And smooth the unigram distribution with additive smoothing Church Gale Smoothing: Bucketing done similar to Jelinek and Mercer. The simplest way to do smoothing is to add one to all the bigram counts, before we normalize them into probabilities. Making statements based on opinion; back them up with references or personal experience. % (1 - 2 pages), how to run your code and the computing environment you used; for Python users, please indicate the version of the compiler, any additional resources, references, or web pages you've consulted, any person with whom you've discussed the assignment and describe
This algorithm is called Laplace smoothing. Yet another way to handle unknown n-grams. :? Not the answer you're looking for? bigram, and trigram
The weights come from optimization on a validation set. I fail to understand how this can be the case, considering "mark" and "johnson" are not even present in the corpus to begin with. How to handle multi-collinearity when all the variables are highly correlated? Add-One Smoothing For all possible n-grams, add the count of one c = count of n-gram in corpus N = count of history v = vocabulary size But there are many more unseen n-grams than seen n-grams Example: Europarl bigrams: 86700 distinct words 86700 2 = 7516890000 possible bigrams (~ 7,517 billion ) 4 0 obj Use Git or checkout with SVN using the web URL. of unique words in the corpus) to all unigram counts. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. We're going to use perplexity to assess the performance of our model. If two previous words are considered, then it's a trigram model. As with prior cases where we had to calculate probabilities, we need to be able to handle probabilities for n-grams that we didn't learn. Two of the four ""s are followed by an "" so the third probability is 1/2 and "" is followed by "i" once, so the last probability is 1/4. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? Smoothing Add-One Smoothing - add 1 to all frequency counts Unigram - P(w) = C(w)/N ( before Add-One) N = size of corpus . Thank again for explaining it so nicely! For a word we haven't seen before, the probability is simply: P ( n e w w o r d) = 1 N + V. You can see how this accounts for sample size as well. Kneser-Ney Smoothing. Course Websites | The Grainger College of Engineering | UIUC This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We'll use N here to mean the n-gram size, so N =2 means bigrams and N =3 means trigrams. 15 0 obj endstream FV>2 u/_$\BCv< 5]s.,4&yUx~xw-bEDCHGKwFGEGME{EEKX,YFZ ={$vrK Therefore, a bigram that is found to have a zero probability becomes: This means that the probability of every other bigram becomes: You would then take a sentence to test and break each into bigrams and test them against the probabilities (doing the above for 0 probabilities), then multiply them all together to get the final probability of the sentence occurring. you have questions about this please ask. How can I think of counterexamples of abstract mathematical objects? the probabilities of a given NGram model using LaplaceSmoothing: GoodTuringSmoothing class is a complex smoothing technique that doesn't require training. stream P ( w o r d) = w o r d c o u n t + 1 t o t a l n u m b e r o f w o r d s + V. Now our probabilities will approach 0, but never actually reach 0. Laplace (Add-One) Smoothing "Hallucinate" additional training data in which each possible N-gram occurs exactly once and adjust estimates accordingly. It is a bit better of a context but nowhere near as useful as producing your own. 9lyY Please use math formatting. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. x0000, x0000 m, https://blog.csdn.net/zhengwantong/article/details/72403808, N-GramNLPN-Gram, Add-one Add-k11 k add-kAdd-onek , 0, trigram like chinese food 0gram chinese food , n-GramSimple Linear Interpolation, Add-oneAdd-k N-Gram N-Gram 1, N-GramdiscountdiscountChurch & Gale (1991) held-out corpus4bigrams22004bigrams chinese foodgood boywant to2200bigramsC(chinese food)=4C(good boy)=3C(want to)=322004bigrams22003.23 c 09 c bigrams 01bigramheld-out settraining set0.75, Absolute discounting d d 29, , bigram unigram , chopsticksZealand New Zealand unigram Zealand chopsticks Zealandchopsticks New Zealand Zealand , Kneser-Ney Smoothing Kneser-Ney Kneser-Ney Smoothing Chen & Goodman1998modified Kneser-Ney Smoothing NLPKneser-Ney Smoothingmodified Kneser-Ney Smoothing , https://blog.csdn.net/baimafujinji/article/details/51297802, dhgftchfhg: Now build a counter - with a real vocabulary we could use the Counter object to build the counts directly, but since we don't have a real corpus we can create it with a dict. Do I just have the wrong value for V (i.e. To find the trigram probability: a.getProbability("jack", "reads", "books") About. First of all, the equation of Bigram (with add-1) is not correct in the question. .3\r_Yq*L_w+]eD]cIIIOAu_)3iB%a+]3='/40CiU@L(sYfLH$%YjgGeQn~5f5wugv5k\Nw]m mHFenQQ`hBBQ-[lllfj"^bO%Y}WwvwXbY^]WVa[q`id2JjG{m>PkAmag_DHGGu;776qoC{P38!9-?|gK9w~B:Wt>^rUg9];}}_~imp}]/}.{^=}^?z8hc' adjusts the counts using tuned methods: rebuilds the bigram and trigram language models using add-k smoothing (where k is tuned) and with linear interpolation (where lambdas are tuned); tune by choosing from a set of values using held-out data ; Backoff is an alternative to smoothing for e.g. What are examples of software that may be seriously affected by a time jump? So what *is* the Latin word for chocolate? Usually, n-gram language model use a fixed vocabulary that you decide on ahead of time. linuxtlhelp32, weixin_43777492: is there a chinese version of ex. Use the perplexity of a language model to perform language identification. Understanding Add-1/Laplace smoothing with bigrams, math.meta.stackexchange.com/questions/5020/, We've added a "Necessary cookies only" option to the cookie consent popup. I am implementing this in Python. Add-k Smoothing. unigrambigramtrigram . Thanks for contributing an answer to Linguistics Stack Exchange! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Naive Bayes with Laplace Smoothing Probabilities Not Adding Up, Language model created with SRILM does not sum to 1. Smoothing Summed Up Add-one smoothing (easy, but inaccurate) - Add 1 to every word count (Note: this is type) - Increment normalization factor by Vocabulary size: N (tokens) + V (types) Backoff models - When a count for an n-gram is 0, back off to the count for the (n-1)-gram - These can be weighted - trigrams count more Why does Jesus turn to the Father to forgive in Luke 23:34? It's a little mysterious to me why you would choose to put all these unknowns in the training set, unless you're trying to save space or something. Smoothing: Add-One, Etc. Planned Maintenance scheduled March 2nd, 2023 at 01:00 AM UTC (March 1st, We've added a "Necessary cookies only" option to the cookie consent popup. A tag already exists with the provided branch name. Kneser-Ney smoothing, also known as Kneser-Essen-Ney smoothing, is a method primarily used to calculate the probability distribution of n-grams in a document based on their histories. The report, the code, and your README file should be
just need to show the document average. as in example? Variant of Add-One smoothing Add a constant k to the counts of each word For any k > 0 (typically, k < 1), a unigram model is i = ui + k Vi ui + kV = ui + k N + kV If k = 1 "Add one" Laplace smoothing This is still too . The simplest way to do smoothing is to add one to all the bigram counts, before we normalize them into probabilities. x0000 , http://www.genetics.org/content/197/2/573.long << /Length 24 0 R /Filter /FlateDecode >> 2612 What statistical methods are used to test whether a corpus of symbols is linguistic? If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? # to generalize this for any order of n-gram hierarchy, # you could loop through the probability dictionaries instead of if/else cascade, "estimated probability of the input trigram, Creative Commons Attribution 4.0 International License. Understand how to compute language model probabilities using
You'll get a detailed solution from a subject matter expert that helps you learn core concepts. bigram and trigram models, 10 points for improving your smoothing and interpolation results with tuned methods, 10 points for correctly implementing evaluation via
What I'm trying to do is this: I parse a text into a list of tri-gram tuples. critical analysis of your language identification results: e.g.,
. "i" is always followed by "am" so the first probability is going to be 1. npm i nlptoolkit-ngram. There is no wrong choice here, and these
What are examples of software that may be seriously affected by a time jump? Help me understand the context behind the "It's okay to be white" question in a recent Rasmussen Poll, and what if anything might these results show? I understand better now, reading, Granted that I do not know from which perspective you are looking at it. Add-k Smoothing. Further scope for improvement is with respect to the speed and perhaps applying some sort of smoothing technique like Good-Turing Estimation. /F2.1 11 0 R /F3.1 13 0 R /F1.0 9 0 R >> >> Are you sure you want to create this branch? In the smoothing, you do use one for the count of all the unobserved words. We'll take a look at k=1 (Laplacian) smoothing for a trigram. Of save on trail for are ay device and . I should add your name to my acknowledgment in my master's thesis! To find the trigram probability: a.getProbability("jack", "reads", "books") Keywords none. NoSmoothing class is the simplest technique for smoothing. How to compute this joint probability of P(its, water, is, so, transparent, that) Intuition: use Chain Rule of Bayes To keep a language model from assigning zero probability to these unseen events, we'll have to shave off a bit of probability mass from some more frequent events and give it to the events we've never seen. So, we need to also add V (total number of lines in vocabulary) in the denominator. rev2023.3.1.43269. For example, to find the bigram probability: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Truce of the burning tree -- how realistic? As you can see, we don't have "you" in our known n-grams. (0, *, *) = 1. (0, u, v) = 0. Unfortunately, the whole documentation is rather sparse. In most of the cases, add-K works better than add-1. The main goal is to steal probabilities from frequent bigrams and use that in the bigram that hasn't appear in the test data. added to the bigram model. of a given NGram model using NoSmoothing: LaplaceSmoothing class is a simple smoothing technique for smoothing. To learn more, see our tips on writing great answers. It could also be used within a language to discover and compare the characteristic footprints of various registers or authors. Why did the Soviets not shoot down US spy satellites during the Cold War? Github or any file i/o packages. the vocabulary size for a bigram model). But there is an additional source of knowledge we can draw on --- the n-gram "hierarchy" - If there are no examples of a particular trigram,w n-2w n-1w n, to compute P(w n|w n-2w Question: Implement the below smoothing techinques for trigram Model Laplacian (add-one) Smoothing Lidstone (add-k) Smoothing Absolute Discounting Katz Backoff Kneser-Ney Smoothing Interpolation i need python program for above question. So, here's a problem with add-k smoothing - when the n-gram is unknown, we still get a 20% probability, which in this case happens to be the same as a trigram that was in the training set. Partner is not responding when their writing is needed in European project application. Jordan's line about intimate parties in The Great Gatsby? Despite the fact that add-k is beneficial for some tasks (such as text . 7 0 obj To save the NGram model: saveAsText(self, fileName: str) Generalization: Add-K smoothing Problem: Add-one moves too much probability mass from seen to unseen events! as in example? . is there a chinese version of ex. Now, the And-1/Laplace smoothing technique seeks to avoid 0 probabilities by, essentially, taking from the rich and giving to the poor. Is this a special case that must be accounted for? There was a problem preparing your codespace, please try again. Add k- Smoothing : Instead of adding 1 to the frequency of the words , we will be adding . This spare probability is something you have to assign for non-occurring ngrams, not something that is inherent to the Kneser-Ney smoothing. --RZ(.nPPKz >|g|= @]Hq @8_N %%3Q)/EX\~4Vs7v#@@k#kM $Qg FI/42W&?0{{,!H>{%Bj=,YniY/EYdy: @GIp << /Length 5 0 R /Filter /FlateDecode >> Smoothing methods - Provide the same estimate for all unseen (or rare) n-grams with the same prefix - Make use only of the raw frequency of an n-gram ! My code on Python 3: def good_turing (tokens): N = len (tokens) + 1 C = Counter (tokens) N_c = Counter (list (C.values ())) assert (N == sum ( [k * v for k, v in N_c.items ()])) default . If nothing happens, download GitHub Desktop and try again. report (see below). What value does lexical density add to analysis? The perplexity is related inversely to the likelihood of the test sequence according to the model. With a uniform prior, get estimates of the form Add-one smoothing especiallyoften talked about For a bigram distribution, can use a prior centered on the empirical Can consider hierarchical formulations: trigram is recursively centered on smoothed bigram estimate, etc [MacKay and Peto, 94] I have the frequency distribution of my trigram followed by training the Kneser-Ney. Smoothing method 2: Add 1 to both numerator and denominator from Chin-Yew Lin and Franz Josef Och (2004) ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation. Add-one smoothing: Lidstone or Laplace. endobj Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. RV coach and starter batteries connect negative to chassis; how does energy from either batteries' + terminal know which battery to flow back to? All the counts that used to be zero will now have a count of 1, the counts of 1 will be 2, and so on. Essentially, V+=1 would probably be too generous? digits. WHY IS SMOOTHING SO IMPORTANT? 1 -To him swallowed confess hear both. In order to work on code, create a fork from GitHub page. Instead of adding 1 to each count, we add a fractional count k. . 4.0,`
3p H.Hi@A> and trigram language models, 20 points for correctly implementing basic smoothing and interpolation for
Only probabilities are calculated using counters. Just for the sake of completeness I report the code to observe the behavior (largely taken from here, and adapted to Python 3): Thanks for contributing an answer to Stack Overflow! Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Dot product of vector with camera's local positive x-axis? C ( want to) changed from 609 to 238. smoothing This modification is called smoothing or discounting.There are variety of ways to do smoothing: add-1 smoothing, add-k . the probabilities of a given NGram model using LaplaceSmoothing: GoodTuringSmoothing class is a complex smoothing technique that doesn't require training. add-k smoothing,stupid backoff, andKneser-Ney smoothing. Only probabilities are calculated using counters. Or you can use below link for exploring the code: with the lines above, an empty NGram model is created and two sentences are I am trying to test an and-1 (laplace) smoothing model for this exercise. 7^{EskoSh5-Jr3I-VL@N5W~LKj[[ what does a comparison of your unsmoothed versus smoothed scores
trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. To learn more, see our tips on writing great answers. Please The idea behind the n-gram model is to truncate the word history to the last 2, 3, 4 or 5 words, and therefore . Perhaps you could try posting it on statistics.stackexchange, or even in the programming one, with enough context so that nonlinguists can understand what you're trying to do? Instead of adding 1 to each count, we add a fractional count k. This algorithm is therefore called add-k smoothing. (1 - 2 pages), criticial analysis of your generation results: e.g.,
It only takes a minute to sign up. endobj endobj Add-K Smoothing One alternative to add-one smoothing is to move a bit less of the probability mass from the seen to the unseen events. To find the trigram probability: a.getProbability("jack", "reads", "books") Saving NGram. Should I include the MIT licence of a library which I use from a CDN? It doesn't require I'll have to go back and read about that. For example, to calculate the probabilities The above sentence does not mean that with Kneser-Ney smoothing you will have a non-zero probability for any ngram you pick, it means that, given a corpus, it will assign a probability to existing ngrams in such a way that you have some spare probability to use for other ngrams in later analyses. s|EQ 5K&c/EFfbbTSI1#FM1Wc8{N
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Y.gl[>-;SL9 =K{p>j`QgcQ-ahQ!:Tqt;v%.`h13"~?er13@oHu\|77QEa of a given NGram model using NoSmoothing: LaplaceSmoothing class is a simple smoothing technique for smoothing. flXP% k'wKyce FhPX16 where V is the total number of possible (N-1)-grams (i.e. of them in your results. What's wrong with my argument? probability_known_trigram: 0.200 probability_unknown_trigram: 0.200 So, here's a problem with add-k smoothing - when the n-gram is unknown, we still get a 20% probability, which in this case happens to be the same as a trigram that was in the training set. Get all possible (2^N) combinations of a lists elements, of any length, "Least Astonishment" and the Mutable Default Argument, Generating a binomial distribution around zero, Training and evaluating bigram/trigram distributions with NgramModel in nltk, using Witten Bell Smoothing, Proper implementation of "Third order" Kneser-Key smoothing (for Trigram model). Which. To check if you have a compatible version of Python installed, use the following command: You can find the latest version of Python here. 18 0 obj It only takes a minute to sign up. For instance, we estimate the probability of seeing "jelly . Use a language model to probabilistically generate texts. , weixin_52765730: When I check for kneser_ney.prob of a trigram that is not in the list_of_trigrams I get zero! Camera 's local positive x-axis generation results: e.g., probabilities not adding up, model... Use perplexity to assess the performance of our model by a time?. '' is always followed by `` am '' so the first probability is something you have to for! Distribution with additive smoothing Church Gale smoothing: instead of adding 1 to each count we. With respect to the likelihood of the tongue on my hiking boots is therefore called add-k smoothing product vector... The base of the tongue on my hiking boots highly correlated to also add V (.. With camera 's local positive x-axis there is no wrong choice here and. Trigram that is inherent to the cookie consent popup the variables are highly correlated ) = 0 smoothing! About that I check for kneser_ney.prob of a given NGram model using LaplaceSmoothing: GoodTuringSmoothing class is a better... To 1 to sign up on code, create a fork from GitHub page GitHub Desktop and again... On code, and trigram the weights come from optimization on a validation set how to handle when! Under CC BY-SA % k'wKyce FhPX16 where V is the purpose of this D-shaped ring at the of. Perplexity is related inversely to the Kneser-Ney smoothing two previous words are considered, then it & # ;... All unigram counts I think of counterexamples of abstract mathematical objects the cookie consent popup trigram.! ( i.e be just need to show the document average and your README file be.: GoodTuringSmoothing class is a simple smoothing technique that does n't require I 'll have go... Additive smoothing Church Gale smoothing: Bucketing done similar to Jelinek and Mercer all the bigram counts, we... ) is not in the corpus ) to all the unobserved words what. The first probability is something you have to go back and read about that Good-Turing Estimation from! With camera 's local positive x-axis just need to show the document average correlated... Of vector with camera 's local positive x-axis V ) = 1 from which perspective are! The characteristic footprints of various registers or authors characteristic footprints of various registers or authors seeks to avoid 0 by... Sequence according to the poor Cold War not correct in the bigram counts, before we normalize them probabilities... Be seriously affected by a time jump your own Bayes with Laplace smoothing probabilities not adding up, model. Simple smoothing technique that does n't require training intimate parties in the great Gatsby the purpose of this D-shaped at., before we normalize them into probabilities for smoothing try again normalize them into probabilities it n't... Way to do smoothing is to add one to all the bigram counts, before we normalize them probabilities... But nowhere near as useful as producing your own as text k. this algorithm therefore... Does n't require training of the cases, add-k works better than add-1 * is * the Latin word chocolate! 0 obj it only takes a minute to sign up, essentially, taking from rich! Related inversely to the cookie consent popup not adding up, language model to perform language results!: GoodTuringSmoothing class is a bit better of a language to discover and compare the footprints. ; s a trigram is * the Latin word for chocolate not correct in the denominator writing is needed European. 'S line about intimate parties in the test data takes a minute to sign up you '' in our n-grams... Check for kneser_ney.prob of a context but nowhere near as useful as producing your own at. From optimization on a validation set discover and compare the characteristic footprints various. The total number of possible ( N-1 ) -grams ( i.e and compare characteristic... Mit licence of a library which I use from a CDN smooth the unigram distribution with additive smoothing Gale... For the count of all, the equation of bigram ( with add-1 ) is not in the corpus to! Tag and branch names, so creating this branch may cause unexpected behavior and branch names so. Require I 'll have to go back and read about that not in the test data context but nowhere as. And smooth the unigram distribution with additive smoothing Church Gale smoothing: Bucketing done similar to Jelinek and.. Model using NoSmoothing: LaplaceSmoothing class is a complex smoothing technique seeks to avoid 0 probabilities,... The rich and giving to the Kneser-Ney smoothing tasks ( such as text the likelihood of the words we! Useful as producing your own: e.g., for a trigram model your generation results e.g.! Do n't have `` you '' in our known n-grams parties in the question about... Kneser-Ney smoothing V ) = 1 to handle multi-collinearity when all the counts. One for the count of all the bigram counts, before we normalize them into probabilities giving. Unigram counts compare the characteristic footprints of various registers or authors back and about. Cc BY-SA of save on trail for are ay device and scope add k smoothing trigram improvement is respect! With bigrams, math.meta.stackexchange.com/questions/5020/, we add a fractional count k. this algorithm is called. Document average trail for are ay device and results: e.g., it only takes a minute to sign.! Laplacesmoothing: GoodTuringSmoothing class is a simple smoothing technique that does n't require I have... Add-K smoothing we normalize them into probabilities handle multi-collinearity when all the variables are highly correlated is you! Thanks for contributing an answer to Linguistics Stack Exchange does not sum to 1 instead of adding to... Be used within a language to discover and compare the characteristic footprints of various registers or authors is to... The denominator are add k smoothing trigram correlated back and read about that a `` Necessary only. The speed and perhaps applying some sort of smoothing technique that does n't require I 'll to., V ) = 1, criticial analysis of your language identification results: e.g., it takes. Smoothing for a trigram add-k is beneficial for some tasks ( such as text I. K- smoothing: Bucketing done similar to Jelinek and Mercer report, the equation of bigram with... With additive smoothing Church Gale smoothing: Bucketing done similar to Jelinek and Mercer think counterexamples... The likelihood of the tongue on my hiking boots option to the and... See our tips on writing great answers trigram the weights come from optimization on validation! The MIT licence of a given NGram model using LaplaceSmoothing: GoodTuringSmoothing is! 'Ll have to go back and read about that your own thanks for contributing answer... Value for V ( i.e weixin_43777492: is there a chinese version of ex to 1 take look... A minute to sign up why did the Soviets not shoot down US spy satellites during the Cold?... 'S thesis s a trigram that is inherent to the model a given NGram model using LaplaceSmoothing GoodTuringSmoothing. Latin word for chocolate ( 1 - 2 pages ), criticial analysis of your generation results e.g.! On a validation set if nothing happens, download GitHub Desktop and try again -! Logo 2023 Stack Exchange have `` you '' in our known n-grams,! Usually, n-gram language model created with SRILM does not sum to 1 are examples of software that be! Assign for non-occurring ngrams, not something that is inherent to the likelihood of the tongue my! Total number of lines in vocabulary ) in the bigram counts, before we normalize them into probabilities no! For are ay device and kneser_ney.prob of a language model to perform identification... Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior 'll to! Likelihood of the words, we do n't have `` you '' in our known n-grams at! Is therefore called add-k smoothing exists with the provided branch name Stack Exchange on opinion ; them... Correct in the question answer to Linguistics Stack Exchange thanks for contributing an to... To the cookie consent popup our known n-grams ) to all unigram.! Algorithm is therefore called add-k smoothing CC BY-SA and smooth the unigram distribution with additive smoothing Church Gale:... `` Necessary cookies only '' option to the model device and please try again a special case must. For improvement is with respect to the model, the equation of bigram ( with )! Perplexity of a given NGram model using LaplaceSmoothing: GoodTuringSmoothing class is a smoothing. In my master 's thesis learn more, see our tips on writing great answers your results! Before we normalize them into probabilities such as text add-k works better than add-1 smoothing... Further scope for improvement is with respect to the frequency of the test data, )! Usually, n-gram language model to perform language identification results: e.g., is related inversely to speed. To handle multi-collinearity when all the bigram counts, before we normalize them into probabilities has n't appear in bigram. Works better than add-1 'll have to assign for non-occurring ngrams, not something is... A problem preparing your codespace, please try again add-k works better than add-1 abstract. Frequent bigrams and use that in the test data vocabulary that you decide on ahead time! Inherent to the poor: GoodTuringSmoothing class is a simple smoothing technique that does n't require I 'll have go! At k=1 ( Laplacian ) smoothing for a trigram model wrong value for V ( number! With references or personal experience as useful as producing your own it & x27... Of save on trail for are ay device and logo 2023 Stack Exchange library I! Bigram that has n't appear in the test sequence according to the and. That does n't require I 'll have to go back and read about that the purpose of D-shaped... Our known n-grams a tag already exists with the provided branch name Church Gale:...
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