Abstract
Transforming information in a digital way modifies the people views and their daily functioning. Social media is a key platform where people express their views regarding any event and it also plays an important role in daily activities. Digital marketing is an example of such digital transformation of information. In this present era, social channels use their personal information of the users to launch any product or tool. Digital Education plays a key role in transforming information in a digital way. In such cases, Natural Language Processing of people views and blog chatting plays an important role. Adding an explanatory layer is important for an Intelligent Tutoring System (ITS), where students interact with an application through natural language. This paper proposed a method, which will able to measure the interpretability between two sentences by rating the degree of semantic equivalence on a graded scale from 0 (not aligned) to 5 (semantically equivalent). The goal of the paper is not to add an interpretable layer but developed a method which can explain the similarities and differences between the two sentences. This task has been motivated by SemEval 2016 Task 2. The proposed method has been developed and tested over the headlines dataset. For the gold standard data, an accuracy of 0.64 for alignment type and score is reported.
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Acknowledgement
The work presented here falls under the Research Project Grant No. YSS/2015/000988 and supported by the Department of Science & Technology (DST) and Science and Engineering Research Board (SERB), Govt. of India. The authors would like to acknowledge the Department of Computer Science & Engineering, National Institute of Technology Mizoram, India for providing infrastructural facilities and support.
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Majumder, G., Pakray, P., Avendaño, D.E.P. (2018). Interpretable Semantic Textual Similarity Using Lexical and Cosine Similarity. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_59
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