Are all the tables in the dataset from the same website?
No, they come from the top ranked web pages relevant to a query and from Wikipedia 
Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress towards improving this area requires powerful models of semantic matching and richer training and evaluation resources. To remedy this, we present a ranking based approach, and implement both carefully designed features and neural network architectures to measure the relevance between a query and the content of a table. Furthermore, we release an open-domain dataset that includes 21,113 web queries for 273,816 tables. We conduct comprehensive experiments on both real world and synthetic datasets. Results verify the effectiveness of our approach and present the challenges for this task.
Table is a special and valuable information that could be found almost everywhere from the Internet. We target at the task of content-based table retrieval in this work. Given a query, the task is to find the most relevant table from a collection of tables. Table retrieval is of great importance for both natural language processing and information retrieval. On one hand, it could improve existing information retrieval systems. The well-organized information from table, such as product comparison from different aspects and flights between two specific cities, could be used to directly respond to web queries. On the other hand, the retrieved table could be used as the input for question answering BIBREF0 . Unlike existing studies in database community BIBREF1 , BIBREF2 that utilize surrounding text of a table or pagerank score of a web page, we focus on making a thorough exploration of table content in this work. We believe that content-based table retrieval has the following challenges. The first challenge is how to effectively represent a table, which is semi-structured and includes many aspects such as headers, cells and caption. The second challenge is how to build a robust model that measures the relevance between an unstructured natural language query and a semi-structured table. Table retrieval could be viewed as a multi-modal task because the query and the table are of different forms. Moreover, to the best of our knowledge, there is no publicly available dataset for table retrieval. Further progress towards improving this area requires richer training and evaluation resources. To address the aforementioned challenges, we develop a ranking based approach. We separate the approach into two cascaded steps to trade-off between accuracy and efficiency. In the first step, it finds a small set (e.g. 50 or 100) of candidate tables using a basic similarity measurement. In the second step, more sophisticated features are used to measure the relevance between the query and each candidate table. We implement two types of features, including manually designed features inspired by expert knowledge and neural network models jointly learned from data. Both strategies take into account the relevance between query and table at different levels of granularity. We also introduce a new dataset WebQueryTable for table retrieval. It includes 21,113 web queries from search log, and 273,816 web tables from Wikipedia. We conduct comprehensive experiments on two datasets, a real world dataset introduced by us, and a synthetic dataset WikiTableQuestions BIBREF0 which has been widely used for table-based question answering. Results in various conditions show that neural network models perform comparably with carefully designed features, and combining them both could obtain further improvement. We study the influence of each aspect of table for table retrieval, and show what depth of table understanding is required to do well on this task. Results show the difference between question and web query, and present future challenges for this task. This paper has the following contributions. We develop both feature-based and neural network based approaches, and conduct thorough experiments on real world and synthetic datasets. We release an open-domain dataset for table retrieval.