Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus
Adilakshmi Vadavalli1, R Subhashini2

1Adilakshmi Vadavalli, Research Scholar, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2R Subhashini, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 19 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 605-611 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11120782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1112.0782S319
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Abstract: Lot of research has gone into Natural language processing and the state of the art algorithms in deep learning that unambiguously helps in converting an English text into a data structure without loss of meaning. Also with the advent of neural networks for learning word representations as vectors has helped a lot in revolutionizing the automatic feature extraction from text data corpus. A combination of word embedding and the use of a deep learning algorithm like a convolution neural network helped in better accuracy for text classification. In this era of Internet of things and the voluminous amounts of data that is overwhelming the users determining the veracity of the data is a very challenging task. There are many truth discovery algorithms in literature that help in resolving the conflicts that arise due to multiple sources of data. These algorithms help in estimating the trustworthiness of the data and reliability of the sources. In this paper, a convolution based truth discovery with multitasking is proposed to estimate the genuineness of the data for a given text corpus. The proposed algorithm has been tested on analysing the genuineness of Quora questions dataset and experimental results showed an improved accuracy and speed over other existing approaches.
Keywords: Truth Discovery, Natural Language Processing, Text Classification, Deep Learning, Word Embedding.
Scope of the Article: Deep Learning