Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Syntactic Dependency-Aware Neural Networks for Enhanced Entity Relation Analysis

Version 1 : Received: 26 November 2023 / Approved: 27 November 2023 / Online: 27 November 2023 (07:55:12 CET)

How to cite: Nazrul, M.; Stellato, C.; Iqbal, S.; Lee, F.; Ali, W. Syntactic Dependency-Aware Neural Networks for Enhanced Entity Relation Analysis. Preprints 2023, 2023111681. https://doi.org/10.20944/preprints202311.1681.v1 Nazrul, M.; Stellato, C.; Iqbal, S.; Lee, F.; Ali, W. Syntactic Dependency-Aware Neural Networks for Enhanced Entity Relation Analysis. Preprints 2023, 2023111681. https://doi.org/10.20944/preprints202311.1681.v1

Abstract

In the intricate domain of text analysis, syntactic dependency trees play a crucial role in unraveling the web of relations among entities embedded in the text. These trees provide a structural roadmap, guiding us through the complex syntax to pinpoint the interactions and connections between different entities. However, the challenge lies in sifting through this intricate structure to extract relevant information, a task that requires precision and discernment. Traditional approaches often rely on rule-based pruning methods to simplify these dependency structures, focusing on certain parts while discarding others. Yet, this approach has its pitfalls, as it can overlook critical nuances and connections that are vital for a comprehensive understanding of the text. Addressing this gap, our research introduces the Syntactic Dependency-Aware Neural Networks (SDANNs), a groundbreaking model designed to harness the full power of the entire dependency tree. This approach marks a significant departure from the conventional methods. Instead of the rigid rule-based pruning, SDANNs implement a more flexible and dynamic 'soft-pruning' technique. This method allows the model to adaptively focus on the sub-structures within the dependency tree that are most relevant for understanding the relationships between entities. By doing so, it ensures that no vital information is overlooked, and all potential connections are considered. The efficacy of SDANNs is not just theoretical but has been empirically validated through extensive testing and evaluations across a wide range of tasks. These tasks include the extraction of complex relations spanning multiple sentences, as well as detailed analyses at the sentence level. In each of these scenarios, SDANNs have demonstrated a remarkable ability to leverage the full structural complexity of dependency trees. This capability sets them apart from existing models, enabling a more nuanced and comprehensive analysis of textual relations. The results of these evaluations consistently show that SDANNs not only meet but significantly exceed the performance of prior models. This superiority is evident in the way SDANNs handle the multifaceted and often subtle interactions within the text, offering insights that were previously inaccessible with conventional methods. In summary, the Syntactic Dependency-Aware Neural Networks represent a significant advancement in the field of text analysis. By fully embracing the complexity of syntactic dependency trees and employing a sophisticated 'soft-pruning' approach, SDANNs open new avenues for exploring and understanding the intricate relationships that exist within written language. This model stands as a testament to the potential of combining advanced neural network architectures with a deep understanding of linguistic structures, paving the way for more accurate, nuanced, and comprehensive analyses of text.

Keywords

information extraction; entity relation extraction; graph neural networks; dependency feature

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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