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In this post I want to go a little deeper into Ludwig Wittgenstein's argument of "meaning is use" (Philosophical Investigations, 1953), and how it can be seen as a philosophical justification for statistical NLP, including machine learning.

What makes a good search engine? You type a sequence of letters and the document is searched for all occurrences of this combination. This way you can quickly and easily find certain text passages within a document. For a user who wants to find out more about a particular topic or the use of a particular word, such a basic search functionality would certainly not be enough.

IntraFind has long been known for high quality information retrieval. For our new product generation iFinder5 elastic we completely overhauled our core search technology consisting of our Lucene / Elasticsearch Analyzers and our Query Parser. In part 1 of this blog article I talk about advantages for the standard user and how we are able to reduce configuration efforts.

Identifying the language of a given text is a crucial preprocessing step for almost all text analysis methods. It is considered as a solved problem since more than 20 years. Available solutions build on the simple observation that for all languages typical letter sequences (letter n-grams) exist, that occur significantly more frequent in this language than in other languages.