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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Text analysis and deep learning: A network approach · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2110.04151/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2110.04151/feed.xml" rel="self" type="application/rss+xml" /><item><title>Text analysis and deep learning: A network approach — mentioned by zwaps</title><link>https://news.ycombinator.com/item?id=35510386</link><guid isPermaLink="false">urn:hackerpapers:mention:2110.04151:35510386</guid><pubDate>Mon, 10 Apr 2023 08:49:35 GMT</pubDate><description>&lt;p&gt;Embeddings can go beyond a contextual definition of meaning. You can use (transformer) LLMs to get a &amp;quot;word sense&amp;quot; aka its predicted probability distribution, which also induces a network of associations (not via context, but via whatever the LLM has learned as fitting words). This gives a structural definition of meaning the ye olde sense (v. Humboldt and so forth). This of course starts with the assumption that LLMs can encode meaning, albeit not in a simplistic linear fashion as Word2Vec or GloVe did https://arxiv.org/abs/2110.04151&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2110.04151/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
