Year: 2024
Type: article
Abstract: Relation extraction, a crucial task in natural language processing (NLP) for constructing knowledge graphs, entails extracting relational semantics between pairs of entities within a sentence. Given the intricacy of language, a single sentence often encompasses multiple entities that mutually influence one another. Recently, various iterations of recurrent neural networks (RNNs) have been introduced into relation extraction tasks, where the efficacy of neural network structures directly influences task performance. However, many neural networks necessitate manual determination of optimal parameters and network architectures, resulting in limited generalization capabilities for specific tasks. In this paper, we formally define the context-based relation extraction problem and propose a solution utilizing neural architecture search (NAS) to optimize RNN. Specifically, NAS employs an RNN controller to delineate an RNN cell, yielding an optimal structure to represent all relationships, thereby aiding in extracting relationships between target entities. Additionally, to enhance relation extraction performance, we leverage the XLNet pretrained model to comprehensively capture the semantic features of the sentence. Extensive experiments conducted on a real-world dataset containing words with multiple relationships demonstrate that our proposed method significantly enhances micro-F1 scores compared to state-of-the-art baselines.
Source: Applied Sciences
DOI: 10.3390/app142310960
Authors: Rongen Yan, Dongmei Li, Yan Wu, Depeng Dang, Ye Tao, Shaofei Wang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Awards: G3127987424, G8200413736
OpenAlex ID: W4404721814