To us humans, language is a very natural way of interacting with the world. That is why attempts at formalizing natural language in a manner similar to SQL have been largely replaced by Transformer-based language models. A sentence can have any length, and its words draw from an infinite vocabulary. Unstructured data, on the other hand, does not follow a standardized format and does not have any predefined input categories. Consider this table of 100-meter dash world record times, ordered by gender and geographical region (from Wikipedia): The columns normally represent features, while the records stand for individual data points. For instance, a two-dimensional table follows the format of columns on the x-axis, and rows, or records, on the y-axis. This makes structured data readily processable by computers. Structured data is presented in a standardized format. Learn how to use the Haystack NLP framework to extract tables from documents, retrieve best results from a table corpus, and do question answering on tabular data. We’ll be covering some concepts and terminology, before moving on to practical code examples of the tools in action. In this article, we introduce Haystack’s newest tools for question answering on tables. Tabular question answering is a powerful tool for extracting and aggregating information from tables and mixed table-text formats, as you would find in financial statements or technical documentation. You can now do semantic question answering not only on unstructured text, but also on structured tabular data. Slowly but surely, natural language is becoming the primary interface to data.
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