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| Both sides previous revision Previous revision Next revision | Previous revision | ||
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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 |
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| 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 to be 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 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. | ||
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| 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. | ||
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| + | 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. | ||