Tabular Cell Classification Using Pre-Trained Cell Embeddings

There is a large amount of data on the web in tabular form, such as excel sheets, CSVs, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (e.g. font size, border type, background color) to classify tabular cells by their role in the data layout of the document (top attribute, data, metadata, etc.). In this paper, we propose a method to embed the semantic and contextual information about tabular cells in a low dimension cell embedding space. We then propose an RNN-based classification technique to use these cell vector representations, combining them with stylistic features introduced in previous work, in order to improve the performance of cell type classification in complex documents. We evaluate the performance of our system on three datasets containing documents with various data layouts, in two settings, in-domain, and cross-domain training. Our evaluation result shows that our proposed cell vector representations in combination with our RNN-based classification technique significantly improves cell type classification performance.