SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

Chunrong Fang; Dong Sun; Jiawei He; Jiawei Liu; Mengyu Shi; Xikai Yang; Zhenyu Chen; Zhijie Wang

arxiv: 2605.23440 · v3 · pith:UH2L37TVnew · submitted 2026-05-22 · 💻 cs.CL · cs.AI

SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

Jiawei He , Mengyu Shi , Jiawei Liu , Dong Sun , Zhijie Wang , Chunrong Fang , Xikai Yang , Zhenyu Chen This is my paper

Pith reviewed 2026-06-30 16:23 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords data augmentationjoint entity and relation extractionsemantic consistencyentity semantic restructuringtopic filteringJEREcontext-aware encoding

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The pith

SSDAU augments training data for joint entity and relation extraction while preserving semantic structures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SSDAU as a data augmentation approach to improve generalization in joint entity and relation extraction, which suffers from weak performance due to limited or low-quality labeled data. Existing augmentation techniques often ignore text relevance and break semantic structures and dependencies. SSDAU instead segments text using entity labels, applies context-aware encoding to capture entity semantics, restructures those semantics to form new examples, fuses embeddings with similarity scores to handle close entities, and filters with a topic model to keep content consistent. The result is augmented data that supports stronger model performance across different datasets and extraction architectures.

Core claim

SSDAU preserves the semantic structure of text during augmentation by segmenting text based on entity labels and employing an encoder to capture semantic features of entities through context awareness. It then performs entity semantic restructuring to generate augmented data. To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores. To mitigate potential topic ambiguity and information loss, it applies the BERTTopic model to filter out irrelevant topics, ensuring topic consistency. This process produces augmented data that improves downstream JERE model results over prior augmentation strategies.

What carries the argument

SSDAU pipeline of entity-label text segmentation, context-aware semantic encoding, entity semantic restructuring, embedding-similarity fusion, and BERTTopic filtering to maintain semantic consistency.

If this is right

JERE models trained on SSDAU data achieve higher scores on standard metrics than models trained with other augmentation methods.
The augmented data maintains greater robustness when models encounter ambiguous inputs.
The method works across datasets that use different annotation schemes.
It delivers gains when applied to multiple representative JERE architectures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same segmentation and filtering steps could be adapted for data augmentation in related tasks such as named entity recognition or event extraction.
In low-resource settings the approach might reduce reliance on new manual annotations by producing reliable synthetic examples.
Testing the pipeline on non-English text would show whether the semantic preservation steps transfer across languages.

Load-bearing premise

Segmenting text based on entity labels, applying context-aware encoding and semantic restructuring, fusing embeddings, and filtering topics with BERTTopic will reliably preserve semantic structures and dependencies without introducing new ambiguities or topic drift.

What would settle it

Generate SSDAU-augmented training sets, train JERE models on them, and evaluate on test examples that contain controlled semantic ambiguity or topic shifts; if the models show no improvement or larger drops than baselines, the preservation mechanism does not hold.

Figures

Figures reproduced from arXiv: 2605.23440 by Chunrong Fang, Dong Sun, Jiawei He, Jiawei Liu, Mengyu Shi, Xikai Yang, Zhenyu Chen, Zhijie Wang.

**Figure 1.** Figure 1: Overview of SSDAU. The Data Discretization and Reconstruction component discretizes the text data S semantically using the Encoder and outputs text collections in the form of segmented sets. The Decoder then processes these segmented sets to facilitate the Structured Semantic Data Augmentation component, where the Input View is based on similarity matching, while the Output View focuses on augmenting the d… view at source ↗

**Figure 1.** Figure 1: Overview of SSDAU. Given labeled JERE sentences S, SSDAU first applies Data Discretization and Reconstruction to extract head, relation, and tail spans and group them under shared semantic constraints. It then performs Structured Semantic Data Augmentation by ranking candidate spans with hybrid similarity scores and generating new instances through structure-consistent replacement. Finally, Scoring-based C… view at source ↗

**Figure 2.** Figure 2: The structure of our feature-based encoder. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 2.** Figure 2: The structure of our feature-based encoder. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: The comparison between the number of triads included in SSDAU after augmentation and [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 3.** Figure 3: The comparison between the number of text [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

read the original 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. However, existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization. In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve the semantic structure of text during augmentation. SSDAU segments text based on entity labels and employs an encoder to capture semantic features of entities through context awareness. It then performs entity semantic restructuring to generate augmented data. To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores. To mitigate potential topic ambiguity and information loss, we apply the BERTTopic model to filter out irrelevant topics, ensuring topic consistency. We evaluate SSDAU on datasets with different annotation types and compare its performance on five representative JERE models against seven popular data augmentation baselines. Experiments demonstrate that SSDAU generates semantically consistent data with superior robustness against ambiguity (8.26% F1 decrease vs. 31.91% for baselines), significantly outperforming all existing methods across all metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SSDAU assembles entity segmentation, context encoding, restructuring, fused similarity, and BERTTopic filtering into a JERE augmentation pipeline and reports an 8.26% F1 drop under ambiguity versus 31.91% for baselines, but the abstract supplies no equations, fusion details, or ablations.

read the letter

SSDAU segments text by entity labels, runs a context-aware encoder, restructures entity semantics, fuses embeddings with similarity scores, and applies BERTTopic to keep topics consistent. The main result is that models trained on the resulting data lose only 8.26% F1 when facing ambiguity, against 31.91% for the seven baselines.

The combination is new in its exact stacking, especially the topic filter on top of the restructuring step. The evaluation runs the method through five JERE models on datasets with different annotation styles, which gives the comparison some breadth.

The work targets a real problem: most augmentation techniques break entity-relation dependencies. Keeping structure through segmentation and filtering is a reasonable response, and the robustness gap is the clearest empirical signal in the abstract.

The soft spots sit in the missing internals. No equations describe the restructuring operation or the fusion of contextual embeddings with similarity scores. BERTTopic scope and settings are unspecified, and the abstract mentions no ablations that would show which piece drives the gain. Dataset sizes, significance tests, and error bars are also absent, so the headline numbers are hard to weigh without the full experimental section.

The paper is for people who already work on data augmentation inside information extraction and want a concrete pipeline to try. A reader who needs a practical trick for low-resource JERE settings can extract value from the reported comparisons.

It deserves peer review. The task is standard, the method is implementable, and the claims are specific enough that referees can verify the experimental details and check whether the robustness advantage holds up under scrutiny.

Referee Report

3 major / 1 minor

Summary. The paper proposes Structured Semantic Data Augmentation (SSDAU) for Joint Entity and Relation Extraction (JERE). SSDAU segments text by entity labels, applies context-aware encoding, performs entity semantic restructuring to create augmented samples, fuses contextual embeddings with similarity scores, and uses BERTTopic to filter topic drift. Experiments across datasets with varying annotation schemes and five JERE models demonstrate that SSDAU outperforms seven existing augmentation baselines on all metrics while exhibiting greater robustness to ambiguity (F1 drop of 8.26% versus 31.91% for baselines).

Significance. If the reported robustness and outperformance hold after verification, the work would address a recognized weakness in JERE data augmentation—disruption of semantic dependencies—by introducing an explicit preservation pipeline. The ambiguity-robustness metric is a practically useful addition to standard F1 evaluation. Credit is due for evaluating on multiple models and annotation types rather than a single setting.

major comments (3)

[Abstract] Abstract: the headline robustness result (8.26% F1 decrease vs. 31.91%) is load-bearing for the central claim yet rests on the unverified assumption that the four-step pipeline (entity-label segmentation, context-aware encoding, semantic restructuring, BERTTopic filtering) preserves entity-relation dependencies without introducing new ambiguities; no equations for the restructuring operation or the fusion of contextualized embeddings with similarity scores are supplied.
[Abstract] Abstract: no ablation results are described that isolate the contribution of each pipeline component (e.g., BERTTopic filtering versus embedding fusion), which is required to attribute gains to semantic preservation rather than dataset-specific artifacts.
[Abstract] Abstract: the claim of significant outperformance across all metrics and models supplies no dataset sizes, number of runs, statistical significance tests, error bars, or baseline implementation details, rendering the empirical evidence impossible to assess for soundness.

minor comments (1)

The abstract would benefit from naming the specific datasets and JERE models used, even at high level, to clarify the evaluation scope.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential value of the robustness metric and multi-model evaluation. We address each major comment below and will make targeted revisions to the abstract and manuscript to improve clarity and verifiability.

read point-by-point responses

Referee: [Abstract] Abstract: the headline robustness result (8.26% F1 decrease vs. 31.91%) is load-bearing for the central claim yet rests on the unverified assumption that the four-step pipeline (entity-label segmentation, context-aware encoding, semantic restructuring, BERTTopic filtering) preserves entity-relation dependencies without introducing new ambiguities; no equations for the restructuring operation or the fusion of contextualized embeddings with similarity scores are supplied.

Authors: The experimental results across five JERE models and multiple datasets with differing annotation schemes provide empirical support that the pipeline preserves dependencies, as evidenced by the substantially smaller F1 degradation under ambiguity. The full manuscript (Section 3) describes the restructuring and fusion steps in detail. To address the absence of equations in the abstract, we will add concise mathematical formulations for the entity semantic restructuring operation and the embedding-similarity fusion in the revised abstract. revision: partial
Referee: [Abstract] Abstract: no ablation results are described that isolate the contribution of each pipeline component (e.g., BERTTopic filtering versus embedding fusion), which is required to attribute gains to semantic preservation rather than dataset-specific artifacts.

Authors: The full manuscript contains ablation studies (Section 4.3) that isolate each component's contribution. The abstract currently omits any reference to these results. In revision we will add a brief statement summarizing the ablation outcomes to better attribute performance gains to the semantic-preservation mechanisms. revision: yes
Referee: [Abstract] Abstract: the claim of significant outperformance across all metrics and models supplies no dataset sizes, number of runs, statistical significance tests, error bars, or baseline implementation details, rendering the empirical evidence impossible to assess for soundness.

Authors: We agree that the abstract is too terse on experimental protocol. The full paper reports dataset statistics (Section 4.1), results from five independent runs with error bars and significance testing, and baseline re-implementation details (Appendix). We will incorporate the key figures (dataset sizes, run count, and significance notation) into the revised abstract. revision: yes

Circularity Check

0 steps flagged

Empirical augmentation pipeline with no derivation chain

full rationale

The paper presents SSDAU as a four-step empirical pipeline (entity-label segmentation, context-aware encoding, semantic restructuring, BERTTopic filtering) whose value is established solely by direct experimental comparison against baselines on standard JERE datasets. No equations, fitted parameters, or predictions are defined; the reported robustness metric (8.26 % vs. 31.91 % F1 drop) is an observed outcome, not a quantity derived from the method itself. No self-citations, uniqueness theorems, or ansatzes are invoked to justify core claims. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method description implies standard use of BERTTopic and encoders but does not introduce new fitted constants or unproven assumptions beyond the general claim that the pipeline preserves semantics.

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Works this paper leans on

2 extracted references · 2 canonical work pages

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