I. Model Architecture
Recursive Neural Network and the Semantic EmbeddingXugang YeI. Model ArchitectureRNN803606192908yt-1yttanh+ltxtyt-1yttanh+ltxt19364263810001270000-34990018288005238750018288009271000LSTM501142039359504445386221179yt00yt4770225308610173418529908500902970120650ct-100ct-11271270213360348996015106650036893501332230++2828290157797500160020030480000138038529908500487553068475××4245034124281tanh00tanh447803644150005011950565150041652990622tanhyt-1yt(g)ltxtitsigmoid+++ftsigmoid00tanhyt-1yt(g)ltxtitsigmoid+++ftsigmoid3247495177800××18860558064500282818580645003478635812800013805958318500102870080010102870080010004194705160655ct00ct4252595217805004164521123478392102016446500189621543815003257655123190××1599460438150010287004254501035050514354262650281940sigmoidsigmoid19089152171701033145290830002828395438150012612152178054809381255298ot00ot480761322591943588272682742418715506730004115965135255++1038860260880004261933621800II. FormulationIII. Learning ................
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