Research Article
Rumor Detection Based on Knowledge Enhancement and Graph Attention Network
| Input: aspect , dependency tree T, and dependency relations r. | | Output: subject dependency tree T1ˆ. 1: | (1) | Construct the root R for T1ˆ; | (2) | for x to k do | (3) | for y = 1 to n do | (4) | if then, | (5) | | (6) | else if then, | (7) | | (8) | else | (9) | = change word() | (10) | n = distance(x,y) | (11) | | (12) | end if | (13) | end for | (14) | end for | (15) | return T1ˆ |
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