From ef49ab9fd3e70b260ecb2e5943071a60dcf04479 Mon Sep 17 00:00:00 2001 From: Sarah Oberbichler <66369271+soberbichler@users.noreply.github.com> Date: Tue, 24 Dec 2024 15:01:06 +0100 Subject: [PATCH] Update module_6.html --- modules/module_6.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/module_6.html b/modules/module_6.html index 1b9a78c..5d6f645 100644 --- a/modules/module_6.html +++ b/modules/module_6.html @@ -50,8 +50,8 @@

Module 6: Named Entity Recognition and Text Classification

Module 6 will present different approaches to Named Entity Recognition/Extraction and Text Classification. Named Entity Recognition (NER) is a natural language processing task that identifies and classifies named entities (like people, organizations, locations, dates) in text. For example, in "Apple released iPhone in 2007", NER would identify "Apple" as an organization, "iPhone" as a product, and "2007" as a date. Text Classificaion Text classification assigns predefined categories/labels to text documents. Example: Categorizing emails as spam/not-spam, or news articles into topics like sports, politics, technology. We will use:

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  1. NER using models trained by the Impresso Project via the Impresso API
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  2. Question Answering based Namend Entiy Extraction using the small language model NuExtract
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  3. NER using models via the Impresso API (Impresso Project)
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  4. Question-Answering based Namend Entiy Extraction using the small language model NuExtract
  5. Text Classification using a small language model