# An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
###### Abstract  
Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model. mask selector; relation extraction; attention mechanism; threshold; multi-feature fusion +
Footnote †: journal: Medical  
can used for relation extraction with some appropriate modifications. The convolutional neural networks was first applied to relation classification tasks in the paper [1]. Although its network structure was very simple, it was significantly improved compared to non-neural network methods. The bidirectional recurrent neural networks was used to handle sentences and capture more features in the paper [2] that detected word-level cotext information between entity pair, because of which the final relation extraction effect is also improved. In addition, the paper [3] made use of the Bi-LSTM model in the relation extraction task, and achieved good results. It utilized attention mechanism to reduce the influence of words noisy in the sentence, and enhance the influence of keywords. At present, an end-to-end approach often was adopted to entity relation extraction tasks, which means identifying named entities and extracting semantic relation at the same time. The training methods are divided into pipeline style and joint learning. The pipeline learning method refers to the extracting relation of entities on the basis of the entity recognition. The joint learning method is mainly based on the end-to-end model of the neural network, sharing the word embedding representation, and jointly optimizing the entity recognition and ERE. Existing models mostly rely on static context methods. A single vector representation is selected as the context of the sentence in the feature extraction layer, which leads to the problem of missing context, thereby limiting the effect of relation extraction. To address the problem, the model in this paper extends SPERT [4] model to achieve entity and relation joint task. The main contributions of this paper can be summarized as follows:  
* This paper proposes an entity relation extraction method based on dynamic context that can address the text context missing issue of existing models. With the help of dynamic context, the proposed method can efficiently extract context representation of sentences, yielding a wonderful result compared with prior studies on public datasets. * This paper designs a threshold selector, which can (1) dynamically choose text context area with entity pairs position for sentences, and (2) suppress the negative influence of adjacent entity pairs. * This paper proposes a Bi-LSTM combined with self-attention (Bi-LSTM_ATT). By using Bi-LSTM_ATT, learning long-term dependencies, and focusing on significant region of sentence, we greatly improve the compatibility for longer texts, while maintaining the effect of extraction for normal sentences. * This paper uses multi-feature fusion (MFF) to enhance context representation. The entity tags are added into feature fusion layer, which is combined with others and fed into relation extraction module.