REWARDING JOB OPPORTUNITY (as I am leaving for few months)
this is a last seminar organised by me, at least for some time, as I am leaving for 2.5 months. I hope that seminars will continue uninterrupted, and that people of MACS will take some of my responsibilities in their own hands.
- There is a need for someone to take over sending emails
- and for someone to show up every time with a laptop and make sure that there is no chaos.
I am not sure if this could be maybe classified as lab helping, or other for an additional reward, but I guess you could try.
if you would be up for it, let me know soon
TODAY'S SPEAKER:
Hamouda Chantar - Document categorization
this is a last seminar organised by me, at least for some time, as I am leaving for 2.5 months. I hope that seminars will continue uninterrupted, and that people of MACS will take some of my responsibilities in their own hands.
- There is a need for someone to take over sending emails
- and for someone to show up every time with a laptop and make sure that there is no chaos.
I am not sure if this could be maybe classified as lab helping, or other for an additional reward, but I guess you could try.
if you would be up for it, let me know soon
TODAY'S SPEAKER:
Hamouda Chantar - Document categorization
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Abstract—Document categorization is an important
topic that is central to many applications that demand
reasoning about and organisation of text documents,
web pages, and so forth. Document classification is
commonly achieved by choosing appropriate features
(terms) and building a TFIDF document vector feature.
In this process, feature selection is a key factor in
determining the accuracy and effectiveness of resulting
classifications. For a given classification task, the right
choice of features means accurate classification with
suitable levels of computational efficiency. Meanwhile,
most document classification work is based on English
language documents. In this work (paper) we make three main
contributions: (i) we demonstrate successful document
classification in the context of Arabic documents
(although previous work has demonstrated text
classification in Arabic, the datasets used, and the
experimental setup, have not been revealed); (ii) we
offer our datasets to enable other researchers to
compare directly with our results; (iii) we demonstrate
a combination of Binary Particle Swarm Optimization
and K nearest neighbour that performs well in selecting
good sets of features for this task.
topic that is central to many applications that demand
reasoning about and organisation of text documents,
web pages, and so forth. Document classification is
commonly achieved by choosing appropriate features
(terms) and building a TFIDF document vector feature.
In this process, feature selection is a key factor in
determining the accuracy and effectiveness of resulting
classifications. For a given classification task, the right
choice of features means accurate classification with
suitable levels of computational efficiency. Meanwhile,
most document classification work is based on English
language documents. In this work (paper) we make three main
contributions: (i) we demonstrate successful document
classification in the context of Arabic documents
(although previous work has demonstrated text
classification in Arabic, the datasets used, and the
experimental setup, have not been revealed); (ii) we
offer our datasets to enable other researchers to
compare directly with our results; (iii) we demonstrate
a combination of Binary Particle Swarm Optimization
and K nearest neighbour that performs well in selecting
good sets of features for this task.
