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- W2786366275 abstract "Most of the applications currently deal with short texts but understanding short texts is a difficult task for machines. Most of the natural language processing tools find it difficult to process short texts. The main reasons for the difficulty in understanding short texts include: i. Short texts usually do not follow the syntax of the written language; ii. Short texts do not contain sufficient statistical signals to support recent developments in text mining such as topic modeling; iii. Short texts are ambiguous and noisy and are generated in larger volumes which further increase the difficulty in handling them. This paper focuses on two efficient semantic knowledge based techniques for understanding short texts which include i. Harvesting and Analyzing Semantic Knowledge (HASK) technique and ii. Semantic Enrichment (SE) technique. These two techniques are evaluated on the basis of three parameters which include accuracy of the system on correctly creating the required predictions, average execution time and space requirement. Experimental results show that the HASK technique outperforms the SE technique." @default.
- W2786366275 created "2018-02-23" @default.
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- W2786366275 date "2017-03-01" @default.
- W2786366275 modified "2023-09-23" @default.
- W2786366275 title "Comparative analysis of semantic knowledge-based techniques for understanding short texts" @default.
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- W2786366275 doi "https://doi.org/10.1109/iciiecs.2017.8276086" @default.
- W2786366275 hasPublicationYear "2017" @default.
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