Abstract

It is well known today that pages on the Web contain a large number of content-rich relational tables. Such tables have been systematically extracted in a number of efforts to empower important applications such as table search and schema discovery. However, a significant fraction of rela-tional tables are not embedded in the standard HTML table tags, and are thus difficult to extract. In particular, a large number of relational tables are known to be in a “list ” form, which contains a list of clearly separated rows that are not separated into columns. In this work, we address the important problem of au-tomatically extracting multi-column relational tables from such lists. Our key intuition lies in the simple observation that in correctly-extracted tables, values in the same column are coherent, both at a syntactic and at a semantic level. Us-ing a background corpus of over 100 million tables crawled from the Web, we quantify semantic coherence based on a statistical measure of value co-occurrence in the same col-umn from the corpus. We then model table extraction as a principled optimization problem – we allocate tokens in each row sequentially to a fixed number of columns, such that the sum of coherence across all pairs of values in the same column is maximized. Borrowing ideas from A? search and metric distance, we develop an efficient 2-approximation algorithm. We conduct large-scale table extraction experi-ments using both real Web data and proprietary enterprise spreadsheet data. Our approach considerably outperforms the state-of-the-art approaches in terms of quality, achieving over 90 % F-measure across many cases. 1.