CN113792539A - Entity relation classification method and device based on artificial intelligence, electronic equipment and medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses an entity relationship classification method based on artificial intelligence, which comprises the following steps: inputting a target word sequence corresponding to a text to be processed into a first feature extraction network of an entity relation classification model to perform literal feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word in the target word sequence output by each coding layer; inputting each first feature vector into a second feature extraction network to perform local feature and global associated feature extraction processing to obtain a weight value corresponding to each word in the target word sequence; and calculating a second feature vector of the text to be processed based on the first feature vector and the weight value, and inputting the second feature vector into a classification network to obtain a relation classification result between entities in the text to be processed. The invention also provides an entity relation classification device based on artificial intelligence, electronic equipment and a medium. The invention realizes the fast and accurate classification of the entity relationship.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an entity relation classification method and device based on artificial intelligence, electronic equipment and a medium.
Background
The entity relationship classification is to classify the relationship between entities in a sentence, and the entity relationship classification has wide application in the field of natural language processing, for example, construction of a knowledge graph, question understanding in a question-answering system, multi-hop reasoning in machine reading understanding, and the like, and how to quickly and accurately classify the entity relationship is the current focus of attention.
Currently, a syntactic analysis tool is usually adopted to extract syntactic characteristics of a sentence, and the sentence and the syntactic characteristics are input into a neural network model to perform entity relationship classification. Therefore, there is a need for an entity relationship classification method based on artificial intelligence to classify the relationships between entities quickly and accurately.
Disclosure of Invention
In view of the above, there is a need to provide an entity relationship classification method based on artificial intelligence, which aims to classify relationships between entities quickly and accurately.
The entity relation classification method based on artificial intelligence provided by the invention comprises the following steps:
responding an entity relation classification request aiming at a text to be processed sent by a user based on a client, and executing word segmentation processing and entity marking processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed;
acquiring an entity relationship classification model from a preset database, wherein the entity relationship classification model comprises a first feature extraction network, a second feature extraction network and a classification network, and the first feature extraction network comprises a plurality of coding layers which are connected in series;
inputting the target word sequence into the first feature extraction network to perform literal feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word in the target word sequence output by each coding layer;
inputting each first feature vector into the second feature extraction network to execute local feature and global associated feature extraction processing, and obtaining a weight value corresponding to each word in the target word sequence;
and calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, and inputting the second feature vector into the classification network to execute entity relationship classification processing to obtain a relationship classification result between entities in the text to be processed.
Optionally, the performing word segmentation processing and entity tagging processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed includes:
executing entity identification processing on the text to be processed to obtain an entity set corresponding to the text to be processed;
performing word segmentation processing on the text to be processed to obtain an initial word sequence corresponding to the text to be processed;
and performing entity marking processing on the initial word sequence based on the entity set to obtain a target word sequence corresponding to the text to be processed.
Optionally, the inputting each first feature vector into the second feature extraction network to perform local feature and global associated feature extraction processing, so as to obtain a weight value corresponding to each word in the target word sequence, includes:
combining the first feature vectors to obtain a first feature matrix of each word in the target word sequence;
inputting the first feature matrix into a first linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a second feature matrix of each word in the target word sequence;
inputting the second feature matrix into a plurality of combined feature extraction networks connected in series of the second feature extraction network to execute local feature and global associated feature extraction processing, so as to obtain a third feature matrix of each word in the target word sequence;
inputting the third feature matrix into a second linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a third feature vector of each word in the target word sequence;
and inputting the third feature vector into an activation layer of the second feature extraction network to perform weight calculation to obtain a weight value corresponding to each word in the target word sequence.
Optionally, the calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value includes:
calculating a fourth feature vector corresponding to the text to be processed based on the first feature vector of each word in the target word sequence output by the last coding layer and the weight value;
calculating a fifth feature vector corresponding to the text to be processed based on the first feature vectors of a plurality of preset words in the target word sequence output by the last coding layer;
and performing stacking processing on the fourth feature vector and the fifth feature vector to obtain a second feature vector corresponding to the text to be processed.
Optionally, the process of constructing the entity relationship classification model includes:
respectively constructing a first feature extraction network, a second feature extraction network and a classification network, and connecting the output of the first feature extraction network with the input of the classification network;
and connecting the output of each coding layer in the first feature extraction network with the input of the second feature extraction network, and connecting the output of the second feature extraction network with the input of the classification network to obtain an entity relationship classification model.
Optionally, the inputting the second feature vector into the classification network to perform entity relationship classification processing to obtain a relationship classification result between entities in the text to be processed includes:
and inputting the second feature vector into the classification network to obtain a probability value of each relationship category of every two entities in the text to be processed in the target relationship category set, and taking the relationship category with the maximum probability value as the target relationship category of the two corresponding entities.
Optionally, the determining process of the target relationship category set includes:
determining a field type corresponding to each entity in the text to be processed;
acquiring a mapping relation between a field category and a relation category set, and determining an initial relation category set corresponding to each entity in the text to be processed based on the mapping relation;
and combining the set of the initial relationship category set into a target relationship category set.
In order to solve the above problem, the present invention further provides an entity relationship classification apparatus based on artificial intelligence, the apparatus comprising:
the response module is used for responding an entity relationship classification request which is sent by a user based on a client and aims at the text to be processed, and executing word segmentation processing and entity marking processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed;
the system comprises an acquisition module, a classification module and a classification module, wherein the acquisition module is used for acquiring an entity relationship classification model from a preset database, the entity relationship classification model comprises a first feature extraction network, a second feature extraction network and a classification network, and the first feature extraction network comprises a plurality of coding layers which are connected in series;
the first extraction module is used for inputting the target word sequence into the first feature extraction network to execute the extraction processing of the literal feature, the semantic feature and the syntactic feature so as to obtain a first feature vector of each word in the target word sequence output by each coding layer;
the second extraction module is used for inputting each first feature vector into the second feature extraction network to execute local feature and global associated feature extraction processing so as to obtain a weight value corresponding to each word in the target word sequence;
and the classification module is used for calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, inputting the second feature vector into the classification network to execute entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores an entity relationship classification program executable by the at least one processor, the entity relationship classification program being executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based entity relationship classification method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium having an entity relationship classification program stored thereon, where the entity relationship classification program is executable by one or more processors to implement the above artificial intelligence-based entity relationship classification method.
Compared with the prior art, the method has the advantages that word segmentation processing and entity marking processing are firstly executed on the text to be processed to obtain a target word sequence; then, inputting the target word sequence into a first feature extraction network of the entity relation classification model to perform literal feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word in the target word sequence output by each coding layer; then, inputting each first feature vector into a second feature extraction network to execute local feature and global associated feature extraction processing to obtain a weight value corresponding to each word in the target word sequence; and finally, calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, and inputting the second feature vector into a classification network to obtain a relation classification result between entities in the text to be processed. The invention extracts the literal feature, semantic feature, syntactic feature, local feature and global association feature of the text to be processed, so that the extracted features are richer, the classification accuracy is higher, the feature extraction and classification are performed by an entity relation classification model without depending on an external tool, and the high efficiency of classification is ensured. Therefore, the invention realizes the fast and accurate classification of the relationship between the entities.
Drawings
FIG. 1 is a schematic flow chart illustrating an entity relationship classification method based on artificial intelligence according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an entity relationship classification model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a combined feature extraction network according to an embodiment of the present invention;
FIG. 4 is a block diagram of an apparatus for artificial intelligence-based entity relationship classification according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing an artificial intelligence-based entity relationship classification method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides an entity relation classification method based on artificial intelligence. Referring to fig. 1, a schematic flow chart of an entity relationship classification method based on artificial intelligence according to an embodiment of the present invention is shown. The method may be performed by an electronic device, which may be implemented by software and/or hardware.
In this embodiment, the entity relationship classification method based on artificial intelligence includes:
s1, responding to an entity relation classification request sent by a user based on a client and aiming at the text to be processed, and executing word segmentation processing and entity marking processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed.
In this embodiment, the text to be processed may be a sentence or a multiple sentence, and the purpose of the present solution is to classify the relationship between any two entities in the text to be processed. For example, if the text to be processed is "a corporation stands in 1980 and is located in guangdong province", the text to be processed includes three entities "a corporation", "1980" and "guangdong province", and the present scheme is used for performing relationship classification on any two entities in the three entities.
After the text to be processed is obtained, word segmentation processing and entity marking processing need to be executed on the text to be processed, so that a target word sequence corresponding to the text to be processed is obtained.
The word segmentation processing and entity marking processing are executed on the text to be processed to obtain a target word sequence corresponding to the text to be processed, and the method comprises the following steps:
a11, performing entity identification processing on the text to be processed to obtain an entity set corresponding to the text to be processed;
in this embodiment, the entity recognition processing is performed on the text to be processed through an entity recognition model, which may be a BERT model or a neural network model.
A12, performing word segmentation processing on the text to be processed to obtain an initial word sequence corresponding to the text to be processed;
in this embodiment, the word segmentation process may be performed on the text to be processed by using a forward maximum matching method, a reverse maximum matching method, or a least segmentation method.
And A13, performing entity marking processing on the initial word sequence based on the entity set to obtain a target word sequence corresponding to the text to be processed.
The entity marking processing comprises entity marking, sentence beginning marking and sentence ending marking, the scheme adopts a marker to perform entity marking processing on an initial word sequence, the marker comprises a sentence beginning symbol, a sentence ending symbol, an entity beginning symbol and an entity ending symbol, and an entity needing to be marked in the initial word sequence is determined through the entities in the entity set.
In this embodiment, the beginning of the sentence is [ CLS ], the end of the sentence is [ SEP ], the beginning of the first entity is [ E11], the end of the entity is [ E12], the beginning of the second entity is [ E21], the end of the entity is [ E22], and … ….
For example, for the text to be processed, "a company stands in 1980 and is located in Guangdong province", the word segmentation processing and the entity marking processing are performed on the text, and the obtained target word sequence is "[ CLS ] [ E11] a company [ E12] stands in [ E21]1980 [ E22] and is located in [ E31] Guangdong province [ E32] [ SEP ]".
S2, obtaining an entity relationship classification model from a preset database, wherein the entity relationship classification model comprises a first feature extraction network, a second feature extraction network and a classification network, and the first feature extraction network comprises a plurality of coding layers which are connected in series.
In this embodiment, in order to solve the problem that entity relationship classification needs to be performed by means of a syntactic analysis tool in the prior art, an entity relationship classification model is pre-constructed and stored, and the entity relationship classification model can extract the literal features, semantic features, and syntactic features of an input text, and also extract the local features and global association features of the input text, so that deep learning can be performed on the input text, and an entity relationship classification result can be improved.
The construction process of the entity relationship classification model comprises the following steps:
b11, respectively constructing a first feature extraction network, a second feature extraction network and a classification network, and connecting the output of the first feature extraction network and the input of the classification network;
referring to fig. 2, which is a schematic structural diagram of an entity relationship classification model according to an embodiment of the present invention, an output of a first feature extraction network is connected to an input of a classification network to serve as a backbone network of the entity relationship classification model, and a left side of fig. 2 is the backbone network, where the first feature extraction network includes an embedding layer and a plurality of coding layers (transform Encoder layers) connected in series, the embedding layer is configured to convert input words into word vectors, and the coding layer is configured to extract features of the word vectors.
In this embodiment, the coding layer may be 12 layers, which is similar to the coding layer in the BERT model, and the 1 st to 3 rd layer coding layers may extract the character surface features of a single word or several adjacent words in the input text, that is, the character surface features of the input text; the 4 th-7 th coding layers extract the part of speech (such as noun, verb and the like) of each word in the input text and the grammatical relation among the words, such as the main and predicate relation, the moving and guest relation and the like, so that the syntactic characteristics of the input text can be extracted; the 8 th layer to the 12 th layer can extract the semantic relation of each word of the input text, namely extracting the semantic features of the input text.
The classification network comprises a fully connected layer and an active layer, wherein the fully connected layer is used for integrating input features, and the active layer is used for classification prediction.
And B12, connecting the output of each coding layer in the first feature extraction network with the input of the second feature extraction network, and connecting the output of the second feature extraction network with the input of the classification network to obtain an entity relationship classification model.
The right side of fig. 2 is a second feature extraction network, which is a branch network of the entity relationship classification model. In this embodiment, the second feature extraction network includes a linear layer, a plurality of combined feature extraction networks (Local and global combination) connected in series, and an activation layer, where the linear layer is configured to convert an input from a high-dimensional sparse feature to a low-dimensional dense feature, the combined feature extraction network is configured to extract a Local feature and a global association feature of the input, and the activation layer is configured to output a weight corresponding to each input.
Referring to fig. 3, a schematic structural diagram of a combined feature extraction network according to an embodiment of the present invention is shown, where the combined feature extraction network includes a convolutional layer, an attention layer, and a linear layer, where the convolutional layer is used to extract an input local feature, the attention layer is used to extract an input global correlation feature, and the linear layer is used to perform dimension reduction processing on the feature.
The output of each coding layer in the first feature extraction network is used as the input of the second feature extraction network, and the second feature extraction network enhances the understanding of the input text by learning the local features and the global association features of the output features of each coding layer, so that the accuracy of entity relationship classification in the input text can be improved.
And S3, inputting the target word sequence into the first feature extraction network to perform literal feature, semantic feature and syntactic feature extraction processing, and obtaining a first feature vector of each word in the target word sequence output by each coding layer.
Inputting the target word sequence into an embedding layer of a first feature extraction network to execute word vector conversion processing to obtain a word vector of each word in the target word sequence, inputting the word vector into a plurality of coding layers connected in series to execute word feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word output by each coding layer. In this embodiment, the coding layer is 12 layers, and each word corresponds to 12 first feature vectors.
And S4, inputting each first feature vector into the second feature extraction network to perform local feature and global associated feature extraction processing, and obtaining a weight value corresponding to each word in the target word sequence.
The second feature extraction network is used for extracting input local features and global association features, the first feature vector is fused with literal features, semantic features and syntactic features, and after the second feature extraction network is used for processing, the obtained weighted value is accurate.
The inputting each first feature vector into the second feature extraction network to perform local feature and global associated feature extraction processing to obtain a weight value corresponding to each word in the target word sequence includes:
c11, combining the first feature vectors to obtain a first feature matrix of each word in the target word sequence;
for example, for word 1 in the target word sequence, if the first feature vector output by each coding layer is an n-dimensional feature, the first feature matrix obtained by merging 12 first feature vectors is a feature of 12 × n.
C12, inputting the first feature matrix into a first linear layer of the second feature extraction network to execute feature dimension reduction processing, and obtaining a second feature matrix of each term in the target term sequence;
the linear layer is used for carrying out dimension reduction processing on the input features, so that the classification efficiency is improved.
C13, inputting the second feature matrix into a plurality of combined feature extraction networks connected in series of the second feature extraction network to execute local feature and global associated feature extraction processing, and obtaining a third feature matrix of each word in the target word sequence;
the convolutional layer of the combined feature extraction network is used to extract local features and the attention layer is used to extract global correlation features, so the third feature matrix contains local and global features.
C14, inputting the third feature matrix into a second linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a third feature vector of each word in the target word sequence;
the second linear layer performs further dimension reduction processing on the third feature matrix.
And C15, inputting the third feature vector into an activation layer of the second feature extraction network to perform weight calculation, and obtaining a weight value corresponding to each word in the target word sequence.
The activation layer performs operation through an activation function and can output a weight value corresponding to each word in the target word sequence.
S5, calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, inputting the second feature vector into the classification network to execute entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed.
Through the processing of the first characteristic extraction network and the second characteristic extraction network, the characteristics of the input classification network are rich, and the relation categories among the entities in the text to be processed can be accurately output.
For example, for a text to be processed, the relationship classification result output by the processing of the entity relationship classification model is: the relationship between "company a" and "1980" is "company agency-established time", "the relationship between company a" and "guangdong province" is "company agency-location", and the relationship between "1980" and "guangdong province" is "established time-location".
The calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value includes:
d11, calculating a fourth feature vector corresponding to the text to be processed based on the first feature vector of each word in the target word sequence output by the last coding layer and the weight value;
in this embodiment, the second feature vector corresponding to the text to be processed is calculated according to the first feature vector and the weight value of each word output by the last coding layer (i.e., the 12 th coding layer).
The last coding layer fuses the characteristics of each previous coding layer, and the output characteristics represent the largest information quantity.
The calculation formula of the fourth feature vector is as follows: a is1*x1+a2*x2+…+an*xnWherein y is a fourth feature vector corresponding to the text to be processed, and x1、x2、xnFirst feature vectors, a, of the first, second and nth words in the target word sequence, respectively1、a2、anThe weight values corresponding to the first, second and nth words in the target word sequence are respectively.
D12, calculating a fifth feature vector corresponding to the text to be processed based on the first feature vectors of a plurality of preset words in the target word sequence output by the last coding layer;
in this embodiment, the preset words include a beginning of a sentence and start characters of each entity, for example, [ CLS ], [ E11], [ E21] and [ E31] in a target word sequence of the text to be processed, where [ CLS ] aggregates features of the entire target word sequence, [ E11] aggregates features of a first entity, and [ E21] aggregates features of a second entity.
And merging the sentence head symbol and the first feature vector of each entity head symbol to obtain a fifth feature vector corresponding to the text to be processed.
D13, stacking the fourth feature vector and the fifth feature vector to obtain a second feature vector corresponding to the text to be processed.
And if the fourth feature vector is a p-dimensional feature and the fifth feature vector is a q-dimensional feature, the dimensionality of the second feature vector obtained after the stacking process is (p + q).
Inputting the second feature vector into the classification network to execute entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed, including:
and inputting the second feature vector into the classification network to obtain a probability value of each relationship category of every two entities in the text to be processed in the target relationship category set, and taking the relationship category with the maximum probability value as the target relationship category of the two corresponding entities.
In order to prevent the classification efficiency from being low due to too many relationship classes in the target relationship class set, the scheme determines the target relationship class set according to the field classes of the entities in the text to be processed.
The determination process of the target relationship category set comprises the following steps:
e11, determining the corresponding field type of each entity in the text to be processed;
in this embodiment, the entity is matched with the entity library corresponding to each field type to determine the field type corresponding to each entity.
E12, obtaining a mapping relation between the field category and the relation category set, and determining an initial relation category set corresponding to each entity in the text to be processed based on the mapping relation;
in this embodiment, the mapping relationship between the domain category and the relationship category set is stored in advance, and the initial relationship category set corresponding to each entity can be determined by the mapping relationship.
E13, collaborating the initial relation category set into a target relation category set.
And summarizing the initial relation category sets corresponding to the entities to obtain a target relation category set corresponding to the text to be processed.
According to the embodiment, the entity relationship classification method based on artificial intelligence provided by the invention comprises the steps of firstly, performing word segmentation processing and entity marking processing on a text to be processed to obtain a target word sequence; then, inputting the target word sequence into a first feature extraction network of the entity relation classification model to perform literal feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word in the target word sequence output by each coding layer; then, inputting each first feature vector into a second feature extraction network to execute local feature and global associated feature extraction processing to obtain a weight value corresponding to each word in the target word sequence; and finally, calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, and inputting the second feature vector into a classification network to obtain a relation classification result between entities in the text to be processed. The invention extracts the literal feature, semantic feature, syntactic feature, local feature and global association feature of the text to be processed, so that the extracted features are richer, the classification accuracy is higher, the feature extraction and classification are performed by an entity relation classification model without depending on an external tool, and the high efficiency of classification is ensured. Therefore, the invention realizes the fast and accurate classification of the relationship between the entities.
Fig. 4 is a schematic block diagram of an artificial intelligence-based entity relationship classification apparatus according to an embodiment of the present invention.
The entity relationship classification device 100 based on artificial intelligence can be installed in electronic equipment. According to the implemented functions, the artificial intelligence based entity relationship classification apparatus 100 may include a response module 110, an obtaining module 120, a first extraction module 130, a second extraction module 140, and a classification module 150. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the response module 110 is configured to respond to an entity relationship classification request for a to-be-processed text sent by a user based on a client, and perform word segmentation processing and entity tagging processing on the to-be-processed text to obtain a target word sequence corresponding to the to-be-processed text.
The word segmentation processing and entity marking processing are executed on the text to be processed to obtain a target word sequence corresponding to the text to be processed, and the method comprises the following steps:
a21, performing entity identification processing on the text to be processed to obtain an entity set corresponding to the text to be processed;
a22, performing word segmentation processing on the text to be processed to obtain an initial word sequence corresponding to the text to be processed;
and A23, performing entity marking processing on the initial word sequence based on the entity set to obtain a target word sequence corresponding to the text to be processed.
The obtaining module 120 is configured to obtain an entity relationship classification model from a preset database, where the entity relationship classification model includes a first feature extraction network, a second feature extraction network, and a classification network, and the first feature extraction network includes a plurality of coding layers connected in series.
The construction process of the entity relationship classification model comprises the following steps:
b21, respectively constructing a first feature extraction network, a second feature extraction network and a classification network, and connecting the output of the first feature extraction network and the input of the classification network;
and B22, connecting the output of each coding layer in the first feature extraction network with the input of the second feature extraction network, and connecting the output of the second feature extraction network with the input of the classification network to obtain an entity relationship classification model.
A first extraction module 130, configured to input the target word sequence into the first feature extraction network to perform literal feature, semantic feature, and syntactic feature extraction processing, so as to obtain a first feature vector of each word in the target word sequence output by each coding layer.
The second extraction module 140 is configured to input each first feature vector into the second feature extraction network to perform local feature and global associated feature extraction processing, so as to obtain a weight value corresponding to each term in the target term sequence.
The inputting each first feature vector into the second feature extraction network to perform local feature and global associated feature extraction processing to obtain a weight value corresponding to each word in the target word sequence includes:
c21, combining the first feature vectors to obtain a first feature matrix of each word in the target word sequence;
c22, inputting the first feature matrix into a first linear layer of the second feature extraction network to execute feature dimension reduction processing, and obtaining a second feature matrix of each term in the target term sequence;
c23, inputting the second feature matrix into a plurality of combined feature extraction networks connected in series of the second feature extraction network to execute local feature and global associated feature extraction processing, and obtaining a third feature matrix of each word in the target word sequence;
c24, inputting the third feature matrix into a second linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a third feature vector of each word in the target word sequence;
and C25, inputting the third feature vector into an activation layer of the second feature extraction network to perform weight calculation, and obtaining a weight value corresponding to each word in the target word sequence.
The classification module 150 is configured to calculate a second feature vector corresponding to the to-be-processed text based on the first feature vector and the weight value, and input the second feature vector to the classification network to perform entity relationship classification processing, so as to obtain a relationship classification result between entities in the to-be-processed text.
The calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value includes:
d21, calculating a fourth feature vector corresponding to the text to be processed based on the first feature vector of each word in the target word sequence output by the last coding layer and the weight value;
d22, calculating a fifth feature vector corresponding to the text to be processed based on the first feature vectors of a plurality of preset words in the target word sequence output by the last coding layer;
d23, stacking the fourth feature vector and the fifth feature vector to obtain a second feature vector corresponding to the text to be processed.
Inputting the second feature vector into the classification network to execute entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed, including:
and inputting the second feature vector into the classification network to obtain a probability value of each relationship category of every two entities in the text to be processed in the target relationship category set, and taking the relationship category with the maximum probability value as the target relationship category of the two corresponding entities.
The determination process of the target relationship category set comprises the following steps:
e21, determining the corresponding field type of each entity in the text to be processed;
e22, obtaining a mapping relation between the field category and the relation category set, and determining an initial relation category set corresponding to each entity in the text to be processed based on the mapping relation;
e23, collaborating the initial relation category set into a target relation category set.
Fig. 5 is a schematic structural diagram of an electronic device for implementing an entity relationship classification method based on artificial intelligence according to an embodiment of the present invention.
The electronic device 1 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a command set or stored in advance. The electronic device 1 may be a computer, or may be a single network server, a server group composed of a plurality of network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing, where cloud computing is one of distributed computing and is a super virtual computer composed of a group of loosely coupled computers.
In the embodiment, the electronic device 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13, which are communicatively connected to each other through a system bus, wherein the memory 11 stores an entity relationship classification program 10, and the entity relationship classification program 10 can be executed by the processor 12. While FIG. 3 shows only the electronic device 1 having components 11-13 and the entity relationship classification program 10, those skilled in the art will appreciate that the configuration shown in FIG. 3 does not constitute a limitation of the electronic device 1, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
The storage 11 includes a memory and at least one type of readable storage medium. The memory provides cache for the operation of the electronic equipment 1; the readable storage medium may be a non-volatile storage medium such as flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the non-volatile storage medium may also be an external storage device of the electronic device 1, such as a plug-in hard disk provided on the electronic device 1, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. In this embodiment, the readable storage medium of the memory 11 is generally used for storing an operating system and various application software installed in the electronic device 1, for example, code of the entity relationship classification program 10 in an embodiment of the present invention is stored. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally configured to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with other devices. In this embodiment, the processor 12 is configured to execute the program code stored in the memory 11 or process data, for example, execute the entity relationship classification program 10.
The network interface 13 may comprise a wireless network interface or a wired network interface, and the network interface 13 is used for establishing a communication connection between the electronic device 1 and a client (not shown).
Optionally, the electronic device 1 may further include a user interface, the user interface may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further include a standard wired interface and a wireless interface. Alternatively, 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) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The entity relationship classification program 10 stored in the memory 11 of the electronic device 1 is a combination of instructions, and when running in the processor 12, can implement the above-mentioned artificial intelligence-based entity relationship classification method.
Specifically, the processor 12 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the entity relationship classification program 10, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be non-volatile or non-volatile. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The computer readable storage medium has stored thereon an entity relationship classification program 10, and the entity relationship classification program 10 is executable by one or more processors to implement the artificial intelligence based entity relationship classification method described above.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An entity relationship classification method based on artificial intelligence, which is characterized by comprising the following steps:
responding an entity relation classification request aiming at a text to be processed sent by a user based on a client, and executing word segmentation processing and entity marking processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed;
acquiring an entity relationship classification model from a preset database, wherein the entity relationship classification model comprises a first feature extraction network, a second feature extraction network and a classification network, and the first feature extraction network comprises a plurality of coding layers which are connected in series;
inputting the target word sequence into the first feature extraction network to perform literal feature, semantic feature and syntactic feature extraction processing to obtain a first feature vector of each word in the target word sequence output by each coding layer;
inputting each first feature vector into the second feature extraction network to execute local feature and global associated feature extraction processing, and obtaining a weight value corresponding to each word in the target word sequence;
and calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, and inputting the second feature vector into the classification network to execute entity relationship classification processing to obtain a relationship classification result between entities in the text to be processed.
2. The method for classifying entity relationships based on artificial intelligence according to claim 1, wherein the step of performing word segmentation processing and entity labeling processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed comprises:
executing entity identification processing on the text to be processed to obtain an entity set corresponding to the text to be processed;
performing word segmentation processing on the text to be processed to obtain an initial word sequence corresponding to the text to be processed;
and performing entity marking processing on the initial word sequence based on the entity set to obtain a target word sequence corresponding to the text to be processed.
3. The method for entity relationship classification based on artificial intelligence as claimed in claim 1, wherein the step of inputting each first feature vector into the second feature extraction network to perform local feature extraction and global associated feature extraction to obtain a weight value corresponding to each term in the target term sequence comprises:
combining the first feature vectors to obtain a first feature matrix of each word in the target word sequence;
inputting the first feature matrix into a first linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a second feature matrix of each word in the target word sequence;
inputting the second feature matrix into a plurality of combined feature extraction networks connected in series of the second feature extraction network to execute local feature and global associated feature extraction processing, so as to obtain a third feature matrix of each word in the target word sequence;
inputting the third feature matrix into a second linear layer of the second feature extraction network to execute feature dimension reduction processing to obtain a third feature vector of each word in the target word sequence;
and inputting the third feature vector into an activation layer of the second feature extraction network to perform weight calculation to obtain a weight value corresponding to each word in the target word sequence.
4. The method for artificial intelligence based entity relationship classification as claimed in claim 1, wherein said calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value comprises:
calculating a fourth feature vector corresponding to the text to be processed based on the first feature vector of each word in the target word sequence output by the last coding layer and the weight value;
calculating a fifth feature vector corresponding to the text to be processed based on the first feature vectors of a plurality of preset words in the target word sequence output by the last coding layer;
and performing stacking processing on the fourth feature vector and the fifth feature vector to obtain a second feature vector corresponding to the text to be processed.
5. The artificial intelligence based entity relationship classification method of claim 1, wherein the construction process of the entity relationship classification model comprises:
respectively constructing a first feature extraction network, a second feature extraction network and a classification network, and connecting the output of the first feature extraction network with the input of the classification network;
and connecting the output of each coding layer in the first feature extraction network with the input of the second feature extraction network, and connecting the output of the second feature extraction network with the input of the classification network to obtain an entity relationship classification model.
6. The artificial intelligence based entity relationship classification method according to claim 1, wherein the inputting the second feature vector into the classification network to perform entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed comprises:
and inputting the second feature vector into the classification network to obtain a probability value of each relationship category of every two entities in the text to be processed in the target relationship category set, and taking the relationship category with the maximum probability value as the target relationship category of the two corresponding entities.
7. The artificial intelligence based entity relationship classification method of claim 6, wherein the determination process of the target relationship class set comprises:
determining a field type corresponding to each entity in the text to be processed;
acquiring a mapping relation between a field category and a relation category set, and determining an initial relation category set corresponding to each entity in the text to be processed based on the mapping relation;
and combining the set of the initial relationship category set into a target relationship category set.
8. An artificial intelligence based entity relationship classification apparatus, the apparatus comprising:
the response module is used for responding an entity relationship classification request which is sent by a user based on a client and aims at the text to be processed, and executing word segmentation processing and entity marking processing on the text to be processed to obtain a target word sequence corresponding to the text to be processed;
the system comprises an acquisition module, a classification module and a classification module, wherein the acquisition module is used for acquiring an entity relationship classification model from a preset database, the entity relationship classification model comprises a first feature extraction network, a second feature extraction network and a classification network, and the first feature extraction network comprises a plurality of coding layers which are connected in series;
the first extraction module is used for inputting the target word sequence into the first feature extraction network to execute the extraction processing of the literal feature, the semantic feature and the syntactic feature so as to obtain a first feature vector of each word in the target word sequence output by each coding layer;
the second extraction module is used for inputting each first feature vector into the second feature extraction network to execute local feature and global associated feature extraction processing so as to obtain a weight value corresponding to each word in the target word sequence;
and the classification module is used for calculating a second feature vector corresponding to the text to be processed based on the first feature vector and the weight value, inputting the second feature vector into the classification network to execute entity relationship classification processing, and obtaining a relationship classification result between entities in the text to be processed.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores an entity relationship classification procedure executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based entity relationship classification method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon an entity relationship classification program executable by one or more processors to implement the artificial intelligence based entity relationship classification method of any one of claims 1 to 7.
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