I am just wondering what is the use of n-grams (n>3) (and their occurrence frequency) considering the computational overhead in computing them. Are there any applications where bigrams or trigrams are simply not enough?
If so, what is the state-of-the-art in n-gram extraction? Any suggestions? I am aware of the following:
Using Latin numerical prefixes, an n-gram of size 1 is referred to as a "unigram"; size 2 is a "bigram" (or, less commonly, a "digram"); size 3 is a "trigram". English cardinal numbers are sometimes used, e.g., "four-gram", "five-gram", and so on.
N-grams are continuous sequences of words or symbols or tokens in a document. In technical terms, they can be defined as the neighbouring sequences of items in a document. They come into play when we deal with text data in NLP(Natural Language Processing) tasks.
N-gram models are useful in many text analytics applications where sequences of words are relevant, such as in sentiment analysis, text classification, and text generation. N-gram modeling is one of the many techniques used to convert text from an unstructured format to a structured format.
The ngram tokenizer first breaks text down into words whenever it encounters one of a list of specified characters, then it emits N-grams of each word of the specified length. N-grams are like a sliding window that moves across the word - a continuous sequence of characters of the specified length.
I'm not familiar with a good deal of the tags listed here, however n-grams (the abstract concept) are often useful related to statistical models. As a result, here's some applications which aren't restricted merely to bigrams and trigrams:
Those are the ones off the top of my head, but there's much more listed on Wikipedia.
As far as "state-of-the-art" n-gram extraction, no idea. N-gram "extraction" is an adhoc attempt to speed up certain processes while still maintaining the benefits of n-gram style modeling. In short, "state-of-the-art" depends on what you're trying to do. If you're looking at fuzzy matching or fuzzy grouping, it depends on what kind of data you're matching/grouping. (E.g. street addresses are going to be very different to fuzzy match than first names.)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With