【刘知远NLP课 整理】Word Representation

Word representation is a process that transform the symbols to the machine understandable meanings. The goals of Word Representation are

1. Compute word similarity

WR(Star) ≃ WR(Sun)
WR(Motel) ≃ WR(Hotel)

2. Infer word relation

WR(China) − WR(Beijing) ≃ WR(Japan) - WR(Tokyo)
WR(Man) ≃ WR(King) − WR(Queen) + WR(Woman)
WR(Swimming) ≃ WR(Walking) − WR(Walk) + WR(Swim)

Now we start to discuss some ways of obtaining word representations.

1. Use a set of related words

Such as using synonyms and hypernyms to represent a word. e.g. WordNet, a resource containing synonym and hypernym sets.

However, lots of problems exist:

• Missing nuance

  ("proficient", "good") are synonyms only in some contexts

• Missing new meanings of words

  Apple (fruit → IT company)

• Subjective

• Data sparsity

• Requires human labor to create and adapt

2. One-Hot Representation

Regard words as discrete symbols.

• Vector dimension = # words in vocabulary
• Token with greater ID value doesn't imply more or less important as compared with the token with less ID value.

The problem is all the vectors are orthogonal. No natural notion of similarity for one-hot vectors.

标签：NLP,vectors,Word,matrix,each,WR,words,Representation,word
来源： https://www.cnblogs.com/thousfeet/p/15144001.html