WSESA

AcronymDefinition
WSESAWeapon System & Equipment Support Analysis
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In order to make a better illustration of the performance of the algorithm and the openness of the algorithmic system, the algorithm precision rate of some algorithm systems which are respectively Watson system evidence-scoring algorithm (WSESA) [26] and prominent feature extraction evidence gathering algorithm (PFEGA) [27], and the depth text similarity fusion algorithm (DTSFA) was conducted as a comparative test.
WSESA is used to calculate the textual information from four aspects: word frequency (by PTM), word order (by TA), syntax (by S-B), and structure (by LFACS).
System Feature Precision (%) Recall (%) algorithm (%) WSESA PTM 73.7 91.8 S-B 81.4 90.4 TA 75.3 84.9 LFACS 86.2 57.5 PFEGA Lexical features 57.2 62.4 Syntactic features 63.7 79.8 Semantic features 71.8 84.6 Structural features 68.6 82.2 DTSFA Frequency 76.8 92.7 Order 74.5 82.3 TF-IDF 83.4 75.1 Syntax 85.8 88.7 Structure 72.3 83.4 Semantics 80.6 77.2 System Feature F-measure (%) algorithm (%) WSESA PTM 81.7 S-B 85.7 TA 79.8 LFACS 69.0 PFEGA Lexical features 59.68 Syntactic features 70.84 Semantic features 77.67 Structural features 74.78 DTSFA Frequency 84.0 Order 78.21 TF-IDF 79.03 Syntax 87.23 Structure 77.46 Semantics 78.86 [Please note: Some non-Latin characters were omitted from this article]