Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types. Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge discovery. However, they are challenged by the diverse table structures and the spatial-correlated semantics on cell grids. To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction. First, we propose a multi-task framework that learns table region, structural components and cell types jointly; second, we leverage the advances of the recent language model to capture semantics in each cell value; third, we build a large human-labeled dataset with broad coverage of table structures. Our evaluation shows that our proposed multi-task framework is highly effective that outperforms the results of training each task separately. Spreadsheets are the most popular end-user development tool for data management and analysis. Unlike programming languages or databases, no syntax, data models or even vague standards are enforced for spreadsheets. Figure1(a) shows a real-world spreadsheet. To enable intelligent data analysis and knowledge discovery for the data in range B4:H24, one needs to manually transform the data to a standard form as shown in Figure1(e). It would be highly desirable to develop techniques to extract the semantic structure information for automated spreadsheet data transformation. Semantic structure extraction entails three chained tasks to: (1) We also show the transformed data in Figure1(e ), where different cell types are highlighted using the same coloring scheme as in Figure1(d ). Learning the semantic structure for spreadsheets is challenging. While table detection is confounded  by the diverse multi-table layouts, component recognition is confounded by the various structures of  table components , and cell type classification requires semantic-level understanding of cell values. Moreover, the tasks are chained in the sense that latter tasks need to leverage the outcomes of prior tasks. This poses challenges on preventing error propagation, but also provides opportunities for utilizing additional cues from other tasks to improve the current task. For example, header extraction may help table detection since headers need to be inside the table region and vice versa. In this paper, we present a multi-task learning framework to solve spreadsheet table detection, component recognition, and cell type classification jointly. Our contributions are as follows: 1. We formulate spreadsheet table structure extraction as a coarse-to-fine process including table detection, component recognition, and cell type classification. We also build a large labeled dataset. 2. To capture the rich information in spreadsheet cells for model training, we devise a featurization scheme containing both hand-crafted features and model-based semantic representations. 3. We propose a multi-task framework that can be trained to simultaneously locate table ranges, recognize table components and extract cell types. Our evaluation shows that the proposed multi-task framework is highly effective that outperforms the results of training each task separately. Cell type classification is the task of classifying each cell into a certain type such as value, value name, index, and index name. A value is a basic unit in the value region. A value name is a summary term that describes values. As shown in Figure1(a), "Cost" at E6 is a value name to describe the values in E8:H24. After the data extraction, as shown in Figure1(e), "Cost" at D1 is the label of Column D. An index refers to individual values that can be used for indexing data records. In Figure1(a), "January" -"October" at E5:H5 are indexes of columns E -H respectively. A group of indexes is used to breakdown the dataset into subsets. After data transformation, it will form a single data field as Column C shows in Figure1(e). An index name is a summary term that describes the indexes. In the previous example, " Month" is the index name of indexes "January" -"October". After data transformation, the " Month" in Figure1(a) corresponds to the column label at C1 in Figure1(e).
We propose a novel multi-task framework that learns table detection, semantic component recognition and cell type classification for spreadsheet tables with promising results.
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