A Comprehensive Knowledge of Short Texts in Effective Utilizing and Analysis Semantic Knowledge

8 Pages Posted: 10 Jun 2019

See all articles by G. Varada Rajkumar

G. Varada Rajkumar

Ramachandra College of Engineering, Department of Computer Science and Engineering, Students

Dr. A. Daveedu Raju

Ramachandra College of Engineering - Department of Computer Science and Engineering

Dr. V. Surya Narayana

Ramachandra College of Engineering - Department of Computer Science and Engineering

Date Written: May 28, 2019

Abstract

Understanding short texts is crucial to many applications, but challenges abound. Here we focus on short texts which refer to texts with limited context. These short texts are produced including Search queries, Tags, Keywords, Conversation or Social posts and containing limited context. Semantic knowledge is required in order to better understand short texts. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.

Suggested Citation

Rajkumar, G. Varada and Raju, Dr. A. Daveedu and Narayana, Dr. V. Surya, A Comprehensive Knowledge of Short Texts in Effective Utilizing and Analysis Semantic Knowledge (May 28, 2019). International Journal for Innovative Engineering & Management Research, Vol. 08, No. 05, 2019, Available at SSRN: https://ssrn.com/abstract=3395102

G. Varada Rajkumar

Ramachandra College of Engineering, Department of Computer Science and Engineering, Students ( email )

Eluru
India

Dr. A. Daveedu Raju

Ramachandra College of Engineering - Department of Computer Science and Engineering ( email )

India

Dr. V. Surya Narayana (Contact Author)

Ramachandra College of Engineering - Department of Computer Science and Engineering ( email )

India

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