公开数据集
数据结构 ? 8.62G
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
这是一个单词嵌入模型,创建于维基百科+各种来源的评论。与从基于短语的方法(不考虑相邻词的短语/双词上下文)创建双词不同,这是通过考虑中心词周围的所有单词和双词上下文来训练的。
Content
The source code for the dataset is available here, installation can done by "python setup.py install"
Training script can be found here
https://github.com/s4sarath/gensim_ngram/blob/master/train_ngram.py
Training model can be found here
https://github.com/s4sarath/gensim_ngram/blob/master/README.md
Sample similar words from the model
model.wv.most_similar
a.) amazing product
[('amazing product', 1.0), ('awesome product', 0.9272927), ('amazing product,', 0.888031), ('incredible product', 0.8867724), ('amazing product!', 0.88521475), ('amazing product.', 0.8845437), ('awesome product!', 0.8644207), ('amazing product!!', 0.8612526), ('amazing product!!!', 0.85835207), ('awesome product.', 0.8530247), ('awesome product!!', 0.8516336), ('awesome product,', 0.8495761), ('awesome item', 0.8434567), ('product. amazing', 0.84247625), ('incredible product.', 0.8421844), ('awesome product!!!', 0.84074044), ('wonderful product', 0.8406575), ('awesome device', 0.836467), ('incredible product!', 0.8337494), ('fantastic product', 0.8330554)]
b.) brad pitt
[('brad pitt', 1.0), ('julia roberts', 0.84390914), ('angelina jolie', 0.84303164), ('ben affleck', 0.8231394), ('matt damon', 0.81166387), ('affleck', 0.8074477), ('george clooney', 0.80540144), ('costner', 0.80255926), ('tom hanks', 0.8017744), ('dustin hoffman', 0.79872185), ('natalie portman', 0.798303), ('ryan gosling', 0.79511935), ('dicaprio', 0.79246503), ('kevin spacey', 0.7921234), ('alec baldwin', 0.7907918), ('actor brad', 0.7901952), ('russell crowe', 0.78980654), ('kevin costner', 0.7894964), ('christopher walken', 0.7882538), ('jennifer aniston', 0.7878684)]
c.) mohanlal
[('mohanlal', 1.0), ('mammootty', 0.9794469), ('kamal haasan', 0.9596181), ('haasan', 0.9563364), ('rajkumar', 0.95312166), ('gopi', 0.9529321), ('sivaji', 0.95167804), ('madhavan', 0.9510826), ('dileep', 0.95085794), ('chiranjeevi', 0.95059955), ('jayaram', 0.9503455), ('nagesh', 0.9484335), ('sathyaraj', 0.9479996), ('rajinikanth', 0.94777143), ('suresh gopi', 0.9466225), ('sivaji ganesan', 0.94393903), ('prakash raj', 0.9437847), ('sathyan', 0.9431832), ('prabhu', 0.942392), ('bharath', 0.9391954)]
d.) machine learning
[('machine learning', 1.0000001), ('learning algorithms', 0.8841063), ('data mining', 0.8291545), ('machine translation', 0.814913), ('support vector', 0.80520463), ('algorithms', 0.8029659), ('learning theory', 0.8026564), ('algorithms and', 0.80255526), ('information retrieval', 0.7991563), ('neural networks', 0.7982512), ('vector machines', 0.79787594), ('machine intelligence', 0.79575825), ('learning algorithm', 0.7918976), ('reinforcement learning', 0.7897328), ('language processing', 0.78945714), ('and computational', 0.7862742), ('vector machine', 0.78508246), ('knowledge representation', 0.7850384), ('algorithmic', 0.7817018), ('distributed systems', 0.7809721)]
e.) mortal kombat
[('mortal kombat', 0.99999994), ('kombat', 0.92918265), ('tekken', 0.855644), ('kombat ii', 0.8423183), ('virtua fighter', 0.82694477), ('soulcalibur', 0.8240025), ('ninja gaiden', 0.8233547), ('darkstalkers', 0.8189633), ('kombat vs', 0.8051237), ('kombat armageddon', 0.80245066), ('kombat series', 0.80217266), ('samurai shodown', 0.8003039), ('resident evil', 0.8001634), ('game mortal', 0.7937777), ('in capcom', 0.7936872), ('kombat mortal', 0.7936853), ('mortal', 0.79330146), ('kombat deception', 0.7923815), ('onimusha', 0.7913557), ('virtua', 0.79038495)]
f.) nissan
[('nissan', 1.0000002), ('mazda', 0.9355751), ('toyota', 0.89277387), ('lexus', 0.89011514), ('subaru', 0.8749101), ('toyota corolla', 0.86015534), ('nissan skyline', 0.85717183), ('mazda rx', 0.8544719), ('volkswagen', 0.8482176), ('bmw', 0.84316957), ('mitsubishi', 0.8426397), ('honda', 0.8378298), ('infiniti', 0.8358605), ('celica', 0.83509576), ('chevrolet corvette', 0.8315984), ('isuzu', 0.8309591), ('nissan gt', 0.8307908), ('datsun', 0.8291819), ('chevrolet', 0.8271923), ('opel', 0.8265841)]
Inspiration
Conventional phrase based word2vec ( including gensim Phrase approach ) is not considering phrase based context/window or neighbor words.
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