知识点回顾:词向量 词向量表示 One-hot Representation “黑板”表示为[0001000000000000…] Distributional Representation “黑板”表示为[0.792,-0.177,-0.107,0.109,-0.542,…] 词向量降维 SVD. LSA, LDA Based on lexical co-occurrence Learning representations Predict surrounding words of every word 塔款大学⌒ Eg. word2vec 社会计算与信息检索研究中心知识点回顾：词向量 • 词向量表示 – One-hot Representation • “黑板”表示为 [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 ...] – Distributional Representation • “黑板”表示为 [0.792, −0.177, −0.107, 0.109, −0.542, ...] • 词向量降维 – SVD，LSA，LDA • Based on lexical co-occurrence – Learning representations • Predict surrounding words of every word • Eg. word2vec