Informatics Publishing Limited

Richa Dhagat ¹, Arpana Rawal ¹, Sunita Soni ¹

Affiliations
1 Department of Computer Science and Engineering, Bhilai Institute of Technology, IN

Abstract

Text Semantic Similarity can be viewed as one of the challenging tasks as evident from current profound interest in NLP research community that has created achievable milestones through active participation in SemEval task series of the recent decade. Amidst these developments, it was realized that exploring text to compare its semantics largely depends on valid grammatical structures of sentences and sentence formulation types. In this paper, the computation of text semantic similarity is addressed by devising a novel set of generic similarity metrics based on both, word-sense of the phrases constituting the text as well as the grammatical layout and sequencing of these word-phrases forming text with sensible meaning. We have used the combination of word-sense and grammatical similarity metrics over benchmark sentential datasets. Having obtained highest value of Pearson’s correlation coefficient (0.89) with mean human similarity scores, when compared against equivalent scores obtained through closely competent structured approach models, plagiarism-detection classification task was revisited on well-known paragraph-phrased Rewrite corpus articulated by Clough and Stevenson (2011) using our model to provide generic utility perspective to these novel devised similarity metrics. Here also, nearly competent classification model performance (with accuracy 76.8%) encouraged authors to work in directions that are more promising where the performance can be enhanced by improving upon dependency (grammatical relations) component in order to raise the count of true-positives and false-negatives.

Keywords

Structural Features, Word-Sense Similarity, Grammatical Similarity, Generic Similarity Metrics, Wikipedia Rewrite Corpus.

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Shankha De ¹, Arpana Rawal ¹

Affiliations
1 Department of Computer Science and Engineering, Bhilai Institute of Technology, IN

Abstract

Since, last one-decade, numerous deep learning models have been designed to resolve handwritten character recognition task in languages, namely, English, Chinese, Arabic, Japanese and Russian. Recognition of Bengali handwritten character from document image datasets is undoubtedly an open challenging task. Due to the advancement of neural network, many models have been developed and it is improving performance. The LeNet is a pioneering work in the field handwritten document image recognition specially hand written digits from the images by using CNN. This paper focuses on designing a convolution neural network with refinements on layers and its parameter tuning for Bengali character recognition system for classification of 50 different fonts. Our revised CNN model outperforms on some existing approach and shows font-recognition accuracy of 98.46%.

Keywords

Convolution Neural Network, Handwritten Character, LeNet

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