![]() Next, a method to combine the drug molecular structure information and drug description information to the input sentence information is proposed, and the effectiveness of utilizing drug molecular structures and drug descriptions for the relation extraction task is shown. First, a deep neural relation extraction model is prepared and its attention mechanism is analyzed. This thesis works on Drug-Drug Interactions (DDIs) from the literature as a case study and realizes relation extraction utilizing heterogeneous domain information. However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. ![]() The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. The experiments were conducted on the SemEval 2010 Task 8 dataset and the results show that the proposed method can improve the F1 value to 0.902. Secondly, inspired by the pre-training task of next-sentence prediction, we propose a concise relation extraction approach based on the fusion of sequential and structural features using the pre-training model ERNIE. Firstly, for the sequential data, we verify in detail which of the generated representations can effectively improve the performance. ![]() This study proposes a concise approach using the fused features for the relation extraction task. However, previous research has mainly focused on sequential or structural data alone, such as the shortest dependency path, ignoring the fact that fusing sequential and structural features may improve the classification performance. Recently, pre-training models that have learned prior semantic and syntactic knowledge, such as BERT and ERNIE, have enhanced the performance of relation extraction tasks. These triples can then be used to build a knowledge graph. Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. Single-word vector space models have been very successful at learning lexical information.
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