Pith. sign in

REVIEW 2 cited by

Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1804.06987 v1 pith:YNJFESDO submitted 2018-04-19 cs.CL

Sharmistha Jat , Siddhesh Khandelwal , Partha Talukdar This is my paper · ORCID
classification cs.CL
keywords relationextractionattentionsupervisionworddistantmodeldatasets
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A large synthetic instruction corpus with guidelines, preference rules, and format variants improves LLM performance on five NLU benchmarks by an average of 3.1%.

  2. Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction

    cs.CL 2026-06 unverdicted novelty 4.0 of 10

    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.

Pith tools