<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>NLP on Prahyat Puangjaktha | Hub</title><link>https://prayat-pu.github.io/py-website/tags/nlp/</link><description>Recent content in NLP on Prahyat Puangjaktha | Hub</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 11:00:00 +0000</lastBuildDate><atom:link href="https://prayat-pu.github.io/py-website/tags/nlp/index.xml" rel="self" type="application/rss+xml"/><item><title>Paper, Meet Code: Linking Research to Repositories</title><link>https://prayat-pu.github.io/py-website/publications/paper-meet-code/</link><pubDate>Fri, 01 May 2026 11:00:00 +0000</pubDate><guid>https://prayat-pu.github.io/py-website/publications/paper-meet-code/</guid><description>&lt;p&gt;This research project focuses on a deep learning approach to automatically link scholarly articles with their official software repositories on GitHub.&lt;/p&gt;
&lt;h3 id="key-contributions"&gt;Key Contributions&lt;/h3&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;SPECTER Model:&lt;/strong&gt; Utilized for extracting information from both academic articles and software repositories.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Classification:&lt;/strong&gt; Applied machine learning models, with LightGBM achieving high performance in binary classification tasks.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;a href="https://github.com/prayat-pu"&gt;Read the paper (2024)&lt;/a&gt;&lt;/p&gt;</description></item></channel></rss>