<story><title>Wittgenstein’s theories are the basis of all modern NLP</title><url>https://towardsdatascience.com/neural-networks-and-philosophy-of-language-31c34c0796da</url></story><parent_chain></parent_chain><comment><author>idoubtit</author><text>In what sense are these theories a &amp;quot;basis&amp;quot; to NLP? Did they have any influence? Do they bring any practical contributions? I suspect a slight similarity between popular domains (Wittgenstein and NLP) was contrived into an article that seems very light on the W part.&lt;p&gt;The &amp;quot;Wittgenstein’s theories&amp;quot; that appear here is just that &amp;quot;the meaning of a word is its use in the language&amp;quot;. If such a plain concept was all of Wittgenstein’s theories, he would be long forgotten.&lt;p&gt;For centuries, dictionaries have presented words through one or several explanations as well as quotes and examples. 150 years ago, Émile Littré wrote a wonderful French dictionary that contains 80,000 words and about 300,000 literary quotes. He knew no word has a simple and permanent meaning, and that one needs to know many real world contexts to get a fine view on a word.</text></comment>
<story><title>Wittgenstein’s theories are the basis of all modern NLP</title><url>https://towardsdatascience.com/neural-networks-and-philosophy-of-language-31c34c0796da</url></story><parent_chain></parent_chain><comment><author>akozak</author><text>Figuring out how to process context is important for NLP, no question.&lt;p&gt;But I think this is probably wrong on Wittgenstein. I&amp;#x27;m pretty sure his entire point in the Philosophical Investigations was that &amp;quot;meaning&amp;quot; is exactly NOT probabilities of symbol co-occurrence, or just names of objects in the world. Symbols acquire meanings from their use by humans. Accounting for context in NLP via probabilities of occurrence might be useful in better reproducing language, but we should be careful not to say that this is the essence of meaning and language.</text></comment>