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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transfer Learning for Named-Entity Recognition with Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1705.06273/</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/1705.06273/feed.xml" rel="self" type="application/rss+xml" /><item><title>Transfer Learning for Named-Entity Recognition with Neural Networks — mentioned by Eridrus</title><link>https://news.ycombinator.com/item?id=14367505</link><guid isPermaLink="false">urn:hackerpapers:mention:1705.06273:14367505</guid><pubDate>Thu, 18 May 2017 14:31:38 GMT</pubDate><description>&lt;p&gt;Transfer learning is great, but is still pretty limited; the main success stories are retraining CNNs from ImageNet to other datasets and word2vec/GloVE. In the space of all the things you would want to do with ML, this is pretty limited. [EDIT]: I stumbled upon a transfer learning for NER paper a second ago: https://arxiv.org/abs/1705.06273v1 And the results are not conclusive. You can can improve your results in truly data impoverished regimes, but you get diminishing returns as you add more data, so the only way to catch up is to get more data. So, you may be able to get reasonable…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1705.06273/"&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>
