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 courses:rg:2014:wr [2014/11/04 01:24]hoangt courses:rg:2014:wr [2014/11/04 14:41] (current)popel Both sides previous revision Previous revision 2014/11/04 14:41 popel 2014/11/04 01:26 hoangt 2014/11/04 01:24 hoangt 2014/11/04 01:24 hoangt 2014/11/04 01:24 hoangt created Next revision Previous revision 2014/11/04 14:41 popel 2014/11/04 01:26 hoangt 2014/11/04 01:24 hoangt 2014/11/04 01:24 hoangt 2014/11/04 01:24 hoangt created Line 8: Line 8: dog 11010111010 dog 11010111010 cat 11010111010 cat 11010111010 - + 2. Section 4.1 defines a corrupted (or noise) n-gram, but there is a tiny error/typo in the definition. Try nitpicking and point it out. - 2. Section 4.1 defines a corrupted (or noise) n-gram, but there is a tiny error/typo in the definition. Try to be nitpicking and point it out. + 3. Section 7.4 states that "word representations in NER brought larger gains on the out-of-domain data than on the in-domain data." Try to guess what is the reason. 3. Section 7.4 states that "word representations in NER brought larger gains on the out-of-domain data than on the in-domain data." Try to guess what is the reason. Line 17: Line 16: b) Does it contain any compound feature with word representations?​ b) Does it contain any compound feature with word representations?​ c) Give an example of a possible compound feature with word representations for the NER task. c) Give an example of a possible compound feature with word representations for the NER task. + + 5. + Consider the C&W embedding vectors with 50 dimensions. Guess which word has the embedding vector most similar (by Euclidean distance) to the following vector: + a) vector(king) - vector(man) + vector(woman) + b) vector(dollars) - vector(dollar) + vector(mouse) **Hint** : The paper is 11-page long. You can skip section 2 and section 3.2 which are the literature review. **Hint** : The paper is 11-page long. You can skip section 2 and section 3.2 which are the literature review.

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