SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
The paper presents a novel method called Structured Semantic Data Augmentation (SSDAU) aimed at improving Joint Entity and Relation Extraction (JERE). SSDAU focuses on preserving the semantic structure of text during data augmentation, addressing issues with existing methods that disrupt text relevance. Experimental results show that SSDAU significantly outperforms traditional data augmentation techniques in generating semantically consistent data.
- ▪SSDAU is designed to enhance model generalization for Joint Entity and Relation Extraction.
- ▪The method preserves semantic structures during text augmentation by segmenting based on entity labels.
- ▪Experiments indicate that SSDAU achieves superior robustness against ambiguity compared to existing methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23440 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Q3m7QeKXQMlH |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2605.23440 (cs) [Submitted on 22 May 2026] Title:SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Authors:Jiawei He, Mengyu Shi, Chunrong Fang View a PDF of the paper titled SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction, by Jiawei He and 2 other authors View PDF HTML (experimental) Abstract:Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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