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https://issues.apache.org/jira/browse/OPENNLP-757?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14327756#comment-14327756
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Aakarsh Agarwal commented on OPENNLP-757:
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Hello,
I am Aakarsh Agarwal, currently pursuing B.Tech from IIT Roorkee in India. I
hope to participate in GSoC this year and want to contribute to this project.
This project seems interesting to me because I feel comfortable in coding in
JAVA, though I also know C++.
The project seems very interesting as it mainly deals with machine learning and
related algorithms. It would be a fun to code such algorithms. I am also going
through wiki pages of some of these algorithms such as "Decision Trees" and
"Naive Bayes". I just have one doubt if I will be required to code these
algorithms from scratch in JAVA or use some existing code and work upon it.
I would like to hear from mentor how to get started and the probable challenges
that needs to be fulfilled concerning this project. I am eagerly waiting for a
positive reply very soon.
Regards
AAKARSH AGARWAL
IIT ROORKEE
> Supervised WSD techniques
> -------------------------
>
> Key: OPENNLP-757
> URL: https://issues.apache.org/jira/browse/OPENNLP-757
> Project: OpenNLP
> Issue Type: New Feature
> Components: Machine Learning, POS Tagger, Sentence Detector, Stemmer
> Reporter: Mondher Bouazizi
> Labels: gsoc, gsoc2015, java, nlp, wsd
>
> The objective of Word Sense Disambiguation (WSD) is to determine which sense
> of a word is meant in a particular context. Therefore, WSD is a
> classification task, where the classes are the different senses of the
> ambiguous word.
> Different techniques are proposed in the academic literature, which fall
> mainly into two categories: Supervised and Unsupervised.
> For this component, we focus on supervised techniques: these approaches use
> machine-learning techniques to learn a classifier from labeled training sets.
> The object of this project is to create a WSD solution (for English) that
> implements some supervised techniques. For example:
> - Decision Lists
> - Decision Trees
> - Naive Bayes
> - Neural Networks
> - Exemplar-Based or Instance-Based Learning
> - Support Vector Machines
> - Ensemble Methods
> - Semi-supervised Disambiguation
> - Etc.
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