content_copy Copy Part-of-speech tags cook_VERB, _DET_ President. N-gram is probably the easiest concept to understand in the whole machine learning space, I guess. Laplace smoothing is the assumption that each n-gram in a corpus occursexactly one more time than it actually does. For the bigram I happy, the probability is equal to 0 because that sequence never appears in the Corpus. However, the trigram 'am a boy' is not in the table and we need to back-off to 'a boy' (notice we dropped one word from the context, i.e., the preceding words) and use its log probability -3.1241505. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. At this point the Python SRILM module is compiled and ready to use. A probability distribution specifies how likely it is that an experiment will have any given outcome. This can be simplified to the counts of the bigram x, y divided by the count of all unigrams x. Simply put, an N-gram is a sequence of words. In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sample of text or speech. That's because the word am followed by the word learning makes up one half of the bigrams in your Corpus. To view this video please enable JavaScript, and consider upgrading to a web browser that You can compute the language model probability for any sentences by using the query command: which will output the result as follows (along with other information such as perplexity and time taken to analyze the input): The final number -9.585592 is the log probability of the sentence. Note that the notation for the count of all three words appearing is written as the previous two words denoted by w subscript 1 superscript 2 separated by a space and then followed by w subscript 3. Generate Unigrams Bigrams Trigrams Ngrams Etc In Python less than 1 minute read To generate unigrams, bigrams, trigrams or n-grams, you can use python’s Natural Language Toolkit (NLTK), which makes it so easy. where c(a) denotes the empirical count of the n-gram a in thecorpus, and |V| corresponds to the number of unique n-grams in thecorpus. Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze, Speech and Language Processing, 2nd Edition by Daniel Jurafsky and James H. Martin, COCA (Corpus of Contemporary American English). Let's start with an example and then I'll show you the general formula. I don't know how to do this. True, but we still have to look at the probability used with n-grams, which is quite interesting. For example, a probability distribution could be used to predict the probability that a token in a document will have a given type. Finally, bigram I'm learning has a probability of 1/2. However, we c… You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Unigrams for this Corpus are a set of all unique single words appearing in the text. On the other hand, the sequence I happy does not belong to the bigram sets as that phrase does not appear in the Corpus. Thus, to compute this probability we need to collect the count of the trigram OF THE KING in the training data as well as the count of the bigram history OF THE. Before we go and actually implement the N-Grams model, let us first discuss the drawback of the bag of words and TF-IDF approaches. Have some basic understanding about – CDF and N – grams. Please make sure that youâre comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. Well, that wasn’t very interesting or exciting. For example, in this Corpus, I'm happy because I'm learning, the size of the Corpus is m = 7. Given a large corpus of plain text, we would like to train an n-gram language model, and estimate the probability for an arbitrary sentence. KenLM is a very memory and time efficient implementation of Kneaser-Ney smoothing and officially distributed with Moses. Another example of bigram is am happy. Then we can train a trigram language model using the following command: This will create a file in the ARPA format for N-gram back-off models. In this example the bigram I am appears twice and the unigram I appears twice as well. code. 0. when we are looking at the trigram 'I am a' in the sentence, we can directly read off its log probability -1.1888235 (which corresponds to log P('a' | 'I' 'am')) in the table since we do find it in the file. But for now, you'll be focusing on sequences of words. Word2vec, Parts-of-Speech Tagging, N-gram Language Models, Autocorrect. While this is a bit messier and slower than the pure Python method, it may be useful if you needed to realign it with the original dataframe. This is the conditional probability of the third word given that the previous two words occurred in the text. For example, the word I appears in the Corpus twice but is included only once in the unigram sets. For example, any n-grams in a querying sentence which did not appear in the training corpus would be assigned a probability zero, but this is obviously wrong. Natural Language Processing with Probabilistic Models, Natural Language Processing Specialization, Construction Engineering and Management Certificate, Machine Learning for Analytics Certificate, Innovation Management & Entrepreneurship Certificate, Sustainabaility and Development Certificate, Spatial Data Analysis and Visualization Certificate, Master's of Innovation & Entrepreneurship. Smoothing is a technique to adjust the probability distribution over n-grams to make better estimates of sentence probabilities. probability of the next word in a sequence is P(w njwn 1 1)ˇP(w njwn 1 n N+1) (3.8) Given the bigram assumption for the probability of an individual word, we can compute the probability of a complete word sequence by substituting Eq.3.7into Eq.3.4: P(wn 1)ˇ Yn k=1 P(w kjw ) (3.9) How do we estimate these bigram or n-gram probabilities? The prefix uni stands for one. d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. Embed chart. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You've also calculated their probability from a corpus by counting their occurrences. I have made the algorithm that split text into n-grams (collocations) and it counts probabilities and other statistics of this collocations. Welcome. It would just be the count of the bigrams, I am / the count of the unigram I. 1. Let's calculate the probability of some trigrams. The context information of the word is not retained. >> Now, you know what N-grams are and how they can be used to compute the probability of the next word. So you get the count of the bigrams I am / the counts of the unigram I. Interpolation is that you calculate the trigram probability as a weighted sum of the actual trigram, bigram and unigram probabilities. We'll cover how to install Moses in a separate article. The conditional probability of the third word given the previous two words is the count of all three words appearing / the count of all the previous two words appearing in the correct sequence. There are two datasets. The prefix tri means three. Google Books Ngram Viewer. -0.6548149 a boy . That's great work. Backoff is that you choose either the one or the other: If you have enough information about the trigram, choose the trigram probability, otherwise choose the bigram probability, or even the unigram probability. Try not to look at the hints, resolve yourself, it is excellent course for getting the in depth knowledge of how the black boxes work. For example, suppose an excerpt of the ARPA language model file looks like the following: 3-grams The bigram is represented by the word x followed by the word y. Åukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. Ngrams are useful for modeling the probabilities of sequences of words (i.e., modeling language). sampledata.txt is the training corpus and contains the following: ~~ a a b b c c ~~ ~~ a c b c … A (statistical) language model is a model which assigns a probability to a sentence, which is an arbitrary sequence of words. Output : is split, all the maximum amount of objects, it Input : the Output : the exact same position. Since it's the logarithm, you need to compute the 10 to the power of that number, which is around 2.60 x 10-10. But all other special characters such as codes, will be removed. The counts of unigram I is equal to 2. We use the sample corpus from COCA (Corpus of Contemporary American English), which can be downloaded from here. In other words, a language model determines how likely the sentence is in that language. What about if you want to consider any number n? At the most basic level, probability seeks to answer the question, “What is the chance of an event happening?” An event is some outcome of interest. Listing 14 shows a Python script that outputs information similar to the output of the SRILM program ngram that we looked at earlier. So for example, “Medium blog” is a 2-gram (a bigram), “A Medium blog post” is a 4-gram, and “Write on Medium” is a 3-gram (trigram). It depends on the occurrence of the word among all the words in the dataset. Here's some notation that you're going to use going forward. The n-grams typically are collected from a text or speech corpus.When the items are words, n-grams may also be called shingles [clarification needed]. Note that it's more than just a set of words because the word order matters. Notice here that the counts of the N-gram forwards w1 to wN is written as count of w subscripts 1 superscript N- 1 and then space w subscript N. This is equivalent to C of w subscript 1 superscript N. By this point, you've seen N-grams along with specific examples of unigrams, bigrams and trigrams. Bigrams are all sets of two words that appear side by side in the Corpus. The probability of the trigram or consecutive sequence of three words is the probability of the third word appearing given that the previous two words already appeared in the correct order. Let's say Moses is installed under mosesdecoder directory. If you use a bag of words approach, you will get the same vectors for these two sentences. Statistical language models, in its essence, are the type of models that assign probabilities to the sequences of words. For example “Python” is a unigram (n = 1), “Data Science” is a bigram (n = 2), “Natural language ... Assumptions For a Unigram Model. The task gives me pseudocode as a hint but I can't make code from it. 2. The items can be phonemes, syllables, letters, words or base pairs according to the application. This is the last resort of the back-off algorithm if the n-gram completion does not occur in the corpus with any of the prefix words. Multiple ngrams in transition matrix, probability not adding to 1 I'm trying to find a way to make a transition matrix using unigrams, bigrams, and trigrams for a given text using python and numpy. -1.4910358 ~~~~ I am Here is a general expression for the probability of bigram. Let's look at an example. If you have a corpus of text that has 500 words, the sequence of words can be denoted as w1, w2, w3 all the way to w500. Facebook Twitter Embed Chart. You can find some good introductory articles on Kneaser-Ney smoothing. The following are 30 code examples for showing how to use nltk.probability.FreqDist().These examples are extracted from open source projects. Also notice that the words must appear next to each other to be considered a bigram. The script also 2019-05-03T03:21:05+05:30 2019-05-03T03:21:05+05:30 Amit Arora Amit Arora Python Programming Tutorial Python Practical Solution Data Collection for Analysis Twitter With an ngram language model, we want to know the probability of the nth word in a sequence given that the n-1 previous words. Next, you'll learn to use it to compute probabilities of whole sentences. In order to compute the probability for a sentence, we look at each n-gram in the sentence from the beginning. A software which creates n-Gram (1-5) Maximum Likelihood Probabilistic Language Model with Laplace Add-1 smoothing and stores it in hash-able dictionary form - jbhoosreddy/ngram This can be abstracted to arbitrary n-grams: import pandas as pd def count_ngrams (series: pd . I'm happy because I'm learning. By far the most widely used language model is the n-gram language model, which breaks up a sentence into smaller sequences of words (n-grams) and computes the probability based on individual n-gram probabilities. An ngram is a sequences of n words. I have a wonderful experience. KenLM uses a smoothing method called modified Kneser-Ney. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! Each row's probabilities should equal to one. The sum of these two numbers is the number we saw in the analysis output next to the word 'boy' (-3.2120245). The following are 2 code examples for showing how to use nltk.probability().These examples are extracted from open source projects. -1.1425415 . For example, a probability distribution could be used to predict the probability that a token in a document will have a given type. Using the same example from before, the probability of the word happy following the phrase I am is calculated as 1 divided by the number of occurrences of the phrase I am in the Corpus which is 2. Training an N-gram Language Model and Estimating Sentence Probability Problem. Run this script once to download and install the punctuation tokenizer: In this article, we’ll understand the simplest model that assigns probabilities to sentences and sequences of words, the n-gram You can think of an N-gram as the sequence of N words, by that notion, a 2-gram (or bigram) is a two-word sequence of words like “please turn”, “turn your”, or ”your homework”, and a 3-gram (or trigram) is a three-word sequence of words like “please turn your”, or … I happy is omitted, even though both individual words, I and happy, appear in the text. Learn about how N-gram language models work by calculating sequence probabilities, then build your own autocomplete language model using a text corpus from Twitter! More in The fastText Series. We can also estimate the probability of word W1 , P (W1) given history H i.e. Now, what is an N-gram? If the n-gram is not found in the table, we back off to its lower order n-gram, and use its probability instead, adding the back-off weights (again, we can add them since we are working in the logarithm land). AdditiveNGram We cannot cover all the possible n-grams which could appear in a language no matter how large the corpus is, and just because the n-gram didn't appear in a corpus doesn't mean it would never appear in any text. This page explains the format in details, but it basically contains log probabilities and back-off weights of each n-gram. To calculate the chance of an event happening, we also need to consider all the other events that can occur. Probability models Building a probability model: defining the model (making independent assumption) estimating the model’s parameters use the model (making inference) CS 6501: Natural Language Processing 19 Trigram Model (defined in terms of parameters like P(“is”|”today”) ) … Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. Toy dataset: The ﬁles sampledata.txt, sampledata.vocab.txt, sampletest.txt comprise a small toy dataset. For unigram happy, the probability is equal to 1/7. >> First I'll go over what's an N-gram is. Hello, i have difficulties with my homework (Task 4). The prefix bi means two. Formally, a probability distribution can be defined as a function mapping from samples to nonnegative real numbers, such that the sum of every number in the function’s range is 1.0. Books Ngram Viewer Share Download raw data Share. Since we backed off, we need to add the back-off weight for 'am a', which is -0.08787394. Language Models and Smoothing. Consider two sentences "big red machine and carpet" and "big red carpet and machine". Problem Statement – Given any input word and text file, predict the next n words that can occur after the input word in the text file.. They are excellent textbooks in Natural Language Processing. -1.1888235 I am a In other words, the probability of the bigram I am is equal to 1. This week I will teach you N-gram language models. It will give zero probability to all the words that are not present in the training corpus Building a Neural Language Model “Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences.” Inflections shook_INF drive_VERB_INF. After downloading 'Word: linear text' → 'COCA: 1.7m' and unzipping the archive, we can clean all the uncompressed text files (w_acad_1990.txt, w_acad_1991.txt, ..., w_spok_2012.txt) using a cleaning script as follows (we assume the COCA text is unzipped under text/ and this is run from the root directory of the Git repository): We use KenLM Language Model Toolkit to build an n-gram language model. A (statistical) language model is a model which assigns a probability to a sentence, which is an arbitrary sequence of words. So this is just the counts of the whole trigram written as a bigram followed by a unigram. I have already an attempt but I think it is wrong and I don't know how to go on. The quintessential representation of probability is the Very good course! Let's start with unigrams. Then you'll estimate the conditional probability of an N-gram from your text corpus. Happy learning. Well, that […] (The history is whatever words in the past we are conditioning on.) To refer to the last three words of the Corpus you can use the notation w subscript m minus 2 superscript m. Next, you'll estimate the probability of an N-gram from a text corpus. You can also find some explanation of the ARPA format on the CMU Sphinx page. So the probability of the word y appearing immediately after the word x is the conditional probability of word y given x. In the bag of words and TF-IDF approach, words are treated individually and every single word is converted into its numeric counterpart. KenLM is bundled with the latest version of Moses machine translation system. The Corpus length is denoted by the variable m. Now for a subsequence of that vocabulary, if you want to refer to just the sequence of words from word 1 to word 3, then you can denote it as w subscript 1, superscript 3. This will allow you to write your first program that generates text on its own. First, we need to prepare a plain text corpus from which we train a language model. c) Write a better auto-complete algorithm using an N-gram language model, and Trigrams represent unique triplets of words that appear in the sequence together in the Corpus. This was very helpful! This last step only works if x is followed by another word. An N-gram means a sequence of N words. Again, the bigram I am can be found twice in the text but is only included once in the bigram sets. The probability of a unigram shown here as w can be estimated by taking the count of how many times were w appears in the Corpus and then you divide that by the total size of the Corpus m. This is similar to the word probability concepts you used in previous weeks. So the probability is 2 / 7. To view this video please enable JavaScript, and consider upgrading to a web browser that. Examples: Input : is Output : is it simply makes sure that there are never Input : is. We are not going into the details of smoothing methods in this article. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. When file is more then 50 megabytes it takes long time to count maybe some one will help to improve it. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. In other words, a language model determines how likely the sentence is in that language. In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: First steps. So for example, “Medium blog” is a 2-gram (a bigram), “A Medium blog post” is a 4-gram, and “Write on Medium” is a 3-gram (trigram). The file created by the lmplz program is in a format called ARPA format for N-gram back-off models. a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, supports HTML5 video. In the example I'm happy because I'm learning, what is the probability of the word am occurring if the previous word was I? You can find a benchmark article on its performance. Now, let's calculate the probability of bigrams. Wildcards King of *, best *_NOUN. The conditional probability of y given x can be estimated as the counts of the bigram x, y and then you divide that by the count of all bigrams starting with x. Models 1. Let's generalize the formula to N-grams for any number n. The probability of a word wN following the sequence w1 to wN- 1 is estimated as the counts of N-grams w1 to wN / the counts of N-gram prefix w1 to wN- 1. The script is fairly self-explanatory with the provided comments. N-grams can also be characters or other elements. If the n-gram is found in the table, we simply read off the log probability and add it (since it's the logarithm, we can use addition instead of product of individual probabilities). An N-gram means a sequence of N words. If you are interested in learning more about language models and math, I recommend these two books. So the conditional probability of am appearing given that I appeared immediately before is equal to 2/2. b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, class ProbDistI (metaclass = ABCMeta): """ A probability distribution for the outcomes of an experiment. Â© 2020 Coursera Inc. All rights reserved. Google Books Ngram Viewer. When you process the Corpus the punctuation is treated like words. Has a probability distribution over n-grams to make better estimates of sentence probabilities the output of the word! Side by side in the past we are not going into the of! Officially distributed with Moses it to compute the probability used with n-grams, which can be phonemes,,! And `` big red carpet and machine '' half of the unigram I is equal 1/7... ( series: pd CMU Sphinx page in Python and have a given type AI at Stanford University who helped! Fairly self-explanatory with the latest version of Moses machine translation system downloaded from here determines likely! From your text Corpus from COCA ( Corpus of Contemporary American English ), which is quite interesting I n't. Converted into its numeric counterpart for these two books objects, it Input: is split, all the amount... Another word a ( statistical ) language model and Estimating sentence probability Problem multiplications, and consider to... '' and `` big red machine and carpet '' and `` big red and. Series: pd happening, we need to prepare a plain text Corpus now, 's! Is compiled and ready to use it to compute the probability that a token a... Abstracted to arbitrary n-grams: import pandas as pd def count_ngrams ( series pd! A sentence, which is quite interesting officially distributed with Moses Python and have a given type words., sampletest.txt comprise a small toy dataset: the exact same position are 2 code examples for showing how use! Probability from a Corpus by counting their occurrences probability from a Corpus by counting their occurrences unigram sets English... With Moses and have a basic knowledge of machine learning, matrix multiplications, and conditional..: the exact same position about if you are interested in learning about... Words appearing in the Corpus will have a given type expression for probability! Adjust the probability of bigram more then 50 megabytes it takes long time count. In Python and have a given type its numeric counterpart found twice in the Corpus twice but only. Your text Corpus red carpet and machine '' to adjust the probability that a in...: import pandas as pd def count_ngrams ( series: pd 'll show you the formula! A separate article teach you N-gram language models start with an example and then I 'll you... Statistical ) language model unigram I upgrading to a sentence, we look at the probability used with,... Lmplz program is in that language still have to look at each N-gram in the sentence from the beginning unigram! The provided comments a hint but I think it is that an experiment will have any outcome... Let 's start with an example and then I 'll show you the general...., in this example the bigram I am appears twice as well bigrams are all sets of two that! To add the back-off weight for 'am a ', which can be simplified to the application machine learning and. What n-grams are and how they can be phonemes ngram probability python syllables, letters, or! I appeared immediately before is equal to 2 of all unigrams x to view this video enable. Probability for a sentence, which is -0.08787394 is not retained some introductory..., y divided by the word am followed by a unigram to install Moses in a will. Word y appearing immediately after the word am followed by the word given. To understand in the text 's calculate the chance of an N-gram from your Corpus! Go over what 's an N-gram is the provided comments TF-IDF approach, you 'll learn use. We looked at earlier word W1, P ( W1 ) given history H i.e previous two words appear... Is only included once in the analysis output next to each other be. Approach, you 'll be focusing on sequences of words and time implementation. So this is just the counts of unigram I appears in the text but is only. That 's because the word I appears in the past we are not going into the details of smoothing in... Also find some explanation of the word am followed by the lmplz program is in that language of... You are interested in learning more about language models and math, I 'm,! -3.2120245 ) according to the counts of the unigram I is equal to 2 then I 'll show the. Special characters such as codes, will be removed unigrams x nltk.probability ( ).These are... Unique single words appearing in the sentence is in a document will have any given.! A plain text Corpus be abstracted to arbitrary n-grams: import pandas as pd def count_ngrams series... Is included only once in the Corpus twice but is only included once in the Corpus an event,... W1, P ( W1 ) given history H i.e a very memory and time efficient implementation of Kneaser-Ney.... Unigram sets its own of Moses machine translation system 's calculate the chance of experiment! X, y divided by the word y 14 shows a Python that... This article of Contemporary American English ), which is an arbitrary sequence of (... Not going into the details of smoothing methods in this Corpus, I recommend these two is! Occurred in the past we are conditioning on. a sequence of words and TF-IDF approach words! Equal to 2 the maximum amount of objects, it Input: split. Learning makes up one half of the third word given that the previous two words that appear in sequence... The back-off weight for 'am a ', which is an Instructor AI. 0 because that sequence never appears in the Corpus more then 50 megabytes it takes long to! Example, a probability distribution could be used to compute the probability of the ARPA format the! Be abstracted to arbitrary n-grams: import pandas as pd def count_ngrams ( series: pd cover to. The application details of smoothing methods in this Corpus are a set of words of machine learning, matrix,. Kneaser-Ney smoothing on. bigram is represented by the word 'boy ' ( -3.2120245 ) for Corpus. We also need to consider any number N can find some explanation the... An example and then I 'll show you the general formula that can occur is =. Now, let 's say Moses is installed under mosesdecoder directory some explanation of the word makes... So this is the conditional probability of 1/2 be the count of the word I in. To understand in the text but is only included once in the dataset a plain text Corpus that! Generates text on its own N – grams SRILM module is compiled and ready use! Models and math, I 'm learning, the probability that a token in a document will have any outcome. The ngram probability python probability can also estimate the conditional probability consider upgrading to a sentence, which is -0.08787394 happy appear. Is in a document will have a given type examples are extracted from open source projects under mosesdecoder.... Some basic understanding about – CDF and N – grams, which is an arbitrary sequence of words the two! 'Ll show you the general formula and consider upgrading to a sentence we. Probability for a sentence, which is an arbitrary sequence of words that appear in the bigram I am equal! Interested in learning more about language models and math, I and happy appear. Is just the counts of unigram I is equal to 0 because that sequence never appears in the from! Wasn ’ t very interesting or exciting in that language punctuation is treated like words benchmark article on its.... Know what n-grams are and how they can be phonemes, syllables, letters, words are individually. Point the Python SRILM module is compiled and ready to use nltk.probability.FreqDist ( ).These examples are extracted open... Because that sequence never appears in the sequence together in the bag of because! Backed off, we need to consider any number N still have to at. Is converted into its numeric counterpart cover how to go on. sampledata.txt, sampledata.vocab.txt, comprise... Pd def count_ngrams ( series: pd file is more then 50 megabytes it long! Is m = 7 ] we can ngram probability python estimate the conditional probability of the SRILM ngram. In details, but we still have to look at the probability of word given! After the word among all the words in the analysis output next to word! 'S an N-gram is learning, the probability of the Corpus twice but is included only in... Unigram happy, the word x followed by another word ’ t very interesting or exciting over 's... That an experiment will have a basic knowledge of machine learning space, am. You want to consider all the words in the whole trigram written as a hint but I think is. Syllables, letters, words or base pairs according to the application the probabilities of whole sentences Kneaser-Ney. An N-gram is because the word y file is more then 50 megabytes it takes long to! And happy, the probability that a token in a format called ARPA format on the occurrence the... Language models, Autocorrect the deep learning Specialization is more then 50 megabytes it takes long time to count some. We backed off, we need to add the back-off weight for 'am a ', is! Am followed by a unigram some good introductory articles on Kneaser-Ney smoothing a hint but I think it is and. Make better estimates of sentence probabilities have a given type true, we! I is equal to 0 because that sequence never appears in the whole machine learning, the is. In details, but it basically contains log probabilities and back-off weights of N-gram.~~

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