WO2021042516A1

WO2021042516A1 - Named-entity recognition method and device, and computer readable storage medium

Info

Publication number: WO2021042516A1
Application number: PCT/CN2019/116935
Authority: WO
Inventors: 金戈; 徐亮
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-09-02
Filing date: 2019-11-10
Publication date: 2021-03-11
Also published as: CN110688854B; CN110688854A

Abstract

A named-entity recognition method and device, and a computer readable storage medium, relating to the technical field of artificial intelligence. The method comprises: receiving first text data consisting of original statements to be recognized, and preprocessing the first text data to obtain text vector data (S1); obtaining a neural entity inference engine recognition model having a multi-layer structure, and training the neural entity inference engine recognition model (S2); inputting the text vector data into the trained neural entity inference engine recognition model for training to obtain a named entity set (S3); and inputting the text vector data and the named entity set into an inference engine in the neural entity inference engine recognition model for inference to obtain a named entity (S4). The method can realize accurate and efficient named-entity recognition.

Description

Named entity recognition method, device and computer readable storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on September 02, 2019, the application number is 201910825074.1, and the invention title is "Named Entity Recognition Method, Device and Computer-readable Storage Medium", the entire content of which is incorporated by reference Incorporate in the application.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to a method, device and computer-readable storage medium for identifying named entities in a text data set.

Background technique

With the development of the Internet today, there is more and more information in people's lives, most of which are text information. Therefore, how to process text information and identify named entities such as person names, organization names, and place names in it, so as to simplify people's extraction of text information is a big problem. However, the current main method of identifying entities is based on traditional neural entity reasoning methods, but because this method relies too much on local and low-level language features, such methods often encounter difficulties when there are ambiguous statements or rare names.

Summary of the invention

This application provides a named entity recognition method, device, and computer-readable storage medium, the main purpose of which is to perform deep learning on an original text data set to obtain a named entity.

In order to achieve the above-mentioned purpose, a named entity identification method provided by this application includes:

Receiving first text data composed of original sentences to be recognized, and preprocessing the first text data to obtain text vector data;

Obtain a neural entity inference engine recognition model with a multi-layer structure;

Inputting the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set;

The text vector data and the named entity set are input to the inference engine in the neural entity inference engine recognition model to perform inference to obtain the named entity.

In addition, in order to achieve the above object, the present application also provides a named entity recognition device, which includes a memory and a processor, the memory stores a named entity recognition program that can run on the processor, and the named entity The following steps are implemented when the recognition program is executed by the processor:

In addition, in order to achieve the above objective, the present application also provides a computer-readable storage medium having a named entity recognition program stored on the computer-readable storage medium, and the named entity recognition program can be executed by one or more processors, In order to realize the steps of the named entity recognition method as described above.

The named entity recognition method, device, and computer-readable storage medium described in this application apply deep learning technology. The neural entity inference engine recognition model includes a multi-layer structure, and each layer can independently complete a named entity recognition, and each layer of The named entity recognition result is used as a reference for the next layer. At this time, the optimal recognition result can be obtained through the inference engine; the named entity recognition of each layer can share parameters in most cases. Therefore, the named entity recognition method, device, and computer-readable storage medium proposed in this application can achieve precise, efficient and consistent named entity recognition.

Description of the drawings

FIG. 1 is a schematic flowchart of a named entity identification method provided by an embodiment of this application;

2 is a schematic diagram of the internal structure of a named entity identification device provided by an embodiment of the application;

FIG. 3 is a schematic diagram of modules of a named entity identification method program provided by an embodiment of this application.

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

detailed description

It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

This application provides a named entity recognition method. Referring to FIG. 1, it is a schematic flowchart of a named entity recognition method provided by an embodiment of this application. The method can be executed by a device, and the device can be implemented by software and/or hardware.

In this embodiment, the named entity recognition method includes:

S1. Receive first text data composed of original sentences to be recognized, and preprocess the first text data to obtain text vector data.

In a preferred embodiment of the present application, the preprocessing includes operations such as word segmentation, stop word removal, and duplicate removal on the first text data.

Specifically, this application performs word segmentation operations on the first text data to obtain second text data, performs stop word removal operations on the second text data to obtain third text data, and performs deduplication on the third text data The fourth text data is obtained by operation, and the word vector form conversion is performed on the fourth text data using the TF-IDF algorithm, so as to obtain the text vector data after the preprocessing is completed.

This application collects a large number of original sentences to be recognized to form the first text data. Text data is unstructured or semi-structured data, that can not be directly recognized classification algorithm, the purpose of the pretreatment is to be converted to text data vector space _{_{model: d i = (w 1,}} w 2, ..., w n), Among them, w _j is the weight of the j-th feature word.

The word segmentation is to segment each sentence in the original sentence to obtain a single word. Because there is no clear separation mark between words in Chinese representation, word segmentation is indispensable. For Chinese text, words have the ability to truly reflect the content of the document, so words are usually used as text feature words in the vector space model. However, Chinese text does not use spaces to separate words and words like English text, so the Chinese text needs to be segmented first.

Preferably, the word segmentation described in the present application can adopt a dictionary-based word segmentation method, and the Chinese character string to be segmented and the entry in the preset dictionary are matched according to a certain strategy, such as a traversal operation, to obtain the final word segmentation result.

Specifically, the dictionary may include a statistical dictionary. The statistical dictionary is a dictionary constructed by all possible word segmentation obtained by statistical methods. Further, the dictionary may also include a prefix dictionary. The prefix dictionary includes the prefix of each word segment in the statistical dictionary. For example, the prefixes of the word "Peking University" in the statistical dictionary are "North", "Beijing", and "Beijing University"; The prefix is "big" and so on.

The removal of stop words is to remove the functional words of the text data that have no actual meaning, and have no effect on the classification of the text, but words that appear frequently, including commonly used pronouns, prepositions, and the like. In the embodiment of this application, the selected method for removing stop words is stop word list filtering, that is, one-by-one matching is performed through a pre-built stop word list and words in the text data. If the matching is successful, this The word is a stop word, and the word needs to be deleted.

Furthermore, due to the intricate sources of the collected first text data, there may be a lot of duplicate text data. A large amount of repeated data will affect the classification accuracy, therefore, it is necessary to perform deduplication operations. In the embodiment of this application, the Euclidean distance method is used to perform the deduplication operation, and the formula is as follows:

Among them, w _1j and w _2j are the two first text data respectively, and d is the Euclidean distance. After calculating the Euclidean distances of every two first text data separately, the smaller the Euclidean distance, the more similar the text data, and then one of the two first text data whose Euclidean distance is less than the preset threshold is deleted.

After word segmentation, stop words removal, and deduplication, the text is represented by a series of characteristic words (keywords), but the data in this text form cannot be directly processed by the classification algorithm, but should be converted into a numerical form. The weight calculation of these characteristic words is used to characterize the importance of the characteristic words in the text.

In some embodiments of the present application, the TF-IDF algorithm is used to perform feature word calculation, and the data after word segmentation, word segmentation, stop word removal, and deduplication are preprocessed to obtain text vector data. The TF-IDF algorithm uses statistical information, word vector information, and dependency syntax information between words, builds a dependency graph to calculate the correlation strength between words, and uses TextRank algorithm to iteratively calculate the importance score of words.

In particular, the present application when performing recalculation feature words right, first calculates any two words (keywords) and dependent correlation degree w _j W _i is:

Where len(W _i , W _j ) represents the _{length of the dependency path between words W i} and W _j , and b is a hyperparameter.

This application believes that the semantic similarity between two words cannot accurately measure the importance of the words. Only when at least one of the two words appears frequently in the text can it prove that the two words are important. According to the concept of gravitation, the frequency of the word is regarded as the quality, the Euclidean distance between the word vectors of the two words is regarded as the distance, and the gravitation between the two words is calculated according to the formula of gravitation. However, in the current text environment, it is too one-sided to use word frequency to measure the importance of a word in a text. Therefore, this application introduces the IDF value and replaces the word frequency with the TF-IDF value to take into account more global information. So a new formula for the value of word gravity is obtained. The gravity of text words W _i and W _{j is:}

Wherein, tfidf (W) is a TF-IDF value of word W, d is the Euclidean distance between the vectors of words W _i and word w _j to.

Therefore, the degree of relevance between the two words is:

weight(W _i ,W _j )=Dep(W _i ,W _j )*f _grav (W _i ,W _j )

Finally, the present application establish an undirected graph G = (V, E) using TextRank algorithm, where V is the set of vertices, E is the set of edges, the following equation is calculated based on the scores of the words W _i,:

among them

Is a set related to the vertices W _i , and η is the damping coefficient, from which the feature weight WS(W _i ) is obtained, and therefore each word is expressed in the form of a numeric vector, that is, the text vector data is obtained.

S2. Obtain a neural entity inference engine recognition model with a multi-layer structure.

Preferably, the neural entity inference engine recognition model shown in this application is a multi-layer architecture, and each layer is an encoding-decoding Bi-LSTM model. At the same time, each layer independently completes a named entity neural reasoning, and the named entity neural reasoning result of each layer will be stored through a symbolic cache, as a reference for the next layer, this reference is through an interactive The essence of the pooled neural network is based on multiple real-time reasoning models. At the same time, in order to better interpret the model process, this application uses the demonstration text "Dong met Tao and Wiener John met the family of Tao" as an example to analyze the structure of the named entity neural reasoning model. The named entities actually contained in this sentence have four words: "John", "Tao", "Dong", and "Wiener". When the named entity neural inference model of this application is not trained, in the first layer of the named entity neural inference model, the candidate pool is empty because the initial named entity has not been trained to identify the initial named entity. In the model at this moment, the identified named entity result is "John" because "John" is the name of an ordinary person. In the conventional training model, the name of an ordinary person appears frequently, and it is easy to correspond and be recognized as a named entity. In the aforementioned model recognition process, "Tao" may be omitted. First, "Tao" is not an ordinary person name, so it does not appear frequently as a person name in the training model. Second, because "met the family" appears in the context semantics, it is not enough to express the feature of "Tao" as a person name, so the training model There is not enough and strong signal to correctly identify "Tao". After this training, the model stores the information of "John" as the initial named entity information in the candidate pool. In this way, in the second layer, the model can be inferred by the inference engine. The specific principle of reasoning is that the model can know that the word before "met" is a person's name based on the information of "John" and that "Tao" is a person's name, so the inference engine can infer "John" and the first " Tao" maintains consistency in sentence logic and grammatical positioning, and then updates the candidate pool to store "Tao" as the initial named entity into the candidate pool. In the same way, the neural entity inference engine recognition model model can be recognized by the inference engine in the third layer that "Wiener" is the same as the aforementioned "Tao" in sentence logic and grammatical positioning. It is recognized as a named entity. Layer training, to recognize all word units in the text to be recognized, and finally recognize all named entities contained in the text, and complete the named entity recognition process of the entire neural entity inference engine.

Preferably, in the embodiment of the present application, the preprocessed text vector data is encoded into an encoding representation sequence, and the decoder of each layer can independently give prediction labels based on word expression and context generation information. Since the predicted label indicates which words are entities, this application can find the entity representation from the predicted label. At the same time, the model of this application always records the entire neural entity inference engine recognition process, including the identified entity information, so that the model established in this application can "see" all the past decisions, and then each layer can use the inference engine from it. Cite and update the candidate pool, so that the predicted results can help the next layer to maintain global consistency and obtain better results.

S3. Input the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set.

Preferably, inputting the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set includes the following steps:

Step S301: Use the Bi-LSTM model to encode the text vector data to obtain an encoded representation sequence.

In the embodiment of the present application, a neural entity inference engine recognition model layer can be regarded as a rule codec framework based on the neural entity inference engine recognition model, which can receive additional information from the inference engine. In this work, the model of this application uses the Bi-LSTM model as the encoder and the LSTM model as the decoder. The candidate pool is a simple list consisting of a sequence of coded representations of named entities, which can contain all named entities identified in the entire text or in the entire result. The decoders and encoders of each layer can share parameters to avoid parameter growth and make the model easy to train as an end-to-end model. Therefore, the only difference between each layer is the candidate pool and the different named entities.

The LSTM model is designed to solve the problem of gradient disappearance and long-term dependence of learning. Formally, at time t, the memory c_t and hidden state h_t of the basic LSTM unit are updated as follows:

h _t ＝o _t ⊙tanh(c _t )

In the formula, ⊙ represents the element product, σ is the sigmoid function, x _t represents the vector input at time _t , and h t, o _t , c _t , and f _t represent the update of the input gate, forget gate, and output gate at step t, respectively. Because LSTM only receives the information before the current input word, but in sequential tasks, the context information behind is also very important. In order to capture contextual information from the past and the future, this application uses the Bi-LSTM model to encode it. The encoding rules are as follows, and the encoding representation sequence is obtained accordingly:

Where

Indicates the forward hidden state of the LSTM model;

Represents the reverse hidden state of the LSTM model.

Step S302: Input the coded representation sequence and the initial named entity in the candidate pool to be processed by the inference engine to obtain reference information.

The inference engine is a set of programs used to control and coordinate the entire system. Under a certain control strategy, the expert system solves the problem according to the problem information (the information communicated by the user and the expert system) and the knowledge in the knowledge base. That is, after the target engine sets the target object, it uses external information as input and uses logical operation methods such as deductive induction to perform calculations on the target object to generate a conclusion based on the established pattern matching.

Preferably, the reasoning engine in this embodiment is actually a multi-fact reasoning model. In this model, the current code indicates that the sequence information is a query, and the initial named entity information in the candidate pool is a fact. This embodiment uses a kernel K (query, fact) to calculate the relationship between the current code representation sequence information and each word, where the initial named entity information is in the candidate pool, and the calculation result s={s ₁ ,s ₂ , s ₃ ,...,s _n } represents suggestions for each initial named entity information, and then the inference engine obtains citation information from these suggestions.

Step S303: Input the coded representation sequence and the reference information into a decoder to obtain a predicted label; update the candidate pool according to the predicted label to obtain the named entity set.

Preferably, since the Bi-LSTM model is used in the embodiment of the present application, a good prediction label y ⁱ can be obtained. At the same time, this application adopts the BMEOS (Begin, Middle, End, Other, Single) marking scheme, so that from the predicted label y ⁱ , you can know where each named entity starts or ends to form boundary information, and then use the boundary information To organize and form a cache of documents. Since this model relies on local language characteristics to make decisions, this application considers how to store named entity information more reasonably and efficiently on this basis. In the embodiment of this application, a named entity is regarded as an independent and indivisible object, which is composed of several words, so the mode of appearance of an entity can be described as follows: [forward context][entity][向After context]. Therefore, this application stores entities in this mode.

Furthermore, since the coded representation sequence of each entity contains information to determine its predicted label. The encoder in the coding layer is forward

And backward

The combination. Therefore, this application stores the obtained predicted label in the candidate pool to provide decisive information for the inference engine to give the inference result. Based on the candidate pool, this application actually stores an entity as an object, and this object has three descriptions. Therefore, for each word to be predicted, this application can use the similarity between the current word and the candidate word database as a reference from three aspects to make better decisions. Each matrix in the candidate pool is actually a vector representation list, which also contains some entity information facts. Based on this, this application can use a special multi-fact reasoning model to obtain suggestions from it.

Further, the decoder includes:

In the formula, X represents the preprocessed text vector data, y ⁱ represents the predicted label of the i-th layer in the neural entity inference engine recognition model, and x _t represents the value of the text vector x at time t.

Further, in this embodiment, the neural entity inference-based named entity recognition model of each layer can share parameters in most cases, which makes the model of this application truly end-to-end.

Therefore, the candidate pool is updated in real time according to the predicted label to obtain the named entity set.

S4. Input the text vector data and the named entity set to the inference engine in the neural entity inference engine recognition model to perform inference to obtain the named entity.

In this embodiment, a stable named entity neural inference engine is obtained by inputting text vector data into the neural entity inference engine recognition model for training.

At the same time, according to the neural entity inference engine recognition model, by inputting the text data in the original sentence to be recognized, through the multi-layer neural entity inference engine recognition model, the corresponding initial named entity is obtained, and the initial named entity forms a named entity set.

This application uses the trained neural entity inference engine recognition model inference engine to infer the text vector data and the named entity set to obtain the named entity.

The invention also provides a named entity recognition device. Referring to FIG. 2, it is a schematic diagram of the internal structure of a named entity recognition device provided by an embodiment of this application.

In this embodiment, the named entity recognition device 1 may be a PC (Personal Computer, personal computer), or a terminal device such as a smart phone, a tablet computer, or a portable computer, or a server. The named entity recognition device 1 at least includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, and the like. The memory 11 may be an internal storage unit of the named entity recognition device 1 in some embodiments, for example, the hard disk of the named entity recognition device 1. In other embodiments, the memory 11 may also be an external storage device of the named entity recognition device 1, such as a plug-in hard disk equipped on the named entity recognition device 1, a smart media card (SMC), and a secure digital (Secure Digital). Digital, SD) card, flash card (Flash Card), etc. Further, the memory 11 may also include both an internal storage unit of the named entity recognition apparatus 1 and an external storage device. The memory 11 can be used not only to store application software and various data installed in the named entity recognition device 1, such as the code of the named entity recognition program 01, etc., but also to temporarily store data that has been output or will be output.

In some embodiments, the processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip, for running program codes or processing stored in the memory 11 Data, such as named entity recognition program 01, etc.

The communication bus 13 is used to realize the connection and communication between these components.

The network interface 14 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is usually used to establish a communication connection between the apparatus 1 and other electronic devices.

Optionally, the device 1 may also include a user interface. The user interface may include a display (Display) and an input unit such as a keyboard (Keyboard). The optional user interface may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the named entity recognition device 1 based on the neural entity inference engine and to display a visualized user interface.

Figure 2 only shows a named entity recognition device 1 with components 11-14 and a named entity recognition program 01 based on a neural entity inference engine. Those skilled in the art will understand that the structure shown in Figure 1 does not constitute a naming The definition of the entity recognition device 1 may include fewer or more components than shown, or a combination of certain components, or a different component arrangement.

In the embodiment of the apparatus 1 shown in FIG. 2, the memory 11 stores the named entity recognition program 01; when the processor 12 executes the named entity recognition program 01 stored in the memory 11, the following steps are implemented:

Step 1: Receive first text data composed of original sentences to be recognized, and preprocess the first text data to obtain text vector data.

Therefore, the degree of relevance between the two words is:

weight(W _i ,W _j )=Dep(W _i ,W _j )*f _grav (W _i ,W _j )

among them

Step 2: Obtain a neural entity inference engine recognition model with a multi-layer structure.

Step 3: Input the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set.

The first step is to use the Bi-LSTM model to encode the text vector data to obtain an encoded representation sequence.

h _t ＝o _t ⊙tanh(c _t )

In the formula, ⊙ represents the element product, σ is the sigmoid function, x _t represents the input vector at time t, h _t , o _t , c _t , and f _t represent the update of the input gate, forget gate, and output gate at step t, respectively. Because LSTM only receives the information before the current input word, but in sequential tasks, the context information behind is also very important. In order to capture contextual information from the past and the future, this application uses the Bi-LSTM model to encode it. The encoding rules are as follows, and the encoding representation sequence is obtained accordingly:

Where

Indicates the forward hidden state of the LSTM model;

Represents the reverse hidden state of the LSTM model.

The second step is to input the coded representation sequence and the initial named entity in the candidate pool to be processed by the inference engine to obtain reference information.

The third step is to input the coded representation sequence and the reference information into a decoder to obtain a predicted label; update the candidate pool according to the predicted label to obtain the named entity set.

And backward

Further, the decoder includes:

Step 4: Input the text vector data and the named entity set into the inference engine in the neural entity inference engine recognition model to perform inference to obtain a named entity.

Optionally, in other embodiments, the named entity recognition program may also be divided into one or more modules, and the one or more modules are stored in the memory 11 and run by one or more processors (in this embodiment, The processor 12) is executed to complete the application. The module referred to in the application refers to a series of computer program instruction segments capable of completing specific functions, and is used to describe the execution process of the named entity recognition program in the named entity recognition device.

For example, referring to FIG. 3, it is a schematic diagram of modules of the named entity recognition program in an embodiment of the named entity recognition device of this application. In this embodiment, the named entity recognition program can be divided into data receiving and processing modules 10, The word vector conversion module 20, the model training module 30, and the named entity output module 40 are exemplary:

The data receiving and processing module 10 is configured to receive first text data composed of original sentences to be recognized, and perform operations such as word segmentation, stop word removal, and duplication on the first text data.

The word vector conversion module 20 is configured to use the TF-IDF algorithm to perform word vector conversion on the first text data after operations such as word segmentation, stop word removal, and deduplication, so as to obtain text vector data.

The model training module 30 is used to obtain a neural entity inference engine recognition model with a multi-layer structure, where each layer is an encoding-decoding Bi-LSTM model, and each layer independently completes a named entity Neural reasoning, and the named entity neural reasoning results of each layer will be stored through a symbolic cache, which is used as a reference for the next layer.

The named entity output module 40 is configured to: input the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set, and input the text vector data and the named entity set into the neural entity The inference engine recognizes the inference engine in the model for inference, and obtains a named entity.

The above-mentioned data receiving and processing module 10, word vector conversion module 20, model training module 30, named entity output module 40 and other program modules implement functions or operation steps that are substantially the same as those in the above-mentioned embodiment, and will not be repeated here.

In addition, an embodiment of the present application also proposes a computer-readable storage medium having a named entity recognition program stored on the computer-readable storage medium, and the named entity recognition program can be executed by one or more processors to achieve the following operating:

It should be noted that the serial numbers of the foregoing embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. And the terms "include", "include" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes those elements that are not explicitly included. The other elements listed may also include elements inherent to the process, device, article, or method. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, device, article, or method that includes the element.

Through the description of the above implementation manners, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , Magnetic disk, optical disk), including a number of instructions to make a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) execute the method described in each embodiment of the present application.

The above are only the preferred embodiments of the application, and do not limit the scope of the patent for this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of the application, or directly or indirectly applied to other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

A named entity recognition method, characterized in that the method includes:

Receiving first text data composed of original sentences to be recognized, and preprocessing the first text data to obtain text vector data;

Obtain a neural entity inference engine recognition model with a multi-layer structure;

Inputting the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set;

The text vector data and the named entity set are input to the inference engine in the neural entity inference engine recognition model to perform inference to obtain the named entity.
The named entity recognition method according to claim 1, wherein each layer structure of the neural entity inference engine recognition model is encoded by a Bi-LSTM model and decoded by a decoder, and the decoded data enters The next layer structure is encoded and decoded again.
The method for recognizing a named entity according to claim 2, wherein said inputting said text vector data into said neural entity inference engine recognition model for training to obtain a named entity set comprises:

Input the preprocessed text vector data;

Use the Bi-LSTM model to encode the text vector data to obtain an encoded representation sequence and an initial named entity, and add the initial named entity to a candidate pool;

Inputting the coded representation sequence and the initial named entity in the candidate pool into the inference engine for processing to obtain reference information;

The coded representation sequence and the reference information are input to a decoder to obtain a predicted label, and the candidate pool is updated according to the predicted label to obtain the named entity set.
8. The named entity recognition method of claim 3, wherein the decoder comprises:

Wherein, X represents the text vector data preprocessed above, y represents the predicted label obtained after the neural entity inference engine recognition model is trained, and y i represents the i-th layer in the neural entity inference engine recognition model. The predicted label of x t represents the value of the text vector x at time t.
The named entity recognition method according to any one of claims 1 to 4, wherein said preprocessing said first text data to obtain text vector data comprises:

Perform a word segmentation operation on the first text data to obtain second text data, perform a stop word removal operation on the second text data to obtain third text data, and perform a deduplication operation on the third text data to obtain a fourth text data;

The TF-IDF algorithm is used to convert the word vector form to the fourth text data to obtain the text vector data.
5. The named entity recognition method according to claim 1, wherein said preprocessing said first text data to obtain text vector data further comprises:

Using the Euclidean distance method to perform a deduplication operation on the first text data, the formula is as follows:

Among them, w 1j and w 2j are the two first text data respectively, and d is the Euclidean distance.
The named entity recognition method according to claim 2, wherein the coding rule of the Bi-LSTM model is:

Where
Indicates the forward hidden state of the LSTM model;
Represents the reverse hidden state of the LSTM model.
A named entity recognition device, characterized in that the device includes a memory and a processor, the memory stores a named entity recognition program that can run on the processor, and the named entity recognition program is processed by the processor. The following steps are implemented when the device is executed:

Receiving first text data composed of original sentences to be recognized, and preprocessing the first text data to obtain text vector data;

Obtain a neural entity inference engine recognition model with a multi-layer structure;

Inputting the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set;

The text vector data and the named entity set are input to the inference engine in the neural entity inference engine recognition model to perform inference to obtain the named entity.
The named entity recognition device according to claim 8, wherein each layer structure of the neural entity inference engine recognition model is encoded by a Bi-LSTM model and decoded by a decoder, and the decoded data enters The next layer structure is encoded and decoded again.
9. The named entity recognition device according to claim 9, wherein said inputting said text vector data into said neural entity inference engine recognition model for training to obtain a named entity set comprises:

Input preprocessed text vector data;

Use the Bi-LSTM model to encode the text vector data to obtain an encoded representation sequence and an initial named entity, and add the initial named entity to a candidate pool;

Inputting the coded representation sequence and the initial named entity in the candidate pool into the inference engine for processing to obtain reference information;

The coded representation sequence and the reference information are input to a decoder to obtain a predicted label, and the candidate pool is updated according to the predicted label to obtain the named entity set.
The named entity recognition device of claim 10, wherein the decoder comprises:

Wherein, X represents the text vector data preprocessed above, y represents the predicted label obtained after the neural entity inference engine recognition model is trained, and y i represents the i-th layer in the neural entity inference engine recognition model. The predicted label of x t represents the value of the text vector x at time t.
The named entity recognition device according to any one of claims 8 to 11, wherein said preprocessing said first text data to obtain text vector data comprises:

Perform a word segmentation operation on the first text data to obtain second text data, perform a stop word removal operation on the second text data to obtain third text data, and perform a deduplication operation on the third text data to obtain a fourth text data;

The TF-IDF algorithm is used to convert the word vector form to the fourth text data to obtain the text vector data.
8. The named entity recognition device according to claim 8, wherein said preprocessing said first text data to obtain text vector data further comprises:

Using the Euclidean distance method to perform a deduplication operation on the first text data, the formula is as follows:

Among them, w 1j and w 2j are the two first text data respectively, and d is the Euclidean distance.
The named entity recognition device according to claim 9, wherein the coding rule of the Bi-LSTM model is:

Where
Indicates the forward hidden state of the LSTM model;
Represents the reverse hidden state of the LSTM model.
A computer-readable storage medium, characterized in that a named entity recognition program is stored on the computer-readable storage medium, and the named entity recognition program can be executed by one or more processors to implement the following steps:

Receiving first text data composed of original sentences to be recognized, and preprocessing the first text data to obtain text vector data;

Obtain a neural entity inference engine recognition model with a multi-layer structure;

Inputting the text vector data into the neural entity inference engine recognition model for training to obtain a named entity set;

The text vector data and the named entity set are input to the inference engine in the neural entity inference engine recognition model to perform inference to obtain the named entity.
The computer-readable storage medium according to claim 15, wherein each layer structure of the neural entity inference engine recognition model is encoded by the Bi-LSTM model, and decoded by a decoder, and the decoded data Enter the next layer of structure to encode and decode again.
16. The computer-readable storage medium according to claim 16, wherein said inputting said text vector data into said neural entity inference engine recognition model for training to obtain a named entity set comprises:

Input the preprocessed text vector data;

Use the Bi-LSTM model to encode the text vector data to obtain an encoded representation sequence and an initial named entity, and add the initial named entity to a candidate pool;

Inputting the coded representation sequence and the initial named entity in the candidate pool into the inference engine for processing to obtain reference information;

The coded representation sequence and the reference information are input to a decoder to obtain a predicted label, and the candidate pool is updated according to the predicted label to obtain the named entity set.
18. The computer-readable storage medium of claim 17, wherein the decoder comprises:

Wherein, X represents the text vector data preprocessed above, y represents the predicted label obtained after the neural entity inference engine recognition model is trained, and y i represents the i-th layer in the neural entity inference engine recognition model. The predicted label of x t represents the value of the text vector x at time t.
18. The computer-readable storage medium according to any one of claims 15 to 18, wherein said preprocessing said first text data to obtain text vector data comprises:

Perform a word segmentation operation on the first text data to obtain second text data, perform a stop word removal operation on the second text data to obtain third text data, and perform a deduplication operation on the third text data to obtain a fourth text data;

The TF-IDF algorithm is used to convert the word vector form to the fourth text data to obtain the text vector data.
15. The computer-readable storage medium according to claim 15, wherein said preprocessing said first text data to obtain text vector data further comprises:

Using the Euclidean distance method to perform a deduplication operation on the first text data, the formula is as follows:

Among them, w 1j and w 2j are the two first text data respectively, and d is the Euclidean distance.