让表格同时懂语义和结构，提升标注精度
TabEmb: Joint Semantic-Structure Embedding for Table Annotation
- 分步处理：先用大模型学列语义，再用图模型建列间关系
- 在多个标注任务上超越现有方法，尤其擅长罕见值识别
- 适合需要理解复杂表格的NLP应用，如数据挖掘与问答
表格标注对网页和企业级表格在下游自然语言处理中的应用至关重要。与仅需语义丰富词或句嵌入的文本不同，表格是列的结构化组合，其有效表示需同时捕捉列语义与列间关系。现有模型将二维表格线性化为一维标记序列，并用预训练语言模型（如BERT）编码，但存在语义质量有限、对未见或稀有值泛化能力弱，以及因2D转1D导致结构建模下降的问题。我们提出TabEmb，通过解耦语义编码与结构建模来解决这些局限。首先用大语言模型为每列生成语义丰富的嵌入，再通过基于图的模块在列之间注入关系信息，从而得到联合的语义-结构表示，用于表格标注。实验表明，TabEmb在多种表格标注任务中持续优于强基线模型。代码与数据集已公开于https://github.com/hoseinzadeehsan/TabEmb。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。