Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
This work addresses relation extraction for natural language processing, offering a novel method that combines syntactic indicators with sentential contexts, though it is incremental as it builds on existing approaches.
The paper tackled relation extraction by proposing an indicator-aware approach that leverages syntactic indicators and sentential contexts to improve relation representations, resulting in significant outperformance over state-of-the-art methods on the SemEval-2010 Task 8 benchmark dataset.
State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than other words and may be beneficial for identifying semantic relations. Other approaches using fixed text triggers capture such information but ignore the lexical diversity. To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. Firstly, we extract syntactic indicators under the guidance of syntactic knowledge. Then we construct a neural network to incorporate both syntactic indicators and the entire sentences into better relation representations. By this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms the state-of-the-art methods.