Post on 21-Dec-2015
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nltk_lite tag package
Type of taggers: tag.Default()
tag.Regexp()
tag.Affix()
tag.Unigram()
tag.Bigram()
tag.Trigram()
Actions: tag.tag() tag.tagsents() tag.untag() tag.train() tag.accuracy() tag.tag2tuple() tag.string2words() tag.string2tags()
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Training and Testing ofLearning Algorithms
Algorithms that “learn” from data see a set of examples and try to generalize from them.Training set:
Examples trained on
Test set:Also called held-out data and unseen dataUse this for evaluating your algorithmMust be separate from the training set
– Otherwise, you cheated!
“Gold” standardA test set that a community has agreed on and uses as a common benchmark.
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Cross-Validation of Learning Algorithms
Cross-validation setPart of the training set.
Used for tuning parameters of the algorithm without “polluting” (tuning to) the test data.
You can train on x%, and then cross-validate on the remaining 1-x%
– E.g., train on 90% of the training data, cross-validate (test) on the remaining 10%
– Repeat several times with different splits
This allows you to choose the best settings to then use on the real test set.
– You should only evaluate on the test set at the very end, after you’ve gotten your algorithm as good as possible on the cross-validation set.
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Strong Baselines
When designing NLP algorithms, you need to evaluate them by comparing to others.Baseline Algorithm:
An algorithm that is relatively simple but can be expected to do wellShould get the best score possible by doing the somewhat obvious thing.
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A Tagging Baseline
Find the most likely tag for the most frequent words
Frequent words are ambiguousYou’re likely to see frequent words in any collection
– Will always see “to” but might not see “armadillo”
How to do this?First find the most likely words and their tags in the training dataTrain a tagger that looks up these results in a table
– Note that the tag.Lookup() tagger type is not defined in this version of nltk_lite, so we’ll write our own.
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Subclassing a Python Class
The Lookup module isn’t in our version of nltk_liteLet’s make a subclass of the tag.Unigram class that has this functionality.
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N-GramsThe N stands for how many terms are used
Unigram: 1 term (0th order)Bigram: 2 terms (1st order)Trigrams: 3 terms (2nd order)
– Usually don’t go beyond this
You can use different kinds of terms, e.g.:Character based n-gramsWord-based n-gramsPOS-based n-grams
OrderingOften adjacent, but not required
We use n-grams to help determine the context in which some linguistic phenomenon happens.
E.g., look at the words before and after the period to see if it is the end of a sentence or not.
18Modified from Massio Poesio's lecture
Tagging with lexical frequencies
Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NNPeople/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NNProblem: assign a tag to race given its lexical frequencySolution: we choose the tag that has the greater
P(race|VB)P(race|NN)
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Unigram Tagger
Train on a set of sentencesKeep track of how many times each word is seen with each tag.After training, associate with each word its most likely tag.
Problem: many words never seen in the training data.Solution: have a default tag to “backoff” to.
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What’s wrong with unigram?
Most frequent tag isn’t always right!Need to take the context into account
Which sense of “to” is being used?Which sense of “like” is being used?
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N-gram tagger
Uses the preceding N-1 predicted tagsAlso uses the unigram estimate for the current word
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N-gram taggers in nltk_liteConstructs a frequency distribution describing the frequencies each word is tagged with in different contexts.
The context considered consists of the word to be tagged and the n-1 previous words' tags.
After training, tag words by assigning each word the tag with the maximum frequency given its context.
Assigns “None” tag if it sees a word in a context for which it has no data (which it has not seen).
Tuning parameters“cutoff” is the minimal number of times that the context must have been seen in training in order to be incorporated into the statisticsDefault cutoff is 1
24Modified from Diane Litman's version of Steve Bird's notes
Bigram Tagging
For tagging, in addition to considering the token’s type, the context also considers the tags of the n preceding tokens
– What is the most likely tag for word n, given word n-1 and tag n-1?
The tagger picks the tag which is most likely for that context.
26Modified from Diane Litman's version of Steve Bird's notes
Combining Taggers using Backoff
Use more accurate algorithms when we can, backoff to wider coverage when needed.
Try tagging the token with the 1st order tagger. If the 1st order tagger is unable to find a tag for the token, try finding a tag with the 0th order tagger. If the 0th order tagger is also unable to find a tag, use the default tagger to find a tag.
Important point:Bigram and trigram taggers use the previous tag context to assign new tags. If they see a tag of “None” in the previous context, they will output “None” too.
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Demonstrating the n-gram taggers
Trained on brown.tagged(‘a’), tested on brown.tagged(‘b’)Backs off to a default of ‘nn’
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Combining Taggers
The bigram backoff tagger did worse than the unigram! Why?Why does it get better again with trigrams?How can we improve these scores?
30Modified from Diane Litman's version of Steve Bird's notes
Rule-Based Tagger
The Linguistic ComplaintWhere is the linguistic knowledge of a tagger?Just a massive table of numbersAren’t there any linguistic insights that could emerge from the data?Could thus use handcrafted sets of rules to tag input sentences, for example, if input follows a determiner tag it as a noun.
31Slide modified from Massimo Poesio's
The Brill tagger
An example of Transformation-Based LearningBasic idea: do a quick job first (using frequency), then revise it using contextual rules.Painting metaphor from the readings
Very popular (freely available, works fairly well)A supervised method: requires a tagged corpus
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Brill Tagging: In more detail
Start with simple (less accurate) rules…learn better ones from tagged corpus
Tag each word initially with most likely POSExamine set of transformations to see which improves tagging decisions compared to tagged corpus Re-tag corpus using best transformationRepeat until, e.g., performance doesn’t improveResult: tagging procedure (ordered list of transformations) which can be applied to new, untagged text
33Slide modified from Massimo Poesio's
An exampleExamples:
They are expected to race tomorrow.The race for outer space.
Tagging algorithm:1. Tag all uses of “race” as NN (most likely tag in the
Brown corpus)• They are expected to race/NN tomorrow• the race/NN for outer space
2. Use a transformation rule to replace the tag NN with VB for all uses of “race” preceded by the tag TO:• They are expected to race/VB tomorrow• the race/NN for outer space
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Error Analysis
To improve your algorithm, examine where it fails on the cross-validation set
It’s often useful to characterize in detail which examples it fails on and which succeed.
Make fixes, and then re-train on the training set, again using cross-validation