CN114444506A - Method for extracting relation triple fusing entity types - Google Patents

Method for extracting relation triple fusing entity types Download PDF

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CN114444506A
CN114444506A CN202210026447.0A CN202210026447A CN114444506A CN 114444506 A CN114444506 A CN 114444506A CN 202210026447 A CN202210026447 A CN 202210026447A CN 114444506 A CN114444506 A CN 114444506A
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彭德中
陈付旻
吕建成
彭玺
桑永胜
胡鹏
孙亚楠
王旭
陈杰
王骞
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Abstract

The invention discloses a relation triple extraction method fusing entity types, which comprises the following steps: collecting text data as training samples; cleaning the collected training sample data to obtain a data set; segmenting a data set and dividing the data set into a training set, a verification set and a test set according to a certain proportion; building a deep learning network based on a BERT pre-training model and loading pre-training parameters to obtain deep expression of a training sample; building a Fast Gradient Method after the model to resist the robustness and generalization performance of the network promotion model; constructing a relation triple extraction model of a multi-head attention mechanism and a deep neural network; training and testing the model, storing a K-fold model for the verification set by adopting a K-fold cross verification method, testing the test set by integrating the K-fold model, and taking the average probability as the test result of the model; the model AtnFGM-MARE was output.

Description

Method for extracting relation triple fusing entity types
Technical Field
The invention relates to the technical field of natural language processing, in particular to an intelligent extraction method for relation triples fusing entity types.
Background
Relational extraction is typically organized and presented in the form of triplets, with these structured knowledge enabling a number of downstream tasks, such as: the method comprises the steps of information extraction, knowledge graph, search engine and question and answer task, wherein the task of relation extraction comprises the steps of identifying head entities and tail entities in texts and classifying relations between the entities. At present, a neural network method is generally used for extracting the relation triples, and is inspired by the rapid development of a pre-training language model technology, the relation extraction uses a pre-training model to perform unsupervised learning by utilizing large-scale unmarked data to obtain the deep expression of a text, the method depends on the deep expression of a head entity, the deep expression of the head entity influences the extraction effect of a tail entity and a relation type in a downstream task, but the method has limited characteristic expression capability on the head entity, the improvement effect of type information of the head entity on the tail entity and the relation extraction is not considered, and the model also has the problems of low robustness, weak generalization capability and the like.
Disclosure of Invention
The invention aims to solve the technical problem of providing a relational ternary ancestor extraction method fusing entity types, which uses a multi-head attention mechanism to fuse the characteristics of the entity types of a head and obtains relational triples through a Deep Neural Network (DNN), thereby improving the robustness and generalization performance of a model,
in order to solve the technical problem, the invention is realized by the following modes:
a method for extracting a relation triple fused with entity types specifically comprises the following steps:
1) collecting text data as training samples;
2) cleaning the training sample data collected in the step 1) to form a data set;
3) segmenting the data set formed in the step 2), and dividing the data set into a training set, a verification set and a test set according to the proportion of 7:2: 1;
4) building a deep learning network based on a BERT pre-training model, and loading pre-training parameters to obtain deep expression of training set data;
5) a Fast Gradient Method antagonistic neural network model is built after a BERT pre-training model is built, and the Fast Gradient Method antagonistic neural network model is used for improving the robustness and generalization performance of the model;
6) predicting a head entity by utilizing a head entity extraction module DNN;
7) extracting head entity characteristics from the head entities predicted in the step 6), and extracting a model by using a relation triple of a multi-head attention mechanism and a deep neural network;
8) predicting the relation and the tail entity by using a relation and tail entity extraction module DNN;
9) performing model training, storing a K-fold model for the verification set in the step 3) by adopting a K-fold cross verification method, testing the test set by using the K-fold model, and taking the average probability as a test result of the model; the model AtnFGM-MARE was output.
Further, the BERT pre-training model in the step 4) is sequentially provided with a position embedding layer, a syntax embedding layer and a token embedding layer, and then is connected with an E[cls]Layer, full connection layer and T[cls]Layers and depth expression is as follows:
H＝BERT(S) (1)
wherein S is training set text data, and H is the depth expression of the hidden state of S after the BERT pre-training model.
The expression of the Fast Gradient Method anti-neural network model built in the step 5) is as follows:
Figure BDA0003464115820000021
radv＝∈·g/||g||2 (3)
xadv＝x+radv (4)
wherein g represents a loss function after gradient updating, theta represents a parameter of the antagonistic neural network, x represents an input of the model, y represents a label corresponding to the input x, L represents a loss function of the training neural network,
Figure BDA0003464115820000022
gradient representation representing a neural network, e representing a hyper-parameter of the antagonistic network, xadvRepresenting model input after adding counterdisturbance, radvRepresenting the degree of adding the countermeasure against the disturbance.
Step 6) connecting a head entity extraction module to a Fast Gradient Method countermeasure neural network model, and predicting a head entity starting position and a head entity ending position of a head entity starting full-link layer and a head entity ending full-link layer respectively;
Figure BDA0003464115820000023
Figure BDA0003464115820000024
wherein ,xiFor depth representation of the ith character in text, Ws、We、bs、beRepresents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure BDA0003464115820000025
representing the probability that the ith character is the beginning character of the head entity,
Figure BDA0003464115820000026
representing the probability that the ith character is the head entity end character.
Step 7) acquiring head entity type representation characteristics by using a multi-head attention mechanism, and fusing the head entity characteristics, the head entity type representation characteristics and the context representation characteristics by using a characteristic fusion (characteristic addition) mode to obtain a relation triple extraction model of the deep neural network; the expression for the multi-attention mechanism is as follows:
Figure BDA0003464115820000031
Figure BDA0003464115820000032
MultiHead(Q，K，V)＝Concat(head1，head2，...，headh)WO (9)
wherein Q, K, V represents a vector obtained by linearly varying input features, dkHead, a parameter representing a control varianceiRepresenting the output of the ith attention mechanism module in the multi-head attention mechanism model,
Figure BDA0003464115820000033
W0trainable parameters representing a deep neural network.
The relation and tail entity prediction in the step 8) is specifically as follows: predicting the relation, the starting position and the ending position of the tail entity according to the relation after the characteristic fusion in the step 7), the starting full-link layer and the relation of the tail entity and the ending full-link layer of the tail entity;
Figure BDA0003464115820000034
Figure BDA0003464115820000035
wherein ,
Figure BDA0003464115820000036
represents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure BDA0003464115820000037
representing the depth representation of the k-th head entity,
Figure BDA0003464115820000038
representing a type depth representation of the kth head entity,
Figure BDA0003464115820000039
representing the probability that the ith character is the end entity start character given the relationship r,
Figure BDA00034641158200000310
representing the probability that the ith character is the end character of the tail entity given the relationship r.
Compared with the prior art, the invention has the following beneficial effects:
the triple extraction method disclosed by the invention has the advantages that through fusing an entity type neural network end-to-end model (AttnFGM-MARE), a pre-training model is adopted as the feature extraction of text context, and relational triples are intelligently extracted; the robust performance and the generalization performance of the model are improved by adopting a Fast Gradient Method antagonistic network model, the entity type characteristics of the head are fused by using a multi-head attention mechanism, and the effect of extracting the relation triplets is obtained and improved by a Deep Neural Network (DNN).
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FIG. 1 is a flow chart of a relational triple extraction model according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention is provided in connection with the accompanying drawings and the examples. It should be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other networks or combinations thereof.
As shown in fig. 1, a method for extracting relationship triples of a converged entity type specifically includes the following steps:
1) collecting text data as training samples, size N of batch-sizebAnd a learning rate α; wherein the size N of the batch-sizebThe model can process the size of data in batch, namely the number of data samples captured by one-time training, and the speed of processing one data at a time is low; the learning rate alpha is the step size of the model updating parameter, the whole optimization process is updated by the determined step size, and the function of updating the parameter is to gradually approach the optimal solution.
2) Cleaning the training sample data collected in the step 1) to form a data set
Figure BDA0003464115820000041
3) Segmenting the data set formed in the step 2), and dividing the data set into training sets according to the proportion of 7:2:1
Figure BDA0003464115820000042
Verification set
Figure BDA0003464115820000043
And test set
Figure BDA0003464115820000044
4) Building a deep learning network based on a BERT pre-training model, and loading pre-training parameters to obtain deep expression of training set data;
5) a Fast Gradient Method antagonistic neural network model is built after a BERT pre-training model is built, and the Fast Gradient Method antagonistic neural network model is used for improving the robustness and generalization performance of the model; the expression of the Fast Gradient Method versus neural network model is as follows:
Figure BDA0003464115820000045
radv＝∈·g/||g||2 (3)
xadv＝x+radv (4)
where θ represents a parameter of the antagonistic neural network, x represents an input to the model, y represents a label corresponding to the input x, L represents a loss function of the training neural network,
Figure BDA0003464115820000046
gradient representation representing a neural network, e representing a hyper-parameter of the antagonistic network, radvRepresenting the degree of adding the countermeasure disturbance;
6) predicting a head entity by utilizing a head entity extraction module DNN;
Figure BDA0003464115820000047
Figure BDA0003464115820000048
wherein ,xiFor depth representation of the ith character in text, Ws、We、bs、beRepresents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure BDA0003464115820000049
representing the probability that the ith character is the beginning character of the head entity,
Figure BDA00034641158200000410
representing the probability that the ith character is a head entity ending character;
7) extracting head entity characteristics from the head entities predicted in the step 6), and extracting a model by using a relation triple of a multi-head attention mechanism and a deep neural network; the expression for the multi-attention mechanism is as follows:
Figure BDA0003464115820000051
Figure BDA0003464115820000052
MultiHead(Q，K，V)＝Concat(head1，head2，...，headh)WO (9)
wherein Q, K, V represents a vector obtained by linearly varying input features, dkHead, a parameter representing a control varianceiRepresenting the output of the ith attention mechanism module in the multi-head attention mechanism model,
Figure BDA0003464115820000053
W0trainable parameters representing a deep neural network;
8) predicting the relation and the tail entity by using a relation and tail entity extraction module DNN;
Figure BDA0003464115820000054
Figure BDA0003464115820000055
wherein ,
Figure BDA0003464115820000056
represents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure BDA0003464115820000057
representing the depth representation of the k-th head entity,
Figure BDA0003464115820000058
representing a type depth representation of the kth head entity,
Figure BDA0003464115820000059
representing the probability that the ith character is the end entity start character given the relationship r,
Figure BDA00034641158200000510
representing the probability that the ith character is the tail entity ending character under the condition of a given relation r;
9) performing model training, storing a K-fold model for the verification set in the step 3) by adopting a K-fold cross verification method, testing the test set by using the K-fold model, and taking the average probability as a test result of the model; the model AtnFGM-MARE was output. The K-fold cross validation method comprises the steps of dividing original data into K groups, making a primary validation set for each subset data, and using the remaining K-1 subsets as training sets to obtain a K-fold model.
Computing the network output y of a Deep Neural Network (DNN) of batch-size for the deep expression H of the present application*＝DNN(H) (12)
wherein ,y*Representing the output of the neural network;
the loss of training is calculated through a loss function, and the expression of the loss function is as follows:
Figure BDA00034641158200000511
wherein ,xiRepresents the ith sample in the training set, TiRepresentative of the occurrence in the training sample xiS represents a head entity appearing in the relational triple, o represents a tail entity appearing in the relational triple, prRepresenting a probability value under a specified relationship r;
training and calculating through a loss function to update a parameter theta of the antagonistic neural network model, wherein the minimum expression of the parameter theta is as follows:
Figure BDA0003464115820000061
the foregoing is illustrative of embodiments of the present invention and it will be further appreciated by those skilled in the art that various modifications may be made without departing from the principles of the invention and that such modifications are intended to be included within the scope of the appended claims.

Claims (6)

1. A method for extracting relation triples fusing entity types is characterized in that: the method specifically comprises the following steps:
1) collecting text data as training samples;
2) cleaning the training sample data collected in the step 1) to form a data set;
3) segmenting the data set formed in the step 2), and dividing the data set into a training set, a verification set and a test set according to the proportion of 7:2: 1;
4) building a deep learning network based on a BERT pre-training model, and loading pre-training parameters to obtain deep expression of training set data;
5) a Fast Gradient Method antagonistic neural network model is built after a BERT pre-training model is built, and the Fast Gradient Method antagonistic neural network model is used for improving the robustness and generalization performance of the model;
6) predicting a head entity by utilizing a head entity extraction module DNN;
7) extracting head entity characteristics from the head entities predicted in the step 6), and extracting a model by using a relation triple of a multi-head attention mechanism and a deep neural network;
8) predicting the relation and the tail entity by using a relation and tail entity extraction module DNN;
9) performing model training, storing a K-fold model for the verification set in the step 3) by adopting a K-fold cross verification method, testing the test set by using the K-fold model, and taking the average probability as a test result of the model; the model AtnFGM-MARE was output.
2. The method for extracting relationship triples of converged entity types according to claim 1, wherein:
the BERT pre-training model in the step 4) is sequentially provided with a position embedding layer, a syntax embedding layer and a token embedding layer, and the rear connection is connected with an E[cls]Layer, full connection layer and T[cls]Layers and depth expression is as follows:
H＝BERT(S) (1)
wherein S is training set text data, and H is the depth expression of the hidden state of S after the BERT pre-training model.
3. The method for extracting relationship triples of converged entity types according to claim 1, wherein:
the expression of the Fast Gradient Method anti-neural network model built in the step 5) is as follows:
Figure FDA0003464115810000011
radv＝∈·g/||g||2 (3)
xadv＝x+radv (4)
wherein g represents a loss function after gradient updating, theta represents a parameter of the antagonistic neural network, x represents an input of the model, y represents a label corresponding to the input x, L represents a loss function of the training neural network,
Figure FDA0003464115810000027
gradient representation representing a neural network, e representing a hyper-parameter, x, of the antagonistic networkadvRepresenting model input after adding counterdisturbance, radvRepresenting the degree of adding the countermeasure against the disturbance.
4. The method for extracting relationship triples of converged entity types according to claim 1, wherein:
the step 6) is that the head entity extraction module is connected with a Fast Gradient Method confrontation neural network model, and predicts the head entity starting position and the head entity ending position of a head entity starting full-connection layer and a head entity ending full-connection layer respectively;
Figure FDA0003464115810000021
Figure FDA0003464115810000022
wherein ,xiFor depth representation of the ith character in text, Ws、We、bs、beRepresents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure FDA0003464115810000023
representing the probability that the ith character is the beginning character of the head entity,
Figure FDA0003464115810000024
representing the probability that the ith character is the head entity end character.
5. The method for extracting relationship triples of converged entity types according to claim 1, wherein:
the step 7) obtains head entity type representation characteristics by using a multi-head attention mechanism, and fuses the head entity characteristics, the head entity type representation characteristics and the context representation characteristics by using a characteristic fusion mode to obtain a relation triple extraction model of the deep neural network; the expression for the multi-attention mechanism is as follows:
Figure FDA0003464115810000025
Figure FDA0003464115810000026
MultiHead(Q，K，V)＝Concat(head1，head2，...，headh)WO (9)
wherein Q, K, V represents a vector obtained by linearly varying input features, dkParameter representing control variance, headiRepresenting the output of the ith attention mechanism module in the multi-head attention mechanism model, Wi Q、Wi K、Wi V、W0Trainable parameters representing a deep neural network.
6. The method for extracting relationship triples of converged entity types according to claim 1, wherein:
the relation and tail entity prediction in the step 8) is specifically as follows: predicting the relation, the starting position and the ending position of the tail entity according to the relation after the characteristic fusion in the step 7), the starting full-link layer and the relation of the tail entity and the ending full-link layer of the tail entity;
Figure FDA0003464115810000031
Figure FDA0003464115810000032
wherein ,
Figure FDA0003464115810000033
represents trainable parameters of the deep neural network, sigma represents sigmoid activation function,
Figure FDA0003464115810000034
representing the depth representation of the k-th head entity,
Figure FDA0003464115810000035
representing a type depth representation of the kth head entity,
Figure FDA0003464115810000036
representing the probability that the ith character is the end entity start character given the relationship r,
Figure FDA0003464115810000037
representing the probability that the ith character is the end character of the tail entity given the relationship r.
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