Structured Tender Entities Extraction from Complex Tables with Few-short Learning.
| Authors | Asim Abbas 0003 et al. |
| Year | 2025 |
| Venue | COLING 2025 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
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Core Technical Contribution
Unable to determine. The stub does not include the abstract or paper content needed to identify the specific technical novelty, algorithmic innovations, or how this work differs from prior approaches. The authors and publication venue (appears to be RegNLP workshop at ACL 2025) are noted, but the actual contribution cannot be extracted from a missing abstract.
How It Works
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Production Impact
Cannot be assessed without knowing what problem the paper solves or what approach it proposes. Production impact depends critically on understanding the concrete problem, the proposed solution, computational requirements, data dependencies, latency characteristics, and integration complexity—none of which can be extracted from a missing abstract.
Limitations and When Not to Use This
Unknown. Without access to the paper content, I cannot identify what the authors acknowledge as unsolved problems, what assumptions underpin their approach, potential failure modes in production, or what follow-up research directions they identify as future work.
Research Context
The paper appears to be published at ACL 2025 in a workshop focused on resources and evaluation in NLP (RegNLP), suggesting it likely contributes to the NLP evaluation, benchmarking, or resource-building literature. However, the specific prior work it builds on, benchmarks it improves, and research directions it opens cannot be determined without the full abstract or introduction.
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