dylan/crosspost
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 1 Wiki Activity

docs: add section on Topic Modelling in NLP

Browse Source
This commit is contained in:
Dylan De Faoite
2026-03-25 19:44:14 +00:00
parent 376773a0cc
commit 6d8ae3e811
1 changed files with 3 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
report/main.tex
View File

@@ -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. \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} \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} \subsection{Cork Dataset}