machine learning - What is the meaning of this NLP notation? -


I am trying to find relation extraction and learn NLP on a corpus. I found the slide and trying to parse the marking for a high dimensional feature vector (shown below).

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Where

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How do I get the most equation in an English language? Change sentence? For each input text unit, x; For every possible convenience, y - convenience x is - can one be presented by a feature vector? I am used to seeing cartesian product notation and I am used to see the function notation and I used to see set builder notation. But there are very unfamiliar things in the line that I understand what it says. What is the meaning of colon? This means that F is a function that takes input and output and takes one output. In this context, the input is usually a word sequence, and that word sequence's candidate labeling output - eg. Part-of-speech tags or sequences of a parse tree are some examples on slide 8 of Ryan McDonald's slide deck.

Macdonald also makes this point, but I repeat it here: In some cases, we can only create one attribute vector for the input sequence (without reference to any output) for example If we are tagging 2 words of the term 'f function', and our feature mapping only includes existing words and previous words, then we will include 'f' as the last word and 'as' Current sh However, in some cases (especially 'structured prediction') we would like to include features based on the labeling of the candidate - perhaps a label sequence on the whole input (note that usually huge feature space) .

Another note: The mapping of McDonald's is for a real value vector (R ^ N), but in NLP, we often find that the pointer features are adequate, so many systems give us a little more The vector (still in a very high dimensional space) does not change the formality (mapping function only), but the simplified concept is often used in weight vector collection and dot-product implementation. Will allow the capability.


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