bigram

the Word feature in the sorting model. N-gram refers to the processing of n consecutive words as a unit, for example: "John likes to watch movies." Mary likes movies too. " This sentence is processed as a simple word bag model after the result is: ["John": 1, "likes": 2, "to": 1, "Watch": 1, "movies": 2, "Mary": 1, "too": 1] The result of processing as Bigram (2-gram) is: ["John likes": 1, "likes to": 1, "to watch": 1, "Watch movies": 1, "Mary likes

formula (1) can be approximately: (2) When N is 1, 2, and 3, the n-gram model is called the unigram, bigram, and trigram language models respectively. The parameter of the n-gram model is the conditional probability P (Wi|Wi-n + 1,...,Wi-1). Assume that the word table size is 100,000, then the number of parameters in the n-gram model is 100,000 n. The larger the N value, the more accurate the model and the more complex it is. The larger the computing

hypothesis , the probability of the occurrence of a word only depends on the word's first 1 words or the first few words, then there is(1) The appearance of a word depends only on the first 1 words, namely bigram(2-gram):(2) The appearance of a word depends only on the first 2 words, namely trigram(3-gram):The greater the N value of the N-gram, the stronger the binding on the next word, because the more information is provided, the more complex the m

First, what is N-gram?Wikipedia on the definition of N-gram:N-gram is a statistical language model used to predict the nth item based on the previous (n-1) item. At the application level, the item can be a phoneme (speech recognition application), a character (input method application), a word (word breaker), or a base pair (genetic information). In general, N-gram models can be generated from large-scale text or audio corpora.Accustomed to, 1-gram called Unigram,2-gram called

and make sentence tests. You may notice that the sentence of the output is made up of the words given by sample, rather than the words with the highest probability, because if the word with the highest probability is always taken, the most likely word will be repeated at the end. Implementation code See lstm.pyBeam SearchIn the above process, each time is a character as a unit, you can use a bit more characters to make predictions, the highest probability of the one, to prevent special

in bestwords]) print 'evaluating best word features'evaluate_classifier(best_word_feats) def best_bigram_word_feats(words, score_fn=BigramAssocMeasures.chi_sq, n=200): bigram_finder = BigramCollocationFinder.from_words(words) bigrams = bigram_finder.nbest(score_fn, n) d = dict([(bigram, True) for bigram in bigrams]) d.update(best_word_feats(words)) return d print 'evaluating best words +

Language modelP(S) is the language model, the model used to calculate the probability of a sentence S . So, how do you calculate it? The simplest and most straightforward method is to do division after counting, that is, maximum likelihood estimation (Maximum likelihood Estimate,mle), as follows:whichCOUNT(W1,w2,... ,wi? 1,wi) Denotes a word sequence(W1,w2,... ,wi? 1,wi) The frequency that appears in the corpus. Here are two important issues: sparse data and too l

parameter space is too large (the probability of conditional probability p (wn|w1,w2,..., wn-1) is too much, cannot be estimated), and the other flaw is that the data is sparse and serious. Sparse interpretation of data:Suppose there are 20,000 words in the glossary, if it is the Bigram model (two metamodel) then there are 400 million possible 2-gram, such asThe fruit is trigram (3-yuan model), then there are 8 trillion possible 3-gram! So the combin

as:func1 = if (output = B and feature= "U02: One") return 1 else return 0Func2 = if (output = M and feature= "U02: One") return 1 else return 0func3 = if (output = E and feature= "U02: One") return 1 else return 0Func4 = if (output = S and feature= "U02: One") return 1 else return 0...Funcx = if (output = B and feature= "U02:") return 1 else return 0Funcy = if (output = S and feature= "U02:") return 1 else return 0...The total number of feature functions generated by a model is l*n, where L is

I.Dismax 1. Tie : Query and init Param for tiebreaker Value2. QF : Query and init Param for query Fields3. PF : Query and init Param for phrase boost Fields4. Pf2 : Query and init Param for bigram phrase boost Fields5. PF3 : Query and init Param for trigram phrase boost Fields6. Mm : Query and init Param for minshouldmatch Specification7. PS : Query and init Param for phrase slop value in phrase boost query (in PF fields)8. PS2 : Default phrase slop