# Joint Extraction of Long-Distance Entity Relation by Aggregating Local- and Semantic-Dependent Features

Authors: Xiao Wei; Yongqi Chen
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The relations among long-distance entities are important for text understanding. Long-distance entities mean a pair of entities separated by more words in the text. Most current work of the joint extraction of entity relation is between entities that are close together. These methods are not suitable for long-distance entity relation extraction for they usually ignore the interaction between long-distance entities. In this paper, we propose Long-Distance Feature Aggregation Model (LDFAM), an end-to-end long-distance entity relation joint extraction model, which uses a Bi-GRU and weighted graph neural network to model text and learn its local- and semantic-dependent features. The long-distance dependence between entities is obtained by aggregating these two types of features. For entity recognition, we use conditional random fields to further obtain the dependency information between tags. For relation extraction, we fuse entity tag information for biaffine pairwise scoring. Experimental results on NYT and SemEval2010-Task8 datasets show that our model has achieved significant improvements compared to the baseline model. Finally, we apply the model to the field of material science to extract entity relationship knowledge from material literature. Experimental results show that benefiting from the long-distance entity relation extraction, the proposed method can get more useful knowledge from material literature and improve the knowledge coverage of material domain knowledge acquisition.


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## Body

## 1. Introduction

Entity and relation extraction is an important research topic in the field of information extraction and natural language processing. The traditional method is mainly aimed at the short-distance entity relation extraction within a sentence, and this task is regarded as a pipeline of two independent tasks, named entity recognition [1] and relation extraction [2]. In fact, in order to better understand the text, the long-distance entity relation is just as important as the short-distance entity relation.The long-distance entities may be in the same sentence or in different sentences. In either case, the text is generally a long text. For example, as shown in Figure1, there is a “/location/location/contains” relation between “Florida” and “Miami.”Figure 1
Example: there is a “/location/location/contains” relation between “Florida” and “Miami.”We can see that the head entity “Florida” is at the end of the sentence, and the tail entity “Miami” is at the beginning of the sentence, and they are far apart. The pipeline method [3, 4] usually first identifies the entity mentions “Florida” and “Miami” and then judges the relation between “Florida” and “Miami.” These methods ignore interaction between entity recognition and relation extraction tasks. Joint extraction models [5–7] are proposed to establish the association between two tasks. Although these methods reflect the advantages of joint modelling, these are all feature-based systems and therefore rely heavily on feature engineering.With the successful development of deep neural networks, more and more models automatically obtain the semantic features of texts through neural networks to realize entity recognition and relation extraction. These methods use CNN, LSTM, or Tree-LSTM to model text to obtain local semantic features [8, 9], global sequence features [10], and syntactic dependency features [11, 12].However, these methods cannot well capture the long-distance dependence between entities in a long text. When the distance between entities gradually increases, their performance will decrease significantly. The reasons are as follows: first, it is difficult to obtain complete text semantic information using only a single neural network on long text, which will result in the lack of partial semantic information. For example, using CNN can better capture local semantic information, but it is difficult to obtain global information. Using RNN can obtain sequence semantic features but ignores the syntax and semantic features. Second, their methods can cause the propagation of errors. The identified entities may be wrong, but they are directly used as the input of relation extraction, which leads to errors in relation extraction. And the appearance of redundant wrong entities will lead to the decline of recall rate.In this paper, we propose Long-Distance Feature Aggregation Model (LDFAM), an end-to-end model to extract long-distance entity relation. First, the word embedding representation is automatically obtained through BERT, and the global sequence feature and the syntactic dependence feature of the text are captured by Bi-GRU and graph neural network, respectively. Then, these two kinds of features are aggregated to obtain the long-distance dependence between entities. Finally, CRF is used to further obtain the dependency rules between entity mention tags and perform entity mention identification and then predict the entity relation through biaffine paired scoring. Our contribution has three parts:(i)
We integrate the global semantic information of the text and the long-distance grammatical dependence feature to generate the long-distance entity dependence feature(ii)
We map the initial entity mentions through two independent potential spaces and aggregate similar entity mentions into entity representations, avoiding data redundancy problems caused by pairs of similar entity mentions(iii)
We evaluate the method on two public relation extraction datasets NYT and SemEval2010-Task8, and we apply the model to the field of materials to extract entity relation in material literature. Experimental results show that our method significantly improves the performance of joint extraction of long-distance entity relation and achieves a new state-of-art on both datasets


## 2. Related Work

### 2.1. Extraction of Entity Relation

The early joint extraction methods of entity relations are all based on feature engineering. Collins [5] proposed an incremental joint framework that uses structure perceptron [13, 14] and effective cluster search to extract mentioned entities and relations at the same time. Unlike traditional label-based methods, they use a segmented decoder based on the idea of a semi-Markov chain. Miwa and Sasaki [6] proposed a structured learning method based on history, which maps the entity relation extraction task to a simple form filling problem by integrating global features, appropriate learning methods, and search order. These methods require the design of complex features and rely heavily on NLP tools, which may lead to error propagation.Most of the current methods use deep neural networks to achieve joint extraction. Miwa and Bansal [15] extract entity relation based on word sequence information and dependency tree structure information by using bidirectional LSTM and Tree-LSTM, and the dependency layer focuses on the shortest path of the two entities in the dependency tree. The shortest path has been proved to be very effective in the research of relation classification. However, the long dependency of the label is ignored when predicting the entity. The LSTM decoder [16] is optimized to solve the long dependency problem of tags. Zheng et al. [10] provide a new idea of joint extraction, which converts the extraction problem into a sequence labelling problem. The label of the entity object is composed of three parts: word position, relation type, and relation role. The correlation of the entity label is enhanced through the LSTM decoding layer with bias objective function. However, using the nearest combination method when merging the tags will cause some problems. Based on this tagging strategy, an attention mechanism [17] is introduced into the model to enhance the ability to encode relational words in the text. Adversarial training is adopted in the model training process.The above methods all use a single neural network to model the text, which makes it difficult to obtain complete semantic information, resulting in the lack of semantic information. This problem becomes more serious when modelling long texts. The limitation of the model itself makes the model’s performance on long texts not as good as on short texts. For example, the RNN network has gradient disappearance. This makes it more difficult to obtain the full-text semantics.


### 2.2. Long-Distance Relation Extraction

Cross-sentence relation extraction is the research that has only been put forward in recent years. Zhu et al. [18] construct a fully connected graph for the entities in the text and use the features of GNN with generated parameters to automatically learn edges to realize multihop relation inference. This alleviates the long-distance dependence problem of entity relation extraction to a certain extent. Christopoulou et al. [19] use heuristic rules to create different types of edges for different nodes, thereby using different types of nodes and edges to create document-level graphs. The reasoning mechanism at the edge of the graph uses multi-instance learning to learn the relations within and between sentences. Wang et al. [20] encode both the overall and local information of the document and then concatenate the local semantic and global semantic representation of the entity to obtain the representation of the entity pair, which is combined with the document theme to classify relation. Nan et al. [21] regards the graph structure as a latent variable. Based on structural attention, a variant of the matrix tree theorem is used to generate a specific-dependent structure to capture the nonlocal interactive information between entities. They also proposed an iterative strategy to dynamically construct a latent structure based on the previous iteration which can improve the aggregation of information in the document and better perform multihop reasoning.Most of these methods all use graph neural network to achieve cross-sentence relation extraction. They pay more attention to relation extraction, such as multihop relation inference, but ignore the importance of entity information for relation extraction. Making full use of entity semantic information can effectively improve the performance of relation extraction.


## 2.1. Extraction of Entity Relation

The early joint extraction methods of entity relations are all based on feature engineering. Collins [5] proposed an incremental joint framework that uses structure perceptron [13, 14] and effective cluster search to extract mentioned entities and relations at the same time. Unlike traditional label-based methods, they use a segmented decoder based on the idea of a semi-Markov chain. Miwa and Sasaki [6] proposed a structured learning method based on history, which maps the entity relation extraction task to a simple form filling problem by integrating global features, appropriate learning methods, and search order. These methods require the design of complex features and rely heavily on NLP tools, which may lead to error propagation.Most of the current methods use deep neural networks to achieve joint extraction. Miwa and Bansal [15] extract entity relation based on word sequence information and dependency tree structure information by using bidirectional LSTM and Tree-LSTM, and the dependency layer focuses on the shortest path of the two entities in the dependency tree. The shortest path has been proved to be very effective in the research of relation classification. However, the long dependency of the label is ignored when predicting the entity. The LSTM decoder [16] is optimized to solve the long dependency problem of tags. Zheng et al. [10] provide a new idea of joint extraction, which converts the extraction problem into a sequence labelling problem. The label of the entity object is composed of three parts: word position, relation type, and relation role. The correlation of the entity label is enhanced through the LSTM decoding layer with bias objective function. However, using the nearest combination method when merging the tags will cause some problems. Based on this tagging strategy, an attention mechanism [17] is introduced into the model to enhance the ability to encode relational words in the text. Adversarial training is adopted in the model training process.The above methods all use a single neural network to model the text, which makes it difficult to obtain complete semantic information, resulting in the lack of semantic information. This problem becomes more serious when modelling long texts. The limitation of the model itself makes the model’s performance on long texts not as good as on short texts. For example, the RNN network has gradient disappearance. This makes it more difficult to obtain the full-text semantics.


## 2.2. Long-Distance Relation Extraction

Cross-sentence relation extraction is the research that has only been put forward in recent years. Zhu et al. [18] construct a fully connected graph for the entities in the text and use the features of GNN with generated parameters to automatically learn edges to realize multihop relation inference. This alleviates the long-distance dependence problem of entity relation extraction to a certain extent. Christopoulou et al. [19] use heuristic rules to create different types of edges for different nodes, thereby using different types of nodes and edges to create document-level graphs. The reasoning mechanism at the edge of the graph uses multi-instance learning to learn the relations within and between sentences. Wang et al. [20] encode both the overall and local information of the document and then concatenate the local semantic and global semantic representation of the entity to obtain the representation of the entity pair, which is combined with the document theme to classify relation. Nan et al. [21] regards the graph structure as a latent variable. Based on structural attention, a variant of the matrix tree theorem is used to generate a specific-dependent structure to capture the nonlocal interactive information between entities. They also proposed an iterative strategy to dynamically construct a latent structure based on the previous iteration which can improve the aggregation of information in the document and better perform multihop reasoning.Most of these methods all use graph neural network to achieve cross-sentence relation extraction. They pay more attention to relation extraction, such as multihop relation inference, but ignore the importance of entity information for relation extraction. Making full use of entity semantic information can effectively improve the performance of relation extraction.


## 3. Model

The Figure2 illustrates the overall architecture of the proposed joint extraction model of long-distance entity relation. Firstly, we use BERT [22] to represent the initial embedding of the text. With the initial embedding representation of words, we use Bi-GRU and weighted GNN to extract global sequence- and local-dependent features, respectively, then further obtain the dependent features of the entity tag through CRF, then map each word to two different vector spaces and predict the relation of each word pair.Figure 2
The overall architecture of the proposed joint extraction model of long-distance entity relation.


### 3.1. Encoding

We use BERT as the initial encoder. After BERT was recently proposed, it has achieved excellent performance in all directions in the NLP field. BERT uses transformer as the main framework of the model, which can better capture the bidirectional semantic relation in sentences. We segment sentences according to words.(1)Text=w1,w2,w3,⋯,wn,where wi represents the ith word in the input text. According to the BERT pretraining language model, the initial embedding representation ei of the word is obtained. The input text can be represented by several word vectors.
(2)Textembedding=e1,e2,e3,⋯,en.


### 3.2. Feature Acquisition

As shown in Figure2, we use Bi-GRU and graph neural network to obtain the global semantic features and local grammatical dependency features of words, respectively. Bi-GRU [23] is a variant of Bi-LSTM [24], which replaces the forget gate and input gate in LSTM with update gates and merges the cell state and the hidden state. Bi-GRU can well obtain the global sequence semantic features in text. Taking the initial text encoding as input, we use Bi-GRU to further encode the text.
(3)Featureseq=h10,h20,h30,⋯,hn0,where hi0 represents the global sequence semantic features of the ith word.Graph neural networks [25, 26] were first proposed in Scarselli et al. [27]. The graph neural network can obtain local dependency features very well, and it is also helpful to obtain remote dependency features. Since our task is to extract long-distance entity relation, long text contains rich syntax and semantic information. By acquiring syntactic and semantic features and disseminating them in different nodes, remote semantic features can be acquired, which is more conducive to long text modelling. Since the original input text is a sequence structure, not a graph structure, we use spaCy to grammatically analyze the text and build a grammatical dependency tree. In order to avoid the lack of semantic information of the central node in the propagation of graph neural network, we add reflexive dependency to each node. Using the dependency relation in the dependency tree as the adjacency matrix, we assign different weight values to different dependency relation and then use the weighted graph neural network to extract local dependency features.
(4)hul=σ∑v∈NuWlhvl+bl,where hul represents the hidden layer feature of the word u in the lth layer and Nu represents a word that has a dependency relation with u, and σ represents the activation function. Wl and bl, respectively, represent the weight and bias of layer l, which are both learnable neural network parameters.


### 3.3. Entity Recognition

Based on the sequence- and local-dependent semantic features extracted by Bi-GRU and the weighted graph neural network, we use a two-layer linear layer to identify the entity reference of the word and obtain the initial tag score of the word.(5)Pu=softmaxWlinearhul+blinear,where Pu represents the initial tag score of word u. Then, use CRF [28] to further obtain the dependency relation between entity mention tags. We learn the transition probability A between entity mention tags through the CRF layer and calculate the score of each possible tag sequence.
(6)sText,y=∑i=0nAyi,yi+1+∑i=1nPi,yi,where y represents the label sequence predicted by Text and Ai,j represents the transition probability from tag i to tag j.


### 3.4. Relation Extraction

We use entity tag information to guide relation extraction. In order to reduce the error propagation between entity recognition and relationship extraction, we will map entity references into two different latent spaces to generate entity-level representations. First, the hidden layer feature and the mention label feature are spliced.(7)hul+1=hul;Pu.Then, use two feed-forward neural networks (FFNN) for each word to project into two independent latent spaces, corresponding to the head entity and tail entity of the entity pair, respectively.(8)xuhead=σWheadhul+1+bhead,(9)xutail=σWtailhul+1+btail,where σ represents the activation function. Whead and Wtail are the parameters of the two FFNNs, respectively, and xuhead and xutail represent the resulted head/tail representations for the word u.Then, a classifier calculates the reference-level paired confidence score and obtains the entity-level paired score.(10)scoreehead,etail=log∑i∈Ehead,j∈EtailWrelxihead;xitail+brel,where Ehead and Etail, respectively, represent the set of mentions of entities ehead and etail.


### 3.5. Train Details

We use two kinds of loss in the model: entity loss and relation loss. For the entity losslosse, we use (PAD, CLS, Begin, Inside, Out, X) tagging for the ground-truth labels, and the label of each word belongs to one of the five classes. We use the cross-entropy loss function in training. For the relation loss lossr, we use the one-hot relation vector as the ground truth label of each entity pair. Similarly, we use the cross-entropy loss function in training. In addition, we set a trainable relation weight in the experiment to learn the interaction between entity and relation. Finally, the total loss is calculated as the sum of the entity loss and the relation loss:
(11)losstotal=losse+αlossr.


## 3.1. Encoding

We use BERT as the initial encoder. After BERT was recently proposed, it has achieved excellent performance in all directions in the NLP field. BERT uses transformer as the main framework of the model, which can better capture the bidirectional semantic relation in sentences. We segment sentences according to words.(1)Text=w1,w2,w3,⋯,wn,where wi represents the ith word in the input text. According to the BERT pretraining language model, the initial embedding representation ei of the word is obtained. The input text can be represented by several word vectors.
(2)Textembedding=e1,e2,e3,⋯,en.


## 3.2. Feature Acquisition

As shown in Figure2, we use Bi-GRU and graph neural network to obtain the global semantic features and local grammatical dependency features of words, respectively. Bi-GRU [23] is a variant of Bi-LSTM [24], which replaces the forget gate and input gate in LSTM with update gates and merges the cell state and the hidden state. Bi-GRU can well obtain the global sequence semantic features in text. Taking the initial text encoding as input, we use Bi-GRU to further encode the text.
(3)Featureseq=h10,h20,h30,⋯,hn0,where hi0 represents the global sequence semantic features of the ith word.Graph neural networks [25, 26] were first proposed in Scarselli et al. [27]. The graph neural network can obtain local dependency features very well, and it is also helpful to obtain remote dependency features. Since our task is to extract long-distance entity relation, long text contains rich syntax and semantic information. By acquiring syntactic and semantic features and disseminating them in different nodes, remote semantic features can be acquired, which is more conducive to long text modelling. Since the original input text is a sequence structure, not a graph structure, we use spaCy to grammatically analyze the text and build a grammatical dependency tree. In order to avoid the lack of semantic information of the central node in the propagation of graph neural network, we add reflexive dependency to each node. Using the dependency relation in the dependency tree as the adjacency matrix, we assign different weight values to different dependency relation and then use the weighted graph neural network to extract local dependency features.
(4)hul=σ∑v∈NuWlhvl+bl,where hul represents the hidden layer feature of the word u in the lth layer and Nu represents a word that has a dependency relation with u, and σ represents the activation function. Wl and bl, respectively, represent the weight and bias of layer l, which are both learnable neural network parameters.


## 3.3. Entity Recognition

Based on the sequence- and local-dependent semantic features extracted by Bi-GRU and the weighted graph neural network, we use a two-layer linear layer to identify the entity reference of the word and obtain the initial tag score of the word.(5)Pu=softmaxWlinearhul+blinear,where Pu represents the initial tag score of word u. Then, use CRF [28] to further obtain the dependency relation between entity mention tags. We learn the transition probability A between entity mention tags through the CRF layer and calculate the score of each possible tag sequence.
(6)sText,y=∑i=0nAyi,yi+1+∑i=1nPi,yi,where y represents the label sequence predicted by Text and Ai,j represents the transition probability from tag i to tag j.


## 3.4. Relation Extraction

We use entity tag information to guide relation extraction. In order to reduce the error propagation between entity recognition and relationship extraction, we will map entity references into two different latent spaces to generate entity-level representations. First, the hidden layer feature and the mention label feature are spliced.(7)hul+1=hul;Pu.Then, use two feed-forward neural networks (FFNN) for each word to project into two independent latent spaces, corresponding to the head entity and tail entity of the entity pair, respectively.(8)xuhead=σWheadhul+1+bhead,(9)xutail=σWtailhul+1+btail,where σ represents the activation function. Whead and Wtail are the parameters of the two FFNNs, respectively, and xuhead and xutail represent the resulted head/tail representations for the word u.Then, a classifier calculates the reference-level paired confidence score and obtains the entity-level paired score.(10)scoreehead,etail=log∑i∈Ehead,j∈EtailWrelxihead;xitail+brel,where Ehead and Etail, respectively, represent the set of mentions of entities ehead and etail.


## 3.5. Train Details

We use two kinds of loss in the model: entity loss and relation loss. For the entity losslosse, we use (PAD, CLS, Begin, Inside, Out, X) tagging for the ground-truth labels, and the label of each word belongs to one of the five classes. We use the cross-entropy loss function in training. For the relation loss lossr, we use the one-hot relation vector as the ground truth label of each entity pair. Similarly, we use the cross-entropy loss function in training. In addition, we set a trainable relation weight in the experiment to learn the interaction between entity and relation. Finally, the total loss is calculated as the sum of the entity loss and the relation loss:
(11)losstotal=losse+αlossr.


## 4. Experiments

In this section, we present the experimental results of the proposed model. We first describe the relevant settings of the experiment, the datasets, and the baselines for our comparison. Then, we do a quantitative analysis on the accuracy of the relation extraction between entities at different distances. Through the displayed experimental results, the improvement of the proposed model on the joint extraction of long-distance entity relations is verified.


### 4.1. Experimental Settings

In our implementation, we first split the text into words. Each word has three values:inputID, mask, segment, where inputID represents the number of the word in the BERT dictionary, mask represents which sentence the word belongs to, and segment represents which sentence the word belongs to. The word embedding representation (800 d) of each word is generated through the BERT pretraining language model and used as the input embedding of each word. For the construction of the text dependency tree, we use the spaCy grammatical analysis tool to obtain the grammatical dependency of the entire text word and construct the dependency matrix.Since we are targeting distant entities, the text we choose is longer. We use 2-layer Bi-GRU with 400 units to represent the text. Then, we use 2-layer GNN with 256 feature sizes to further model the text. In the entity recognition part, we use a linear layer with a feature size of 256. In the relation prediction part, we also use two two-layer feedforward neural networks with 256 feature sizes. During training, we set the dropout rate to 0.5 and the learning rate (lr) to 0.001. We use the Adam optimizer to train the model and implement it under TensorFlow. More specifics of the experimental settings are provided, as are shown in Table1.Table 1
The specifics of the experimental settings.
SettingsBi-GRU (2 layers)400GNN (2 layers)256Linear layer256FFNN (2 layers)256Dropout0.5Learning rate0.001OptimizerAdam


### 4.2. Datasets

We use NYT and SemEval2010-Task8 datasets to evaluate the proposed model. There are a lot of short texts in NYT and SemEval2010-Task8 datasets. These texts cannot be verified by our experiment. Therefore, we chose those longer texts as the dataset. Due to the different amounts of data in different relation in the datasets, we select 7 relations with a larger amount of data, in order to reduce the interference error of small samples. We divide the datasets into 4 categories according to the distance of the entities: between 10 and 20 (10~20), between 20 and 30 (20~30), between 30 and 40 (30~40), and more than 40 (40~). Between 10 and 20 means the distance between entities differs by 10 to 20 words. Between 20 and 30 means the distance between entities differs by 20 to 30 words. Between 30 and 40 means the distance between entities differs by 30 to 40 words. More than 40 means the distance between entities is greater than 40 words. The statistics of NYT and SemEval2010-Task8 are shown in Table2. In the detailed analysis, we discussed the different categories of results.Table 2
Statistics of datasets.
NYTSemEval2010-Task8TrainTestTrainTest10~20180161336302249620~3013776648202433930~405568296105315940~2640120949174Total40000240070481168


### 4.3. Baseline and Evaluation Metrics

NovelTagging: Zheng et al. [10] proposed a new tagging scheme, which focuses on extracting the triples composed of two entities and the relation between these two entities. By directly modelling the triples, the entity relation joint extraction is converted into a sequence labelling problem, and each word in the sentence is labelled with the entity and relation category. They also make use of the global sequence feature of text, but lack the long-distance syntax dependency feature based on graph. The purpose of comparison with them is to show the importance of using graph to obtain long-distance semantic features.MultiDecoder: Zeng et al. [29] proposed an advanced method that treats relation extraction as a seq-seq problem and used multiple dynamic decoders to extract duplicate relational triples to solve the problem of repeated combination of entity references. The purpose of comparison with them is to show that it is superior to using multiple dynamic decoders to aggregate similar entity references for relation extraction. The advantage of their method is that they can aggregate the representation of similar entities and reduce the emergence of redundant entities to a certain extent. However, their method cannot obtain the dependency features between remote entities and cannot well identify the remote entity relations in the text.We use precision, recall, and standardF1 scores to evaluate the results. If and only if both entities and relation are the same as the ground truth, the predicted triplet is considered correct.


### 4.4. Quantitative Results

Table3 shows the precision, recall, and F1 scores of NovelTagging, MultiDecoder, and our model on the NYT dataset. On 10~20, our model outperforms NovelTagging by 0.171 and MultiDecoder by 0.129. The distance is still relatively close between 10 and 20, so the improvement in this range is not as good as the long distance. With the increase of the distance between entities, our model is still greatly improved compared with the baseline model. When the distance is greater than 40, it appears too large, and the performance will decrease to a certain extent at this time.Table 3
Results for NYT.
NovelTaggingMultiDecoderOur modelPRF1PRF1PRF110~200.5990.4530.5160.6450.4920.5580.7310.6480.68720~300.4080.2380.3010.5850.3290.4210.6950.6640.67930~400.3980.2010.2670.4010.2860.3340.6250.5170.56640~0.3170.250.280.3670.2630.3060.5490.4340.485Similar results can be found on the SemEval2010-Task8 dataset. Table4 shows the precision, recall, and F1 scores of NovelTagging, MultiDecoder, and our model on the SemEval2010-Task8 dataset. There is a significant improvement on all four divided datasets with different distances.Table 4
Results for SemEval2010-Task8.
NovelTaggingMultiDecoderOur modelPRF1PRF1PRF110~200.4920.2170.3010.4360.3920.4130.5820.4260.49220~300.4010.2020.2690.4260.4080.4170.5110.3970.44730~400.3790.1740.2390.3120.3290.3200.4360.3140.36540~0.3200.1520.2060.2950.2810.2880.3910.3090.345We summarize the results of entity-to-relation predictions at different distances. Figure3 shows the extraction results of different distances on NYT (left) and SemEval2010-Task8 (right) datasets. From the general trend in Figure 3, we can see that F1 gradually decreases with the increase of the distance between entities. This means that the distance between entities does increase the difficulty of relation extraction. Both NovelTagging and MultiDecoder use bidirectional RNN network to obtain global sequence semantic features in the encoding stage, while MultiDecoder uses multiple dynamic decoders to obtain overlapping relations in decoding. Therefore, NovelTagging and MultiDecoder perform similarly on the single relational dataset NYT. On the dataset SemEval2010-Task8 with overlapping relationship, the performance of MultiDecoder is better than NovelTagging.Figure 3
Results by different distance.Based on the global sequence semantic features, our model further constructs the syntax dependency structure and obtains the long-distance dependency features between entities through graph neural network, so it has better performance on the single relational dataset NYT. Further, we aggregate entity references by mapping them to two different representation spaces and extract relation only after generating entity representations. Therefore, our model also has excellent performance on the overlapping dataset SemEval2010-Task8.


### 4.5. Application

In order to prove the practicability of our model, we apply the model to the field of material science and use it to extract the entity relation knowledge from the material literature. By analysing the material literature from special steel, aluminium matrix composite, and thermal barrier coating, we divide the entities in the field of material science into three types: component entity, process entity, and performance entity. The component entity represents the constituent of the material, and the process entity represents the manufacturing of the material. In the field of materials, we mainly explore the relation between components and properties, process, and properties, so as to judge which components and processes can improve the performance indicators of materials.Firstly, we construct the dataset of material science based on the patent text and abstract of research paper of the above three material domains. The division of the dataset is shown in Table5. We train the model on the training set and testing set, so that the model can fully learn the entity and relational semantic features in the material field. And then use the learned semantic features to verify on the verification set to judge the performance of the model. Similarly, we use precision, recall, and F1 scores to measure model performance.Table 5
Datasets of materials.
DatasetTrainTestValidQuantity70110190In the selection of comparison models, we choose Bi-GRU model which only considers the global sequence features of entities and GNN model which only considers dependency features. However, our model combines global sequence features and long-distance dependence features. In order to eliminate the influence of the number of network layers on the results, the two-layer structure is adopted in both comparison models.Table6 shows the experimental results on the material dataset. Because material dataset aims at a specific domain and has single semantic features and few types of relationships, compared with the public datasets NYT and SemEval2010-Taks8, experimental results on the material domain dataset we constructed have higher performance. It can be seen from the data in Table 6 that Bi-GRU network and GNN network only consider one semantic feature and do not integrate the two features. Our model fully integrates these two features, so it has better performance. Further analysis on the whole, by comparing F1 scores, we can find that GNN has a greater impact on long-distance entity relation extraction than Bi-GRU network. This shows that for the task of long-distance entity relation extraction, GNN network can better obtain the features of long-distance dependence, so as to judge the entity relation more accurately. We can also find that the experimental results have a greater improvement in the recall rate. This is because in the relation extraction part, we aggregate mention into entity representations through two independent latent spaces, which effectively reduces the generation of wrong results, so we can get a higher recall rate and obtain more relation between components and properties and process and properties from the material literature. The experimental results also show that benefiting from the long-distance entity relation extraction, the proposed method can get more useful knowledge from material literature and improve the knowledge coverage of material domain knowledge acquisition.Table 6
Results for material dataset.
PRF1Bi-GRU (2 layers)0.3810.3900.372GNN (2 layers)0.7810.7680.771Our model0.7560.8100.778


## 4.1. Experimental Settings

In our implementation, we first split the text into words. Each word has three values:inputID, mask, segment, where inputID represents the number of the word in the BERT dictionary, mask represents which sentence the word belongs to, and segment represents which sentence the word belongs to. The word embedding representation (800 d) of each word is generated through the BERT pretraining language model and used as the input embedding of each word. For the construction of the text dependency tree, we use the spaCy grammatical analysis tool to obtain the grammatical dependency of the entire text word and construct the dependency matrix.Since we are targeting distant entities, the text we choose is longer. We use 2-layer Bi-GRU with 400 units to represent the text. Then, we use 2-layer GNN with 256 feature sizes to further model the text. In the entity recognition part, we use a linear layer with a feature size of 256. In the relation prediction part, we also use two two-layer feedforward neural networks with 256 feature sizes. During training, we set the dropout rate to 0.5 and the learning rate (lr) to 0.001. We use the Adam optimizer to train the model and implement it under TensorFlow. More specifics of the experimental settings are provided, as are shown in Table1.Table 1
The specifics of the experimental settings.
SettingsBi-GRU (2 layers)400GNN (2 layers)256Linear layer256FFNN (2 layers)256Dropout0.5Learning rate0.001OptimizerAdam


## 4.2. Datasets

We use NYT and SemEval2010-Task8 datasets to evaluate the proposed model. There are a lot of short texts in NYT and SemEval2010-Task8 datasets. These texts cannot be verified by our experiment. Therefore, we chose those longer texts as the dataset. Due to the different amounts of data in different relation in the datasets, we select 7 relations with a larger amount of data, in order to reduce the interference error of small samples. We divide the datasets into 4 categories according to the distance of the entities: between 10 and 20 (10~20), between 20 and 30 (20~30), between 30 and 40 (30~40), and more than 40 (40~). Between 10 and 20 means the distance between entities differs by 10 to 20 words. Between 20 and 30 means the distance between entities differs by 20 to 30 words. Between 30 and 40 means the distance between entities differs by 30 to 40 words. More than 40 means the distance between entities is greater than 40 words. The statistics of NYT and SemEval2010-Task8 are shown in Table2. In the detailed analysis, we discussed the different categories of results.Table 2
Statistics of datasets.
NYTSemEval2010-Task8TrainTestTrainTest10~20180161336302249620~3013776648202433930~405568296105315940~2640120949174Total40000240070481168


## 4.3. Baseline and Evaluation Metrics

NovelTagging: Zheng et al. [10] proposed a new tagging scheme, which focuses on extracting the triples composed of two entities and the relation between these two entities. By directly modelling the triples, the entity relation joint extraction is converted into a sequence labelling problem, and each word in the sentence is labelled with the entity and relation category. They also make use of the global sequence feature of text, but lack the long-distance syntax dependency feature based on graph. The purpose of comparison with them is to show the importance of using graph to obtain long-distance semantic features.MultiDecoder: Zeng et al. [29] proposed an advanced method that treats relation extraction as a seq-seq problem and used multiple dynamic decoders to extract duplicate relational triples to solve the problem of repeated combination of entity references. The purpose of comparison with them is to show that it is superior to using multiple dynamic decoders to aggregate similar entity references for relation extraction. The advantage of their method is that they can aggregate the representation of similar entities and reduce the emergence of redundant entities to a certain extent. However, their method cannot obtain the dependency features between remote entities and cannot well identify the remote entity relations in the text.We use precision, recall, and standardF1 scores to evaluate the results. If and only if both entities and relation are the same as the ground truth, the predicted triplet is considered correct.


## 4.4. Quantitative Results

Table3 shows the precision, recall, and F1 scores of NovelTagging, MultiDecoder, and our model on the NYT dataset. On 10~20, our model outperforms NovelTagging by 0.171 and MultiDecoder by 0.129. The distance is still relatively close between 10 and 20, so the improvement in this range is not as good as the long distance. With the increase of the distance between entities, our model is still greatly improved compared with the baseline model. When the distance is greater than 40, it appears too large, and the performance will decrease to a certain extent at this time.Table 3
Results for NYT.
NovelTaggingMultiDecoderOur modelPRF1PRF1PRF110~200.5990.4530.5160.6450.4920.5580.7310.6480.68720~300.4080.2380.3010.5850.3290.4210.6950.6640.67930~400.3980.2010.2670.4010.2860.3340.6250.5170.56640~0.3170.250.280.3670.2630.3060.5490.4340.485Similar results can be found on the SemEval2010-Task8 dataset. Table4 shows the precision, recall, and F1 scores of NovelTagging, MultiDecoder, and our model on the SemEval2010-Task8 dataset. There is a significant improvement on all four divided datasets with different distances.Table 4
Results for SemEval2010-Task8.
NovelTaggingMultiDecoderOur modelPRF1PRF1PRF110~200.4920.2170.3010.4360.3920.4130.5820.4260.49220~300.4010.2020.2690.4260.4080.4170.5110.3970.44730~400.3790.1740.2390.3120.3290.3200.4360.3140.36540~0.3200.1520.2060.2950.2810.2880.3910.3090.345We summarize the results of entity-to-relation predictions at different distances. Figure3 shows the extraction results of different distances on NYT (left) and SemEval2010-Task8 (right) datasets. From the general trend in Figure 3, we can see that F1 gradually decreases with the increase of the distance between entities. This means that the distance between entities does increase the difficulty of relation extraction. Both NovelTagging and MultiDecoder use bidirectional RNN network to obtain global sequence semantic features in the encoding stage, while MultiDecoder uses multiple dynamic decoders to obtain overlapping relations in decoding. Therefore, NovelTagging and MultiDecoder perform similarly on the single relational dataset NYT. On the dataset SemEval2010-Task8 with overlapping relationship, the performance of MultiDecoder is better than NovelTagging.Figure 3
Results by different distance.Based on the global sequence semantic features, our model further constructs the syntax dependency structure and obtains the long-distance dependency features between entities through graph neural network, so it has better performance on the single relational dataset NYT. Further, we aggregate entity references by mapping them to two different representation spaces and extract relation only after generating entity representations. Therefore, our model also has excellent performance on the overlapping dataset SemEval2010-Task8.


## 4.5. Application

In order to prove the practicability of our model, we apply the model to the field of material science and use it to extract the entity relation knowledge from the material literature. By analysing the material literature from special steel, aluminium matrix composite, and thermal barrier coating, we divide the entities in the field of material science into three types: component entity, process entity, and performance entity. The component entity represents the constituent of the material, and the process entity represents the manufacturing of the material. In the field of materials, we mainly explore the relation between components and properties, process, and properties, so as to judge which components and processes can improve the performance indicators of materials.Firstly, we construct the dataset of material science based on the patent text and abstract of research paper of the above three material domains. The division of the dataset is shown in Table5. We train the model on the training set and testing set, so that the model can fully learn the entity and relational semantic features in the material field. And then use the learned semantic features to verify on the verification set to judge the performance of the model. Similarly, we use precision, recall, and F1 scores to measure model performance.Table 5
Datasets of materials.
DatasetTrainTestValidQuantity70110190In the selection of comparison models, we choose Bi-GRU model which only considers the global sequence features of entities and GNN model which only considers dependency features. However, our model combines global sequence features and long-distance dependence features. In order to eliminate the influence of the number of network layers on the results, the two-layer structure is adopted in both comparison models.Table6 shows the experimental results on the material dataset. Because material dataset aims at a specific domain and has single semantic features and few types of relationships, compared with the public datasets NYT and SemEval2010-Taks8, experimental results on the material domain dataset we constructed have higher performance. It can be seen from the data in Table 6 that Bi-GRU network and GNN network only consider one semantic feature and do not integrate the two features. Our model fully integrates these two features, so it has better performance. Further analysis on the whole, by comparing F1 scores, we can find that GNN has a greater impact on long-distance entity relation extraction than Bi-GRU network. This shows that for the task of long-distance entity relation extraction, GNN network can better obtain the features of long-distance dependence, so as to judge the entity relation more accurately. We can also find that the experimental results have a greater improvement in the recall rate. This is because in the relation extraction part, we aggregate mention into entity representations through two independent latent spaces, which effectively reduces the generation of wrong results, so we can get a higher recall rate and obtain more relation between components and properties and process and properties from the material literature. The experimental results also show that benefiting from the long-distance entity relation extraction, the proposed method can get more useful knowledge from material literature and improve the knowledge coverage of material domain knowledge acquisition.Table 6
Results for material dataset.
PRF1Bi-GRU (2 layers)0.3810.3900.372GNN (2 layers)0.7810.7680.771Our model0.7560.8100.778


## 5. Conclusion

In this paper, we present Long-Distance Feature Aggregation Model (LDFAM), an end-to-end long-distance entity relation joint extraction model. We use Bi-GRU and graph neural networks to obtain text sequence features, local features, and grammatical- and semantic-dependent features, respectively. Then, the conditional random field is used to further obtain the transfer dependency between the mention tags, so as to improve the accuracy of entity recognition. In the relation prediction, we combine the mention label information of the words to map each word to two different potential independent spaces, corresponding to the head entity and the tail entity, and finally use the paired scoring function to score the entity-level relation. We evaluated the proposed method on NYT and SemEval2010-Task8 datasets. The results show that the performance of our method is better than the previous methods in the joint extraction of long-distance entity relations. Finally, the proposed method is verified on the dataset in the field of materials. The results show that the use of GNN is more helpful to improve the precision of the model, and the latent mapping space is more helpful to improve the recall rate.



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*Source: 3763940-2022-03-16.xml*