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The Republic of Uzbekistan with Youth Reforms at a New Stage of Work
CENTRAL ASIAN JOURNAL OF SOCIAL SCIENCES AND HISTORY, (2022)
Аннотация
We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.
