TabEmb: Joint Semantic-Structure Embedding for Table Annotation

• URL: http://arxiv.org/abs/2604.18939v1
• Date: Tue, 21 Apr 2026 00:25:49 GMT
• Title: TabEmb: Joint Semantic-Structure Embedding for Table Annotation
• Abstract summary: Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications.<n>Existing models learn by linearizing the 2D table into a 1D token sequence and encoding it with pretrained language models.<n>We propose TabEmb, which directly targets these limitations by decoupling semantic encoding from structural modeling.
• Score: 3.03279900330493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically rich token or sentence embeddings often suffice, tables are structured combinations of columns wherein useful representations must jointly capture column's semantics and the inter-column relationships. Existing models learn by linearizing the 2D table into a 1D token sequence and encoding it with pretrained language models (PLMs) such as BERT. However, this leads to limited semantic quality and weaker generalization to unseen or rare values compared to modern LLMs, and degraded structural modeling due to 2D-to-1D flattening and context-length constraints. We propose TabEmb, which directly targets these limitations by decoupling semantic encoding from structural modeling. An LLM first produces semantically rich embeddings for each column, and a graph-based module over columns then injects relationships into the embeddings, yielding joint semantic-tructural representations for table annotation. Experiments show that TabEmb consistently outperforms strong baselines on different table annotation tasks. Source code and datasets are available at https://github.com/hoseinzadeehsan/TabEmb

Related papers

• SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching [20.959946127503354]
  We propose SemStruct, a framework that joins the semantic power of frozen PLMs with the structural inductive bias of Graph Neural Networks (GNNs)<n>We model the table as a heterogeneous graph where columns and values are nodes connected by rows, allowing the GNN to propagate disambiguating context across the structure.
  arXiv  Detail & Related papers  (2026-05-29T01:45:45Z)
• ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering [55.55968342644846]
  Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering.<n>Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility.<n>We propose ASTRA (Adaptive Semantic Tree Reasoning Architecture) including two main modules, AdaSTR and DuTR.<n> Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2026-04-10T06:09:41Z)
• TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment [70.83664203825235]
  TDATR (Table Detail-Aware Table Recognition) improves end-to-end TR through table detail-aware learning and cell-level visual alignment.<n>We achieve state-of-the-art or highly competitive performance on seven benchmarks without dataset-specific fine-tuning.
  arXiv  Detail & Related papers  (2026-03-24T05:45:02Z)
• Language Model Representations for Efficient Few-Shot Tabular Classification [17.63549220100997]
  Large language models (LLMs) are becoming an increasingly integral component of web infrastructure for tasks like semantic search.<n>This work investigates a lightweight paradigm, $textbfTa$ble $textbfR$epresentation with $textbfL$anguage Model.<n>We show that our approach achieves performance comparable to state-of-the-art models in low-data regimes.
  arXiv  Detail & Related papers  (2026-01-21T23:28:51Z)
• TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding [52.59372043981724]
  TableDART is a training-efficient framework that integrates multimodal views by reusing pretrained single-modality models.<n>In addition, we propose a novel agent to cross-modal knowledge integration by analyzing outputs from text- and image-based models.
  arXiv  Detail & Related papers  (2025-09-18T07:00:13Z)
• Taxonomy Inference for Tabular Data Using Large Language Models [31.121233193993906]
  This paper presents two methods for taxonomy inference for tables: (i) EmTT which embeds columns by fine-tuning with contrastive learning encoder-alone LLMs like BERT, and (ii) GeTT which generates table entity types and their hierarchy by iterative prompting using a decoder-alone LLM like GPT-4.
  arXiv  Detail & Related papers  (2025-03-25T16:26:05Z)
• CARTE: Pretraining and Transfer for Tabular Learning [10.155109224816334]
  We propose a neural architecture that does not need such correspondences. As a result, we can pretrain it on background data that has not been matched. A benchmark shows that CARTE facilitates learning, outperforming a solid set of baselines.
  arXiv  Detail & Related papers  (2024-02-26T18:00:29Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• SEMv2: Table Separation Line Detection Based on Instance Segmentation [96.36188168694781]
  We propose an accurate table structure recognizer, termed SEMv2 (SEM: Split, Embed and Merge) We address the table separation line instance-level discrimination problem and introduce a table separation line detection strategy based on conditional convolution. To comprehensively evaluate the SEMv2, we also present a more challenging dataset for table structure recognition, dubbed iFLYTAB.
  arXiv  Detail & Related papers  (2023-03-08T05:15:01Z)
• Retrieving Complex Tables with Multi-Granular Graph Representation Learning [20.72341939868327]
  The task of natural language table retrieval seeks to retrieve semantically relevant tables based on natural language queries. Existing learning systems treat tables as plain text based on the assumption that tables are structured as dataframes. We propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with multi-granular graph representation learning.
  arXiv  Detail & Related papers  (2021-05-04T20:19:03Z)
• A Graph Representation of Semi-structured Data for Web Question Answering [96.46484690047491]
  We propose a novel graph representation of Web tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. Our method improves F1 score by 3.90 points over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-10-14T04:01:54Z)
• TabEAno: Table to Knowledge Graph Entity Annotation [7.451544182579802]
  We propose a novel approach, namely TabEAno, to semantically annotate table rows toward knowledge graph entities. We introduce a "two-cells" lookup strategy bases on the assumption that there is an existing logical relation occurring in the knowledge graph between the two closed cells in the same row of the table. Despite the simplicity of the approach, TabEAno outperforms the state of the art approaches in the two standard datasets.
  arXiv  Detail & Related papers  (2020-10-05T07:39:02Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.