Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025)

Abstract: Extracting entities and relations from scientific text is challenging due to long sentences with densely packed entities. Pipeline approaches address this by first extracting entities and then predicting relations between all possible entity pairs. Since the relation extraction phase operates over this exhaustive set, the inclusion of candidate pairs that may be semantically related but lack syntactic proximity introduces precision errors, ultimately reducing Rel+ F1 metric. We propose a simple yet effective syntactic filtering method based on dependency parsing to prune unlikely entity pairs before relation prediction. By leveraging syntactic proximity in the dependency parse tree, our approach retains structurally plausible pairs and reduces false positives in downstream relation classification. Our method is grounded in consistent statistical patterns observed across all evaluated datasets, reinforcing its generalizability and effectiveness. We integrate this filtering step into architectures such as PL-Marker and HGERE, and evaluate its impact across multiple datasets. Our method improves Rel+ F1 scores significantly by an absolute increase of 3.5–10.3% on SciERC, SciER, and ACE05 datasets. These results highlight the importance of syntactic cues for accurate relation extraction in complex domains like scientific literature.

## introduction entity and relation extraction (ere) is a critical task in natural language processing (nlp), enabling the identification of named entities and their semantic relationships from unstructured text @xcite . this task is pivotal for constructing knowledge graphs, enhancing information retrieval systems, and powering question-answering appli-cations. however, ere remains a persistent challenge due to the inherent complexity of natural language, the interdependencies between entities and relations, and the domain-specific nuances of text, such as those found in scientific literature @xcite . traditional pipeline approaches, which sequentially perform named entity recognition (ner) followed by relation extraction (re), are particularly susceptible to error propagation: inaccuracies in ner can cascade into the re phase, degrading overall performance @xcite . these issues have been partially mitigated by recent stateof-the-art (sota) methods such as pl-marker @xcite and @xcite . such models generate a large set of potential entities, many of which are unlikely to be related, but are nevertheless considered in the relation extraction phase to maintain completeness. this exhaustive candidate set, however, often leads to lower precision due to the inclusion of numerous irrelevant entity pairs. in this work, we propose a novel enhancement to pipeline-based entity and relation extraction (ere) systems that addresses the challenge of relation prediction over a large set of potential entity pairs. extending the standard pipeline, we introduce a dependency parsing (dp) based refinement step @xcite between the ner and re stages. this step leverages the syntactic structure of the sentence to construct a dependency parse tree and filters out entity pairs that, despite potential semantic relatedness, exhibit long syntactic distances. statistical analysis across multiple datasets, including scierc, scier, and ace05, reveals that the shortest dependency path length between entities is significantly shorter for true relations compared to unrelated pairs, as detailed in section 4.6. by leveraging this insight, our approach filters out unrelated entity pairs, thereby reducing the error rate of relation classifiers in the re stage. our method is particularly effective on challenging datasets such as scierc @xcite , which contains domain-specific scientific text with complex entity-relation structures. on the scierc dataset, our approach yields a 10.3% absolute improvement in rel+ f1 score using the hgere as the base model. our approach easily integrates into existing pipeline systems, making it practical for real-world ere tasks. ## conclusion the integration of dependency parsing-based filtering into pipeline architectures marks a significant advancement in entity and relation extraction for scientific texts. by leveraging syntactic structures to prune irrelevant entity pairs, our approach enhances rel+ f1 scores of models like pl-marker and hgere. this method's simplicity, robustness, and domain-generalizability make it a versatile solution for complex ere tasks across diverse datasets. as scientific literature continues to grow in volume and complexity, our syntactic enhancement offers a scalable framework to improve knowledge extraction, fostering the development of more accurate knowledge graphs and advanced nlp applications.