Dear Dhaval Patel,

Thanks for sharing this.

I have a question: Is the test data different from the training?

I assume you have referred to the following two works:

1. Statistical Constituency parser for Sanskrit compounds Amba Kulkarni and Anil Kumar, ICON 2011, Chennai Dec 18-19
2. Clues from Astadhyayi for compound identification Amba Kulkarni and Anil Kumar, 5th international SCLS 2013, Mumbai

The first one reports the performance of the system, using only simple probabilities.

The performance figures for 56 tags were as follows:

Rank      With 55 tags       With 8 tags
1                   63.0%               72.7%
2                   10.9%               13.2%
3                     7.2%                 9.5%

where Rank indicates the position of the correct solution among all possible solutions.

  

The second one uses clues from the A.s.taadhyaayii to detect the compound type of rare compounds, and the performance was 61%  times we got the first guess correct, and if we allow 3 guesses, 81% one among the 3 guesses were correct.

With regards,

Amba Kulkarni

On 16 July 2015 at 00:03, dhaval patel <drdhaval2785@gmail.com> wrote:

Respected scholars,

Recently I have modified one Artificial neural net code for identification of samAsas in Sanskrit language.
https://github.com/drdhaval2785/SamaasaClassification is the code location.

The results are very encouraging.

Without feeding any rule to the computer, the following is the classification result

1. Major 5 samAsa types classification - 70%

2. Minor 55 samAsa subtypes classification - 55 %.

3. Major 5 samAsa types classification taking the first two entries - 85 %.

Probability of a fluke would be 1 - 20 %, 2 - 0.2 %, 40 %.

So, the machine learning is statistically significant, if not good enough.

The database which was used was scraped from http://sanskrit.uohyd.ernet.in/Corpus/SHMT/Samaas-Tagging/ and randomly shuffled to homogenize the dataset.

The tool was developed for samAsa classification initially, but now generalized for any string classification problem.

Hope the scholars would like the tool.

For those interested in Artificial neural networks, the link is http://neuralnetworksanddeeplearning.com/

--

Dr. Dhaval Patel, I.A.S

Collector and District Magistrate, Anand

www.sanskritworld.in

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