Prediction of Part of Speech Tags for Punjabi using Support Vector Machines

Abstract: Part-of-Speech (POS

Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector

AbstractEducational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. In order to keep a check on the changes occurring in curriculum patterns, a regular analysis is must of educational databases. This paper focus on identifying the slow learners among students and displaying it by a predictive data mining model using classification based algorithms. Real World data set from a high school is taken and filtration of desired potential variables is done using WEKA an Open Source Tool. The dataset of student academic records is tested and applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA an Open source tool. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also shown among all five classifiers. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines

An efficient automated answer scoring system for Punjabi language

Automated Scoring is a developing technology. The accuracy and reliability of these systems have been proven to be much higher. Besides being a time-and money-saver, there are a number of studies which are being conducted to provide variety in feedback, not only on grammatical issues, but also on semantic related issues. This will reduce not only the paper load of the teachers, but as well as teachers’ assessment related issues. In this paper, we remove those dummy note bins, thereby having 183 dimensions in total. We augmented the input by concatenating it with the first-order difference of the semitone filtered spectrogram. We observed a significant increase of the transcription performance with this addition. Compared to feedforward neural networks, recurrent neural network (RNN) are capable of learning temporal dependency of sequential data, which is the property found in music answer scoring.Also, the Long Short-Term Memory (LSTM) unit has a memory block updated only when an input or forget gate is open, and the gradients can propagate through memory cells without being multiplied each time step. Throughout backward and forward layers together, the networks can access to both history and future of the given time frame. Comparative analyses show that the proposed technique outperforms existing techniques. Keywords: Natural Language Processing, Automated scoring system, Ambiguit