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 1. 文中所包含的word vector:
    a) Skip-gram
    b) CBOW
    ==> Both can find in word2vec
    c) vLBL
    d) ivLBL
    ==> Both can find in the paper Learning word embeddings efficiently with noise-contrastive estimation.
    e) HPCA
    ==> which can find in the paper Word Embeddings through Hellinger PCA.
 2. 不同的task:
    a) Word analogies.
    b) Word similarity.
    ==> 评价集合:WordSim-353、MC、RG、SCWS、RW
    c) Named entity recognition.
    ==> 评价集合:CoNLL-2003, ACE Phase 2,ACE-2003.
 3. 需要做的工作:
    a) 寻找不同的task
    b) 比较各种word vector的性能


 1. 在仅仅用 Lucene 做 extraction进行算法匹配的情况下,有足够多的模板能够达到85%以上的准确率。

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