From 6d8ae3e811681066c51b2713c8fcf225360f7bf2 Mon Sep 17 00:00:00 2001 From: Dylan De Faoite Date: Wed, 25 Mar 2026 19:44:14 +0000 Subject: [PATCH] docs: add section on Topic Modelling in NLP --- report/main.tex | 3 +++ 1 file changed, 3 insertions(+) diff --git a/report/main.tex b/report/main.tex index 712da72..79f5bf3 100644 --- a/report/main.tex +++ b/report/main.tex @@ -109,6 +109,9 @@ NLP can carry out many different types of tasks, such as classifying sentences o \textbf{Named Entity Recognition (NER)} is the process of identifying and classifying key entities within a text into predefined categories like names of people, organisations, locations, or dates. NER is essential for structuring unstructured text data and is often used in information extraction, search engines, and question-answering systems. Despite its usefulness, NER can struggle with ambiguous entities or context-dependent meanings. \subsubsection{Topic Modelling} +\textbf{Topic Modelling} is a technique used in NLP to identify the main themes or topics present in a collection of text. Instead of analysing each sentence individually, topic modelling looks for patterns of words that frequently appear together, allowing it to group documents based on similar themes. + +This method is often used to organise lots of unstructured data, such as news articles, research papers, or social media posts. \subsection{Cork Dataset}