Taxonomy learning from Malay texts using artificial immune system based clustering
In taxonomy learning from texts, the extracted features that are used to describe the context of a term usually are erroneous and sparse. Various attempts to overcome data sparseness and noise have been made using clustering algorithm such as Hierarchical Agglomerative Clustering (HAC), Bisecting K-...
Saved in:
Main Author: | Ahmad Nazri, Mohd. Zakree |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2011
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/36947/1/MohdZakreeAhmadNazriPFSKSM2011.pdf http://eprints.utm.my/id/eprint/36947/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
by: Ahmad Nazri, Mohd. Zakree, et al.
Published: (2010) -
Using linguistic patterns in FCA-based approach for automatic acquisition of taxonomies from Malay text
by: Ahmad Nazri, Mohd. Zakree, et al.
Published: (2008) -
A hybrid approach for learning concept hierarchy from Malay text using GAHC and immune network
by: Ahmad Nazri, Mohd. Zakree, et al.
Published: (2009) -
Enhanced framework for alert processing using clustering approach based on artificial immune system
by: Mohamed, Ashara Banu
Published: (2015) -
A review on learning taxonomies from Malay text corpora
by: Ahmad Nazri, Mohd. Zakree, et al.
Published: (2007)