WO2023070732A1 - Procédé et appareil de recommandation de texte à base d'apprentissage profond, et support associé - Google Patents

Procédé et appareil de recommandation de texte à base d'apprentissage profond, et support associé Download PDF

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WO2023070732A1
WO2023070732A1 PCT/CN2021/129027 CN2021129027W WO2023070732A1 WO 2023070732 A1 WO2023070732 A1 WO 2023070732A1 CN 2021129027 W CN2021129027 W CN 2021129027W WO 2023070732 A1 WO2023070732 A1 WO 2023070732A1
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text
milvus
database
information
vector
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PCT/CN2021/129027
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English (en)
Chinese (zh)
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钱启
王天星
杨东泉
程佳宇
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深圳前海环融联易信息科技服务有限公司
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Publication of WO2023070732A1 publication Critical patent/WO2023070732A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G06F16/316—Indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • 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

  • the present application relates to the technical field of computer software, in particular to a text recommendation method, device and related media based on deep learning.
  • Natural language processing is an important direction in the field of artificial intelligence. It studies various theories and methods that can realize effective communication between humans and computers using natural language.
  • natural language processing technology includes text processing, machine translation, semantic understanding, knowledge graph, intelligent question answering and other technologies.
  • text matching is a very important application direction of text processing, which plays a very important role in real life.
  • the development of this technology provides a feasible solution for users to search and match better in the sea of complicated information. In fact, text matching plays an important role in many practical scenarios.
  • the system needs to search the corpus for content as semantically similar as possible to the text to be matched, and return the matching result to the user.
  • the system needs to find the most similar question in the question answer database according to the question raised by the user, and return the answer corresponding to the similar question. In these scenarios, the accuracy of text matching directly affects the user experience.
  • the so-called text matching generally involves calculating the semantic similarity between two texts through an algorithm, and judging the matching degree between the two through the similarity. The higher the similarity value, the better the match. On the contrary, the more mismatched.
  • the current text matching mainly adopts more complex methods, and does not have dynamic scalability.
  • dynamic scalability means that the text database does not automatically expand, but needs to be expanded manually.
  • Embodiments of the present application provide a text recommendation method, device, computer equipment, and storage medium based on deep learning, aiming at improving the efficiency and accuracy of text recommendation.
  • the embodiment of the present application provides a text recommendation method based on deep learning, including:
  • the text feature vector is converted into Milvus vector index information, and stored in the Milvus database;
  • the sentence vector containing semantic information in the text to be matched is obtained through the twin neural network structure
  • the embodiment of the present application provides a text recommendation device based on deep learning, including:
  • the first vector generation unit is used to collect different types of text information to construct a text database, and generates a text feature vector for each text information in the text database through a twin neural network structure;
  • the first vector conversion unit is used to convert the text feature vector into Milvus vector index information, and store it in the Milvus database;
  • the second vector generation unit is used to obtain the sentence vector containing semantic information in the text to be matched through the twin neural network structure when the text to be matched is matched;
  • the text matching unit is used to select the top N pieces of Milvus vector index information with the highest semantic similarity in the Milvus database, and based on the correspondence between the Milvus vector index information and text feature vectors, select the corresponding top N pieces in the text database. N pieces of text information are used as matching results of the text to be matched.
  • an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the computer program, Realize the text recommendation method based on deep learning as described in the first aspect.
  • an embodiment of the present application provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program described in the first aspect is implemented.
  • the embodiment of the present application provides a deep learning-based text recommendation method, device, computer equipment, and storage medium, the method including: collecting different types of text information to construct a text database, and using a twin neural network structure to generate the text database Each text information in generates a text feature vector; the text feature vector is converted into Milvus vector index information, and stored in the Milvus database; when the text to be matched is matched, the text to be matched is obtained through the twin neural network structure Include the sentence vector of semantic information; Select the top N pieces of Milvus vector index information with the highest semantic similarity in the Milvus database, and based on the correspondence between Milvus vector index information and text feature vectors, select the corresponding The first N pieces of text information are used as the matching results of the text to be matched.
  • the embodiment of the present application solves the time-consuming and labor-intensive defect of matching the text to be matched with the text information one by one by constructing a text database and introducing the Milvus database, and the recommended matching process of this embodiment is simple to implement, has high accuracy and consumes a lot of effort. It doesn't take long. When recommending text, it can achieve fast retrieval, real-time feedback, and has the dynamic scalability of text data in the text database.
  • FIG. 1 is a schematic flow diagram of a text recommendation method based on deep learning provided in an embodiment of the present application
  • FIG. 2 is a schematic subflow diagram of a text recommendation method based on deep learning provided in an embodiment of the present application
  • FIG. 3 is a schematic block diagram of a text recommendation device based on deep learning provided by an embodiment of the present application
  • FIG. 4 is a sub-schematic block diagram of an apparatus for recommending text based on deep learning provided by an embodiment of the present application.
  • FIG. 1 is a schematic flowchart of a text recommendation method based on deep learning provided by an embodiment of the present application, which specifically includes steps S101 to S104.
  • S101 Collect different types of text information to construct a text database, and generate a text feature vector for each text information in the text database through a twin neural network structure;
  • a text database is constructed by using different types of text information, and at the same time, a text feature vector is generated for the text information in the text database through a twin neural network structure. Then convert the generated text feature vectors into Milvus vector index information and store them in the Milvus database.
  • the corresponding sentence vector is also generated for the text to be matched through the twin neural network structure, and then the similarity between the sentence vector and each Milvus vector index information is calculated through the Milvus database, and one of them is selected.
  • the top N pieces of Milvus vector index information with the highest similarity can then select the corresponding text information in the text database as the matching result or recommendation result.
  • the text database is a CSV text database (that is, a text database in CSV format).
  • the specific steps of constructing the text database may be as follows: divide the texts according to the categories to be recommended, find several texts under the categories, and classify them according to the categories, and each category is a CSV file.
  • the column names of the CSV file content can be question, flag. Among them, question represents the content of the text, and flag represents the name of the category. In a CSV file, the name of the flag is uniform.
  • this embodiment in order to facilitate data modification and data cleaning, stores the text database in the MySQL database in the form of structured data.
  • the purpose of this is that when performing data cleaning, you can directly write Python scripts to operate MySQL data tables to update text data.
  • MySQL database Compared with the general use of CSV format files as text databases, MySQL database has the characteristics of intuitive display, flexible operation, and convenient data dynamic expansion in text databases.
  • this embodiment uses the Milvus database to store the characteristic information of the text database, so as to realize fast retrieval.
  • the so-called Milvus database is an open-source vector database that supports addition, deletion, modification, and near-real-time query and retrieval of TB-level vectors. It has the characteristics of high flexibility, stability, reliability, and high-speed query.
  • text features of two text messages are usually extracted, and then based on the extracted text features, it is judged whether the two text messages match.
  • the word vectors of the text information are often added directly, or directly combined with the weights of the words in the text information, and weighted to construct the text features of the text information.
  • the obtained text vector may be affected by individual words in the text, so the constructed text features cannot accurately reflect the semantics of the text information, resulting in a low matching accuracy.
  • the most commonly used method of representing sentence vectors is to average the vectors of the BERT output layer, or use the first word of the BERT output layer to represent, which will undoubtedly produce poor sentence encoding information.
  • the deep learning-based text recommendation method solves the time-consuming and labor-intensive defect of matching the text to be matched with the text information one by one by constructing a text database and introducing the Milvus database, and
  • the recommendation matching process of this embodiment is simple to implement, has high accuracy and does not take long time.
  • a request only takes about 30 milliseconds to return the result.
  • the step S101 includes:
  • the text information in the text database is first combined in pairs, and then for each combination of two text information, the two are respectively input into the same structure
  • the BERT network model and the average pooling layer and correspondingly obtain 2 encoding results.
  • the encoding result of this model is the obtained text feature vector with semantic information. It is worth noting that this Siamese neural network structure can generate fixed-size vectors for the input sentences, and the semantic information of these vectors can be used to calculate similarity.
  • this embodiment makes improvements based on the BERT network model.
  • the full name of the BERT network model is: Bidirectional Encoder Representations from Transformer, which is a pre-trained network.
  • the goal of the BERT network model is to use large-scale unlabeled corpus training to obtain the semantic representation of the text containing rich semantic information, and then fine-tune the semantic representation of the text in specific NLP tasks, and finally apply for this NLP task.
  • These tasks can include intelligent question answering, sentence classification, sentence pair representation, etc.
  • a major disadvantage of the BERT network model is that it does not calculate independent sentence codes, which makes it difficult to obtain good sentence codes through the BERT network model.
  • the improvement of this embodiment mainly lies in adding an average pooling operation after the output layer of the BERT network model.
  • the role of the pooling layer is feature translation invariance.
  • the advantage of this setting is that after adding the average pooling layer, the final output vector size is fixed for different sentences.
  • the step S102 includes:
  • the text feature vector when the text feature vector is converted into Milvus vector index information, the text feature vector is first normalized, and the specific steps of the normalization process are: input 2 text information, and then pass through the same structure respectively
  • the BERT network model and the average pooling layer obtain two encoding results respectively, and then normalize the two encoding results respectively to obtain a normalized text feature vector.
  • the normalized feature vectors are converted into Milvus vector index information and stored in the Milvus database to obtain Milvus vector information.
  • the text database and the Milvus database are corresponding (that is, the ID numbers of the two are exactly the same), which is convenient for returning the text information of the original text database after query, instead of only returning the difficult-to-recognize index information of Milvus.
  • the step S103 includes:
  • the vector size of the text semantic representation is fixed through an average pooling layer to obtain the sentence vector.
  • the twin neural network structure is used when generating the text feature vector and generating the sentence vector, when generating the text feature vector, since the twin neural network structure is provided with 2 inputs, 2 text information are input at the same time .
  • the twin neural network structure already has the feature representation ability to adapt to similar data after generating text feature vectors, it is only necessary to input the text to be matched separately, that is, to input the text to be matched into the BERT network in sequence Model and average pooling layers.
  • the step S104 includes:
  • the cosine similarity method is used to calculate the similarity between the sentence vector and the Milvus vector index information, so as to search and select the top N text matching results with similar semantics, that is, the top N N pieces of Milvus vector index information.
  • the corresponding text information can then be found in the text database.
  • the Milvus vector index information is sorted and selected according to the degree of confidence.
  • the container before starting the Milvus Docker container, it is necessary to modify the MySQL address in the configuration file and expose port 19530. Once started, the container automatically creates 4 Milvus metadata tables in the MySQL database. If the text matching model is updated, the Milvus index vector needs to be rebuilt.
  • the Milvus vector database and the Siamese neural network structure jointly build a semantic search engine for text recommendation.
  • the text recommendation based on deep learning further includes: steps S201-S204.
  • the sample label score value output by the Siamese neural network structure is set as a number 0-5.
  • the advantage of setting the sample label score in this way is that it can describe the similarity between two texts more finely, instead of the 0-1 label, there are only two cases of similarity and dissimilarity. Because how similar the two texts are, or exactly the same, it cannot be seen only through label 1.
  • the number 0 means that the semantics of text A and text B are completely different.
  • the number 5 means that the semantics of text A and text B are exactly the same.
  • Other numbers (such as: 1, 2, 3, 4) represent the degree of semantic similarity between the two sentences in the middle.
  • these label score values need to be divided by 5 to obtain a normalized score value.
  • the optimizer uses the Adam optimizer, and the learning rate is 2e-5.
  • the loss function used is the cosine similarity loss function.
  • the loss function can also use other loss functions, but compared with other functions, the cosine similarity loss function has more obvious advantages in terms of speed. Directly using cosine similarity to measure the similarity between two sentence vectors can greatly improve the reasoning speed.
  • the performance effect on the test set can be observed quantitatively and qualitatively.
  • the text recommendation based on deep learning also includes:
  • the corresponding update text feature vector is generated through the twin neural network structure, and the update text feature vector is converted into Milvus update vector index information and stored in the Milvus database.
  • the text information in the MySQL database may come from multiple tables, for example, the information in the text database contains 3 categories, and the information of these 3 categories can be passed through the specific content information of a field in the 3 database tables Data cleaning is obtained. Multiple tables represent different types of data sources. After the data of multiple tables is cleaned, the desired data can be obtained and stored in the text database. These tables are called data source tables. Assuming a situation is considered, after the text database is made, the data source table is still continuously adding data.
  • this embodiment therefore adds a timing synchronization stage, which aims to perform data cleaning on the newly added data (that is, the text update information) according to querying the data source table at a specific time, and synchronize the latest data to the text database.
  • the reason for data cleaning is that the data of the text database itself is cleaned, processed and extracted from the specific content information of a certain field in the database table.
  • the primary key ID number is returned.
  • the apscheduler timing framework in the Python language can be used to complete the execution of the timing task.
  • the data in the data source table is constantly updated. If there is no timing synchronization mechanism, the newly added data cannot be automatically stored in the text database, nor can the Milvus index be created in time and inserted into the Milvus vector database. Then, when querying, the text information cannot keep pace with the times.
  • FIG. 3 is a schematic block diagram of a text recommendation device 300 based on deep learning provided by an embodiment of the present application.
  • the device 300 includes:
  • the first vector generation unit 301 is used to collect different types of text information to construct a text database, and generate a text feature vector for each text information in the text database through a twin neural network structure;
  • the first vector conversion unit 302 is used to convert the text feature vector into Milvus vector index information, and store it in the Milvus database;
  • the second vector generation unit 303 is used to obtain the sentence vector containing semantic information in the text to be matched through the twin neural network structure when the text to be matched is matched;
  • the text matching unit 304 is used to select the top N pieces of Milvus vector index information with the highest semantic similarity in the Milvus database, and based on the correspondence between the Milvus vector index information and text feature vectors, select the corresponding in the text database.
  • the first N pieces of text information are used as the matching results of the text to be matched.
  • the first vector generation unit 301 includes:
  • the encoding output unit is used to combine the text information in the text database in pairs, and input the two text information in the combination into the BERT network model and the average pooling layer of the same structure respectively, and output them respectively to obtain An encoding result corresponding to the two text information, and then use the encoding result as a text feature vector corresponding to the two text information.
  • the first vector conversion unit 302 includes:
  • a normalization unit configured to perform normalization processing on the text feature vector to obtain a normalized text feature vector
  • the second vector conversion unit is used to convert the normalized text feature vector into Milvus vector index information.
  • the second vector generating unit 303 includes:
  • the text semantic representation acquisition unit is used to separately input the text to be matched into the BERT network model to obtain the text semantic representation corresponding to the text to be matched;
  • the vector fixing unit is used to fix the vector size of the text semantic representation through the average pooling layer to obtain the sentence vector.
  • the text matching unit 304 includes:
  • a similarity calculation unit for utilizing the cosine similarity method to calculate the similarity score between the sentence vector and each of the Milvus vector index information
  • the index information selection unit is used to select the first N Milvus vector index information with the highest similarity score.
  • the deep learning-based text recommendation device 300 further includes:
  • the training learning unit 402 is used to use the text data in the training set to train and learn the twin neural network structure, and set the hyperparameter batch size of the twin neural network structure to 16, and the learning rate is 2e-5;
  • An optimization evaluation unit 403 configured to optimize parameters of the twin neural network structure by using an Adam optimizer, and perform performance evaluation on the twin neural network structure by using a cosine similarity loss function;
  • a parameter update unit 404 configured to update the parameters of the optimized Siamese neural network structure using the text data in the test set.
  • the text recommendation device 300 based on deep learning also includes:
  • An update information acquiring unit configured to acquire text update information, and store the text update information in the text database after data cleaning
  • the update storage unit is used to generate a corresponding update text feature vector through the twin neural network structure according to the text update information in the text database, and store the update text feature vector into the Milvus update vector index information after converting it into the Milvus update vector index information.
  • Milvus database Milvus database.
  • the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the steps provided in the above-mentioned embodiments can be realized.
  • the storage medium may include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, and other media capable of storing program codes.
  • the embodiment of the present application also provides a computer device, which may include a memory and a processor.
  • a computer program is stored in the memory.
  • the processor invokes the computer program in the memory, the steps provided in the above embodiments can be implemented.
  • the computer equipment may also include components such as various network interfaces and power supplies.

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Abstract

La présente demande concerne un procédé et un appareil de recommandation de texte à base d'apprentissage profond, et un support associé. Le procédé comprend : la collecte de différents types d'informations de texte pour construire une base de données de textes, et la génération d'un vecteur de caractéristique de texte pour chaque élément d'informations de texte dans la base de données de textes au moyen d'une structure de réseau neuronal siamois ; la conversion du vecteur de caractéristique de texte en des informations d'indice de vecteur de Milvus et leur stockage dans une base de données de Milvus ; lorsqu'une mise en correspondance est réalisée sur un texte à mettre en correspondance, l'acquisition d'un vecteur de phrase, qui inclut des informations sémantiques, dans ledit texte au moyen de la structure de réseau neuronal siamois ; et la sélection, dans la base de données de Milvus, des N premiers éléments d'informations d'indice de vecteur de Milvus ayant la plus grande similarité sémantique et la sélection, sur la base de la correspondance entre des informations d'indice de vecteur de Milvus et des vecteurs de caractéristique de texte, des N premiers éléments correspondants d'informations de texte dans la base de données de textes pour servir de résultat de mise en correspondance dudit texte. Selon les modes de réalisation de la présente demande, une base de données de textes est construite et une base de données de Milvus est introduite de sorte que, lorsqu'un texte est recommandé, une récupération rapide et un retour en temps réel peuvent être réalisés, et la précision est grande.
PCT/CN2021/129027 2021-10-27 2021-11-05 Procédé et appareil de recommandation de texte à base d'apprentissage profond, et support associé WO2023070732A1 (fr)

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CN117762917A (zh) * 2024-01-16 2024-03-26 北京三维天地科技股份有限公司 基于深度学习的医疗器械数据清洗方法及系统

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