For tabular data, I recommend using Duckling [1] which is a regex-based approach to data classification and it supports multiple languages. If you only need Eng

For tabular data, I recommend using Duckling [1] which is a regex-based approach to data classification and it supports multiple languages. If you only need English, then Stanford CoreNLP's SUTime [2] will also work. You can extend either tool with your own regexes. For documents, I recommend using Apache Tika which will do a decent job out of the box to extract the raw text. The raw text can then by indexed in ES. In my experience, it is the best tool for detecting a document's type. It is not necessarily the best tool for extracting text depending on the document type. For example, it is better to use a paid solution for OCR PDFs if you can afford it. However it is often good enough and very easy to use. If you need to extract tabular data from documents, then that is a much harder problem and you are on the cutting-edge of NLP/ML research. One tool available in Stanford CoreNLP that may help is SPIED [3]. If the tool isn't exactly what you need, then I would recommend using the paper as a starting point for literature review. 1. https://github.com/facebookincubator/duckling https://github.com/facebookincubator/duckling 2. https://nlp.stanford.edu/software/sutime.html https://nlp.stanford.edu/software/sutime.html 3. https://nlp.stanford.edu/software/patternslearning.html https://nlp.stanford.edu/software/patternslearning.html