<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benchmarks | Language Technologies Lab</title><link>http://nlp.unibo.it/tag/benchmarks/</link><atom:link href="http://nlp.unibo.it/tag/benchmarks/index.xml" rel="self" type="application/rss+xml"/><description>Benchmarks</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 02 Mar 2026 00:00:00 +0000</lastBuildDate><image><url>http://nlp.unibo.it/media/icon_hu_7613a4a452ac7087.png</url><title>Benchmarks</title><link>http://nlp.unibo.it/tag/benchmarks/</link></image><item><title>Argument Mining on Legal Datasets</title><link>http://nlp.unibo.it/proposals_legal/am/</link><pubDate>Mon, 02 Mar 2026 00:00:00 +0000</pubDate><guid>http://nlp.unibo.it/proposals_legal/am/</guid><description>&lt;p>&lt;strong>Description:&lt;/strong>&lt;br>
Argumentation in legal documents is typically well-structured and follows specific domain rules.
We are interested in applying both the most recent NLP techniques and also hybrid techniques that can leverage the domain knowledge.&lt;/p>
&lt;p>&lt;strong>Contact:&lt;/strong> &lt;a href="mailto:a.galassi@unibo.it">Andrea Galassi&lt;/a>, &lt;a href="mailto:marco.lippi@unifi.it">Marco Lippi&lt;/a>&lt;/p></description></item></channel></rss>