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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1412.5335/</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/1412.5335/feed.xml" rel="self" type="application/rss+xml" /><item><title>Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews — mentioned by pigscantfly</title><link>https://news.ycombinator.com/item?id=13172841</link><guid isPermaLink="false">urn:hackerpapers:mention:1412.5335:13172841</guid><pubDate>Wed, 14 Dec 2016 00:50:00 GMT</pubDate><description>&lt;p&gt;No, I think errata would be more appropriate; the gist of my comment was in support of the idea behind the paper, but it&amp;#x27;s disingenuous to pretend that the model works as well as described on that task. It&amp;#x27;s also disingenuous to trivialize the difference as 1-3% - that difference is more than a third of the actual error rate (7.42% vs. 11.27%) assuming you&amp;#x27;re referring to [1]. True, the IMDB dataset isn&amp;#x27;t a very important one, but it&amp;#x27;s important to clarify when you&amp;#x27;ve made a mistake, especially if your paper has been cited hundreds of times. [1] https://arxiv.org/pdf/1412.5335v7.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1412.5335/"&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>
