship recognition.
  • the initialized text vector is obtained by performing word segmentation processing on the acquired text to be recognized, and the initialized text vector is used as the input of the first convolutional neural network preset in the semantic recognition model.
  • the preset embedding layer of the first convolutional neural network uses a preset word vector dictionary to convert the initialized text vector into a word vector and a word vector for representing the text to be recognized.
  • the preset word vector dictionary contains the word vector corresponding to each word in the initialized text vector and the word vector corresponding to each word.
  • the preset first convolutional neural network includes a double-layer one-dimensional full convolution structure, and the word vectors and word vectors from the embedding layer pass through the double-layer one-dimensional full convolution structure to output the text vector of the text to be recognized.
  • the convolution kernel is used to perform a convolution operation (ie, dot multiplication) with the word vector and word vector of the text to be recognized, and all the obtained convolution operation results are used as the text vector of the text to be recognized.
  • set the length of the convolution kernel to 3, that is, use the convolution kernel with dimension 3 to perform convolution operation with the word vector and word vector of the text to be recognized, and use the text vector of the text to be recognized as the preset The input of the second convolutional neural network and the preset third convolutional neural network.
  • the preset first convolutional neural network is a shared network structure of the preset second convolutional neural network and the preset third convolutional neural network, thereby realizing the preset second convolutional neural network and the preset
  • the sharing of the underlying parameters in the third convolutional neural network effectively avoids the information redundancy problem caused by the joint use of the existing two independent recognition models, and further improves the efficiency of semantic recognition.
  • the second convolutional neural network preset in the semantic recognition model is used to perform named entity recognition (NER: Named Entity Recognition) on the obtained text vector to obtain the named entity to be determined.
  • named entity recognition is also called “proprietary name recognition”, which refers to the recognition of entities with specific meaning in the text to be recognized.
  • the preset second convolutional neural network is a dense connection structure DenseNet.
  • the dense connection structure has a large number of dense connections, which can maximize the information flow between all layers in the neural network.
  • the input of each layer of the neural network is the union of the output of all the previous layers, and the feature map output by this layer will also be directly passed to all subsequent layers as input, so as to realize the repeated use of features and reduce redundancy.
  • the preset second convolutional neural network includes a two-layer convolution structure, and further convolution operations are performed on the convolution operation result output by the preset first convolutional neural network in the semantic recognition model based on the two-layer convolution structure. , Get the named entity to be determined.
  • the preset convolution structure in the second convolutional neural network is a one-dimensional full convolution structure, and the one-dimensional full convolution structure can maintain the same length as the convolution operation result output through it, that is, based on the one-dimensional full convolution
  • the product structure makes the convolution operation result output by the preset first convolutional neural network and the convolution operation result output through the one-dimensional full convolution structure a sequence of equal length.
  • step 206 may specifically include: if the boundary character recognition result of the named entity to be determined is consistent with the preset boundary character recognition result, determining the The named entity is the final named entity; if the boundary character recognition result of the to-be-determined named entity is inconsistent with the preset boundary character recognition result, the to-be-determined named entity is used as a new training sample of the semantic recognition model.
  • the second convolutional neural network preset in the semantic recognition model uses the second convolutional neural network preset in the semantic recognition model to perform boundary character recognition according to the obtained SBME mark in the named entity to be determined. Specifically, if the mark in the obtained named entity to be determined is S, that is If the named entity to be determined is a single character, then the single character is recognized; if the recognition result is consistent with the preset boundary character recognition result, the single character is determined to be the final named entity. For example, if the recognized named entity to be determined is "cat", and the recognition result is consistent with the preset boundary character recognition result, then the final recognized named entity is a cat. If the recognition result is inconsistent with the preset boundary character recognition result, it means that the word is not a named entity.
  • the named entity is recognized according to the tag B and the tag E; if the recognition result is consistent with the preset If the boundary character recognition results are consistent, it is determined that the named entity to be determined is the final named entity.
  • the mark in the named entity to be determined includes BME, it is recognized that the mark B and mark E in the named entity to be determined correspond to "pre” and "home”, and the recognition result is consistent with the preset boundary character recognition result, then Identify the final named entity as a prophet; if the mark in the named entity to be determined includes BE, identify the mark B and mark E in the named entity to be determined corresponding to "work” and "home”, and the recognition result is the same as the preset If the boundary character recognition results are consistent, the final named entity is recognized as the writer. If the recognition result is inconsistent with the preset boundary character recognition result, it means that the multi-character or double-character is not a named entity.
  • the recognition result is not a named entity.
  • the named entity to be identified is "writer”
  • the recognition result is inconsistent with the preset boundary character recognition result
  • "writer” is used as a new training sample of the semantic recognition model, and the semantic recognition model is further refined. Optimized to improve the recognition accuracy of the semantic recognition model.
  • the preset boundary character recognition result can be the single character of the named entity, and the head and tail of the double character and multi-character, or the word attribute of the word mark in the training sample set, that is, the word attribute of the word. As well as the beginning and end of double-word and multi-word.
  • the text to be recognized can include one or more named entities. Therefore, according to the text vector of the text to be recognized, the preset activation function softmax in the second convolutional neural network is used to output one or more named entities.
  • the recognition result that is, the output result corresponds to one or more named entities included in the text to be recognized.
  • the second convolutional neural network also includes an activation function softmax. Based on the activation function softmax, the calculation result (ie, the named entity to be determined) obtained through the two-layer convolution structure in the second convolutional neural network is further classified Operate to get the final named entity.
  • the preset third convolutional neural network is a densely connected structure DenseNet.
  • a convolutional layer and a pooling layer are constructed, and are fully connected through the activation function softmax
  • the layer outputs the recognition result, and the output result is a multi-classification variable, that is, one or more entity relationships included in the text to be recognized are determined according to the probability values of different classifications.
  • the corresponding relationship between the named entity in the training sample set and the marked entity relationship is used to determine the entity relationship, and the identified entity relationship is compared with the determined entity relationship. If the recognition results are consistent , The recognized entity relationship is the entity relationship in the text to be recognized; if the recognition results are inconsistent, it means that the recognition is wrong, and the wrong recognition result is adjusted to the corresponding relationship between the named entity in the training sample set and the marked entity relationship.
  • the named entity to be determined is added to the training sample set for training the semantic recognition model as a new phrase corpus, and the name to be determined is marked in the phrase corpus
  • the word attribute of the entity is a recognition error, so that the semantic recognition model can effectively improve the recognition accuracy of the text to be recognized after optimization training.
  • the first convolutional neural network preset in the semantic recognition model is used to obtain the text vector of the text to be recognized, and the second convolutional neural network preset in the semantic recognition model is used, according to the obtained
  • the text vector determines the named entity in the text to be recognized
  • the third convolutional neural network preset in the semantic recognition model is used to determine the entity relationship in the text to be recognized according to the obtained text vector and the determined named entity.
  • the system can According to the sentence input by the user, the semantic recognition model is used to realize accurate and rapid recognition of the sentence, thereby providing users with more accurate services and improving user experience.
  • an embodiment of the present application provides a semantic recognition device based on a convolutional neural network.
  • the device includes: a first convolutional neural network module 31, a second The convolutional neural network module 32 and the third convolutional neural network module 33.
  • the first convolutional neural network module 31 can be used to obtain the text vector of the text to be recognized by using the preset first convolutional neural network in the semantic recognition model; the first convolutional neural network module 31 recognizes the text to be recognized for the device
  • the second convolutional neural network module 32 may be used to use the second convolutional neural network preset in the semantic recognition model to determine the named entity in the text to be recognized according to the text vector obtained by the first convolutional neural network module 31;
  • the second convolutional neural network module 32 is the main functional module of the device for identifying named entities in the text to be recognized, and is also the core functional module of the device.
  • the third convolutional neural network module 33 can be used to use the preset third convolutional neural network in the semantic recognition model, according to the text vector obtained by the first convolutional neural network module 31 and the second convolutional neural network module 32
  • the determined named entity determines the entity relationship in the text to be recognized; the third convolutional neural network module 33 is the main functional module of the device that recognizes the entity relationship in the text to be recognized, and is also the core functional module of the device.
  • the first convolutional neural network module 31 can be specifically used to obtain the word vectors and word vectors of the text to be recognized by using the word vector dictionary; perform convolution operations on the obtained word vectors and word vectors to obtain The text vector of the text to be recognized.
  • a training module 34 which can be used to determine the first loss function and the second loss according to the initialized first, second, and third convolutional neural networks.
  • Function and third loss function according to the determined first loss function, second loss function and third loss function, perform the initialization of the first convolutional neural network, the second convolutional neural network and the third convolutional neural network Train to obtain the preset first convolutional neural network, second convolutional neural network and third convolutional neural network.
  • the training module 34 may be specifically used to determine the loss function of the semantic recognition model according to the determined first loss function, second loss function, and third loss function; use the semantic recognition model
  • the loss function of training initializes the first convolutional neural network, the second convolutional neural network, and the third convolutional neural network to obtain the preset first, second, and third convolutional neural networks The internet.
  • the second convolutional neural network module 32 can be specifically used to perform convolution operations on the acquired text vector to obtain the named entity to be determined; perform boundary character recognition on the named entity to be determined, according to the recognition The result determines the final named entity.
  • the second convolutional neural network module 32 can be specifically used to determine the named entity to be determined if the boundary character recognition result of the named entity to be determined is consistent with the preset boundary character recognition result Is the final named entity; if the boundary character recognition result of the to-be-determined named entity is inconsistent with the preset boundary character recognition result, the to-be-determined named entity is used as a new training sample of the semantic recognition model.
  • the training module 34 may be specifically used to train the semantic recognition model using the newly added training samples to obtain an optimized semantic recognition model. It should be noted that, for other corresponding descriptions of the functional units involved in the convolutional neural network-based semantic recognition device provided by the embodiment of the present application, reference may be made to the corresponding descriptions in FIG. 1 and FIG. 2, and details are not repeated here.
  • an embodiment of the present application also provides a non-volatile readable storage medium on which computer readable instructions are stored, and the program is executed when the processor is executed.
  • the above-mentioned semantic recognition method based on convolutional neural network as shown in Fig. 1 and Fig. 2.
  • the technical solution of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile non-volatile readable storage medium (can be CD-ROM, U disk, mobile hard disk) Etc.), including several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in each implementation scenario of this application.
  • a computer device which may be a personal computer, a server, or a network device, etc.
  • the embodiments of the present application also provide a computer device, which can be a personal computer, a server, or a network.
  • the physical device includes a non-volatile readable storage medium and a processor; the non-volatile readable storage medium is used to store computer readable instructions; and the processor is used to execute computer readable instructions to achieve the above Figure 1 and Figure 2 show the semantic recognition method based on convolutional neural network.
  • the computer device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and so on.
  • the user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, and the like.
  • the network interface can optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.
  • the non-volatile readable storage medium may also include an operating system and a network communication module.
  • the operating system is a program that manages the hardware and software resources of computer equipment, and supports the operation of information processing programs and other software and/or programs.
  • the network communication module is used to implement communication between various components in the non-volatile readable storage medium and communication with other hardware and software in the physical device.
  • this embodiment can effectively avoid the existing two This independent recognition model causes information redundancy in the process of joint use, thereby effectively improving the efficiency of semantic recognition.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Machine Translation (AREA)
  • Character Discrimination (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé et un appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi qu'un support de stockage lisible non volatil et un dispositif informatique, qui se rapportent au domaine technique du traitement de texte et peuvent améliorer l'efficacité de la reconnaissance sémantique. Le procédé consiste à : utiliser un premier réseau neuronal convolutif prédéfini dans un modèle de reconnaissance sémantique pour acquérir un vecteur de texte d'un texte à reconnaître ; utiliser un second réseau neuronal convolutif prédéfini dans le modèle de reconnaissance sémantique pour déterminer, selon le vecteur de texte acquis, une entité nommée dans ledit texte ; et utiliser un troisième réseau neuronal convolutif prédéfini dans le modèle de reconnaissance sémantique pour déterminer, en fonction du vecteur de texte acquis et de l'entité nommée déterminée, une relation d'entité dans ledit texte. La présente invention est applicable pour fournir une réponse intelligente aux questions de services aux clients dans une entreprise de produits d'assurance.
PCT/CN2019/117723 2019-04-26 2019-11-12 Procédé et appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi que support de stockage lisible non volatil et dispositif informatique WO2020215683A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910345595.7 2019-04-26
CN201910345595.7A CN110222330B (zh) 2019-04-26 2019-04-26 语义识别方法及装置、存储介质、计算机设备

Publications (1)

Publication Number Publication Date
WO2020215683A1 true WO2020215683A1 (fr) 2020-10-29

Family

ID=67819991

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/117723 WO2020215683A1 (fr) 2019-04-26 2019-11-12 Procédé et appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi que support de stockage lisible non volatil et dispositif informatique

Country Status (2)

Country Link
CN (1) CN110222330B (fr)
WO (1) WO2020215683A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114332903A (zh) * 2021-12-02 2022-04-12 厦门大学 一种基于端到端神经网络的琵琶乐谱识别方法及系统

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110222330B (zh) * 2019-04-26 2024-01-30 平安科技（深圳）有限公司 语义识别方法及装置、存储介质、计算机设备
CN111079418B (zh) * 2019-11-06 2023-12-05 科大讯飞股份有限公司 命名体识别方法、装置、电子设备和存储介质
CN112232088A (zh) * 2020-11-19 2021-01-15 京北方信息技术股份有限公司 合同条款风险智能识别方法、装置、电子设备及存储介质
CN112765984A (zh) * 2020-12-31 2021-05-07 平安资产管理有限责任公司 命名实体识别方法、装置、计算机设备和存储介质
CN112906380A (zh) * 2021-02-02 2021-06-04 北京有竹居网络技术有限公司 文本中角色的识别方法、装置、可读介质和电子设备
CN112949477B (zh) * 2021-03-01 2024-03-15 苏州美能华智能科技有限公司 基于图卷积神经网络的信息识别方法、装置及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018057945A1 (fr) * 2016-09-22 2018-03-29 nference, inc. Systèmes, procédés et supports lisibles par ordinateur permettant la visualisation d'informations sémantiques et d'inférence de signaux temporels indiquant des associations saillantes entre des entités de sciences de la vie
CN108763445A (zh) * 2018-05-25 2018-11-06 厦门智融合科技有限公司 专利知识库的构建方法、装置、计算机设备和存储介质
CN109165385A (zh) * 2018-08-29 2019-01-08 中国人民解放军国防科技大学 一种基于实体关系联合抽取模型的多三元组抽取方法
CN110222330A (zh) * 2019-04-26 2019-09-10 平安科技（深圳）有限公司 语义识别方法及装置、存储介质、计算机设备

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5761328A (en) * 1995-05-22 1998-06-02 Solberg Creations, Inc. Computer automated system and method for converting source-documents bearing alphanumeric text relating to survey measurements
CN107239446B (zh) * 2017-05-27 2019-12-03 中国矿业大学 一种基于神经网络与注意力机制的情报关系提取方法
CN108304911B (zh) * 2018-01-09 2020-03-13 中国科学院自动化研究所 基于记忆神经网络的知识抽取方法以及系统和设备
CN108536679B (zh) * 2018-04-13 2022-05-20 腾讯科技(成都)有限公司 命名实体识别方法、装置、设备及计算机可读存储介质
CN108804417B (zh) * 2018-05-21 2022-03-15 山东科技大学 一种基于特定领域情感词的文档级情感分析方法
CN109101492A (zh) * 2018-07-25 2018-12-28 南京瓦尔基里网络科技有限公司 一种自然语言处理中使用历史对话行为进行实体提取的方法及系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018057945A1 (fr) * 2016-09-22 2018-03-29 nference, inc. Systèmes, procédés et supports lisibles par ordinateur permettant la visualisation d'informations sémantiques et d'inférence de signaux temporels indiquant des associations saillantes entre des entités de sciences de la vie
CN108763445A (zh) * 2018-05-25 2018-11-06 厦门智融合科技有限公司 专利知识库的构建方法、装置、计算机设备和存储介质
CN109165385A (zh) * 2018-08-29 2019-01-08 中国人民解放军国防科技大学 一种基于实体关系联合抽取模型的多三元组抽取方法
CN110222330A (zh) * 2019-04-26 2019-09-10 平安科技（深圳）有限公司 语义识别方法及装置、存储介质、计算机设备

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
E, HAIHONG ET AL.: "Survey of entity relationship extraction based on deep learning", HTTP://KNS.CNKI.NET/KXREADER/DETAIL?TIMESTAMP=637169548090085000&DBCODE =CJFQ&TABLENAME=CJFDLAST2019&FILENAME=RJXB201906016&RESULT=1&SIGN=IOS%2BYM%2BP1%2FYQO6D83OTVC0JF%2FGG%3D, 28 March 2019 (2019-03-28), XP055746606 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114332903A (zh) * 2021-12-02 2022-04-12 厦门大学 一种基于端到端神经网络的琵琶乐谱识别方法及系统

Also Published As

Publication number Publication date
CN110222330B (zh) 2024-01-30
CN110222330A (zh) 2019-09-10

Similar Documents

Publication Publication Date Title
WO2020215683A1 (fr) Procédé et appareil de reconnaissance sémantique basés sur un réseau neuronal convolutif, ainsi que support de stockage lisible non volatil et dispositif informatique
CN109241524B (zh) 语义解析方法及装置、计算机可读存储介质、电子设备
WO2019242297A1 (fr) Procédé de dialogue intelligent basé sur la compréhension de lecture de machine, dispositif, et terminal
WO2020114429A1 (fr) Procédé d'apprentissage de modèle d'extraction de mot-clé, procédé d'extraction de mot-clé et dispositif informatique
WO2020232861A1 (fr) Procédé de reconnaissance d'entité nommée, dispositif électronique et support de stockage
US10114809B2 (en) Method and apparatus for phonetically annotating text
WO2020062770A1 (fr) Procédé et appareil de construction de dictionnaire de domaine et dispositif et support d'enregistrement
WO2022048173A1 (fr) Procédé et appareil d'identification d'intentions de clients basés sur l'intelligence artificielle, dispositif et support
CN110619050B (zh) 意图识别方法及设备
US10929610B2 (en) Sentence-meaning recognition method, sentence-meaning recognition device, sentence-meaning recognition apparatus and storage medium
US9811517B2 (en) Method and system of adding punctuation and establishing language model using a punctuation weighting applied to chinese speech recognized text
WO2023138188A1 (fr) Procédé et appareil d'apprentissage de modèle de fusion de caractéristiques, procédé et appareil de récupération d'échantillon, et dispositif informatique
WO2014117553A1 (fr) Procédé et système d'ajout de ponctuation et d'établissement de modèle de langue
JP7430820B2 (ja) ソートモデルのトレーニング方法及び装置、電子機器、コンピュータ可読記憶媒体、コンピュータプログラム
WO2021129123A1 (fr) Procédé et appareil de traitement de données de corpus, serveur et support de stockage
CN116127020A (zh) 生成式大语言模型训练方法以及基于模型的搜索方法
WO2022105121A1 (fr) Procédé et appareil de distillation appliqués à un modèle bert, dispositif et support de stockage
WO2024098623A1 (fr) Procédé et appareil de récupération inter-média, procédé et appareil d'apprentissage de modèle de récupération inter-média, dispositif et système de récupération de recette
WO2021098397A1 (fr) Procédé de traitement de données, appareil et support de stockage
CN113053367A (zh) 语音识别方法、语音识别的模型训练方法以及装置
WO2022228127A1 (fr) Procédé et appareil de traitement de texte d'élément, dispositif électronique et support de stockage
CN115359383A (zh) 跨模态特征提取、检索以及模型的训练方法、装置及介质
CN112214595A (zh) 类别确定方法、装置、设备及介质
CN113836316B (zh) 三元组数据的处理方法、训练方法、装置、设备及介质
US20220027766A1 (en) Method for industry text increment and electronic device

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: 19926379

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: 19926379

Country of ref document: EP

Kind code of ref document: A1