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• W2978285909 abstract "The explosive growth of information makes it an important issue to effectively mine useful information from massive information. Text is an important carrier of information, so the processing and analysis of text has become one of the hot spots of data mining and information retrieval. Sentence similarity is the basis of most text-related tasks. The majority of current approaches leverage pairwise similarity characteristics to represent text pairs. Unlike the current approaches, we propose a new method to analyze and quantify the semantic textual similarity between sentences by encoding semantic knowledge based on word embedding into the syntax tree of sentences. We use SemEval-2012 task to test our approach and evaluate the performance with two widely used benchmarks:the Spearman and Pearson correlations, the experimental results show that compared with the best systems of semantic textual similarity (STS) task, our method can effectively improve the accuracy of sentence similarity judgment." @default.
• W2978285909 created "2019-10-10" @default.
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• W2978285909 date "2019-08-01" @default.
• W2978285909 modified "2023-10-12" @default.
• W2978285909 title "Fusing Syntax and Word Embedding Knowledge for Measuring Semantic Similarity" @default.
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• W2978285909 doi "https://doi.org/10.1109/hpcc/smartcity/dss.2019.00062" @default.
• W2978285909 hasPublicationYear "2019" @default.
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