Fine-tuned models under 1B parameters reach micro-F1 of 0.83 on general-domain RE versus 0.69 for GPT-5.4 zero-shot, with similar gains on literary benchmarks.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
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abstract
Relation extraction is the problem of classifying the relationship between two entities in a given sentence. Distant Supervision (DS) is a popular technique for developing relation extractors starting with limited supervision. We note that most of the sentences in the distant supervision relation extraction setting are very long and may benefit from word attention for better sentence representation. Our contributions in this paper are threefold. Firstly, we propose two novel word attention models for distantly- supervised relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based word attention model (BGWA), (2) an entity-centric attention model (EA), and (3) a combination model which combines multiple complementary models using weighted voting method for improved relation extraction. Secondly, we introduce GDS, a new distant supervision dataset for relation extraction. GDS removes test data noise present in all previous distant- supervision benchmark datasets, making credible automatic evaluation possible. Thirdly, through extensive experiments on multiple real-world datasets, we demonstrate the effectiveness of the proposed methods.
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cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction
Fine-tuned models under 1B parameters reach micro-F1 of 0.83 on general-domain RE versus 0.69 for GPT-5.4 zero-shot, with similar gains on literary benchmarks.