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Language modelling using N-Grams
Corpora and Statistical Methods
Lecture 7
In this lecture
We consider one of the basic tasks in Statistical NLP:
language models are probabilistic representations of allowable
sequences
This part:
methodological overview
fundamental statistical estimation models
Next part:
smoothing techniques
Assumptions, definitions, methodology, algorithms
Part 1
Example task The word-prediction task (Shannon game)
Given: a sequence of words (the history)
a choice of next word
Predict: the most likely next word
Generalises easily to other problems, such as predicting the POS of unknowns based on history.
Applications of the Shannon game
Automatic speech recognition (cf. tutorial 1):
given a sequence of possible words, estimate its probability
Context-sensitive spelling correction:
Many spelling errors are real words He walked for miles in the dessert. (resp. desert)
Identifying such errors requires a global estimate of the probability of a sentence.
Applications of N-gram models generally
POS Tagging (cf. lecture 3):
predict the POS of an unknown word by looking at its history
Statistical parsing:
e.g. predict the group of words that together form a phrase
Statistical NL Generation:
given a semantic form to be realised as text, and several possible realisations, select the most probable one.
A real-world example: Google’s did you
mean
Google uses an n-gram
model (based on sequences
of characters, not words).
In this case, the sequence
apple desserts is much more
probable than apple deserts
How it works Documents provided by the search engine are added to:
An index (for fast retrieval)
A language model (based on probability of a sequence of characters)
A submitted query (“apple deserts”) can be modified (using character insertions, deletions, substitutions and transpositions) to yield a query that fits the language model better (“apple desserts”).
Outcome is a context-sensitive spelling correction:
“apple deserts” “apple desserts”
“frod baggins” “frodo baggins”
“frod” “ford”
The noisy channel model
After Jurafsky and Martin (2009), Speech and Language
Processing (2nd Ed). Prentice Hall p. 198
The assumptions behind n-gram models
The Markov Assumption
Markov models:
probabilistic models which predict the likelihood of a future unit
based on limited history
in language modelling, this pans out as the local history assumption:
the probability of wn depends on a limited number of prior words
utility of the assumption:
we can rely on a small n for our n-gram models (bigram, trigram)
long n-grams become exceedingly sparse
Probabilities become very small with long sequences
The structure of an n-gram model
The task can be re-stated in conditional probabilistic terms:
Limiting n under the Markov Assumption means:
greater chance of finding more than one occurrence of the sequence w1…wn-1
more robust statistical estimations
N-grams are essentially equivalence classes or bins
every unique n-gram is a type or bin
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Structure of n-gram models (II)
If we construct a model where all histories with the same n-1
words are considered one class or bin, we have an (n-1)th
order Markov Model
Note terminology:
n-gram model = (n-1)th order Markov Model
Methodological considerations We are often concerned with: building an n-gram model evaluating it
We therefore make a distinction between training and testdata You never test on your training data If you do, you’re bound to get good results. N-gram models tend to be overtrained, i.e.: if you train on a corpus
C, your model will be biased towards expecting the kinds of events in C. Another term for this: overfitting
Dividing the data
Given: a corpus of n units (words, sentences, … depending
on the task)
A large proportion of the corpus is reserved for training.
A smaller proportion for testing/evaluation (normally 5-10%)
Held-out (validation) data
Held-out estimation:
during training, we sometimes estimate parameters for our model
empirically
commonly used in smoothing (how much probability space do we
want to set aside for unseen data)?
therefore, the training set is often split further into training data and
validation data
normally, held-out data is 10% of the size of the training data
Development data A common approach:
1. train an algorithm on training data
a. (estimate further parameters on held-out data if required)
2. evaluate it
3. re-tune it
4. go back to Step 1 until no further finetuning necessary
5. Carry out final evaluation
For this purpose, it’s useful to have:
training data for step 1
development set for steps 2-4
final test set for step 5
Significance testing Often, we compare the performance of our algorithm against some
baseline.
A single, raw performance score won’t tell us much. We need to test for significance (e.g. using t-test).
Typical method:
Split test set into several small test sets, e.g. 20 samples
evaluation carried out separately on each
mean and variance estimated based on 20 different samples
test for significant difference between algorithm and a predefined baseline
Size of n-gram models
In a corpus of vocabulary size N, the assumption is that any
combination of n words is a potential n-gram.
For a bigram model: N2 possible n-grams in principle
For a trigram model: N3 possible n-grams.
…
Size (continued)
Each n-gram in our model is a parameter used to estimate
probability of the next possible word.
too many parameters make the model unwieldy
too many parameters lead to data sparseness: most of them will have f
= 0 or 1
Most models stick to unigrams, bigrams or trigrams.
estimation can also combine different order models
Further considerations When building a model, we tend to take into account the
start-of-sentence symbol: the girl swallowed a large green caterpillar
<s> the
the girl
…
Also typical to map all tokens w such that count(w) < k to <UNK>: usually, tokens with frequency 1 or 2 are just considered “unknown”
or “unseen”
this reduces the parameter space
Building models using Maximum Likelihood
Estimation
Maximum Likelihood Estimation Approach
Basic equation:
In a unigram model, this reduces to simple probability.
MLE models estimate probability using relative frequency.
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Limitations of MLE
MLE builds the model that maximises the probability of the
training data.
Unseen events in the training data are assigned zero
probability.
Since n-gram models tend to be sparse, this is a real problem.
Consequences:
seen events are given more probability mass than they have
unseen events are given zero mass
Seen/unseen
A A’
Probability mass of events in training data
Probability massof events not in training data
The problem with MLE is that it distributes A’ among members of A.
The solution
Solution is to correct MLE estimation using a smoothing
technique.
More on this in the next part
But cf. Tutorial 1, which introduced the simplest method of
smoothing known.
Adequacy of different order models Manning/Schutze `99 report results for n-gram models of a corpus of the
novels of Austen.
Task: use n-gram model to predict the probability of a sentence in the test data.
Models:
unigram: essentially zero-context markov model, uses only the probability of individual words
bigram
trigram
4-gram
Example test case
•Training Corpus: five Jane Austen novels
• Corpus size = 617,091 words
•Vocabulary size = 14,585 unique types
•Task: predict the next word of the trigram
“inferior to ________”
from test data, Persuasion:
“[In person, she was] inferior to both [sisters.]”
Selecting an n
Vocabulary (V) = 20,000 words
n Number of bins
(i.e. no. of possible unique n-grams)
2 (bigrams) 400,000,000
3 (trigrams) 8,000,000,000,000
4 (4-grams) 1.6 x 1017
Adequacy of unigrams
Problems with unigram models:
not entirely hopeless because most sentences contain a majority
of highly common words
ignores syntax completely:
P(In person she was inferior) = P(inferior was she person in)
Adequacy of bigrams
Bigrams:
improve situation dramatically
some unexpected results:
p(she|person) decreases compared to the unigram model. Though she is
very common, it is uncommon after person
Adequacy of trigrams
Trigram models will do brilliantly when they’re useful.
They capture a surprising amount of contextual variation in
text.
Biggest limitation:
most new trigrams in test data will not have been seen in training data.
Problem carries over to 4-grams, and is much worse!
Reliability vs. Discrimination
larger n: more information about the context of the specific
instance (greater discrimination)
smaller n: more instances in training data, better statistical
estimates (more reliability)
Backing off
Possible way of striking a balance between reliability and
discrimination:
backoff model:
where possible, use a trigram
if trigram is unseen, try and “back off ” to a bigram model
if bigrams are unseen, try and “back off ” to a unigram
Evaluating language models
Perplexity
Recall: Entropy is a measure of uncertainty:
high entropy = high uncertainty
perplexity:
if I’ve trained on a sample, how surprised am I when exposed to a
new sample?
a measure of uncertainty of a model on new data
Entropy as “expected value”
One way to think of the summation part is as a weighted average of the information content.
We can view this average value as an “expectation”: the expected surprise/uncertainty of our model.
Xx
xpxpXH )(log)()(
Comparing distributions We have a language model built from a sample. The sample is
a probability distribution q over n-grams.
q(x) = the probability of some n-gram x in our model.
The sample is generated from a true population (“the language”) with probability distribution p.
p(x) = the probability of x in the true distribution
Evaluating a language model
We’d like an estimate of how good our model is as a model of
the language
i.e. we’d like to compare q to p
We don’t have access to p. (Hence, can’t use KL-Divergence)
Instead, we use our test data as an estimate of p.
Cross-entropy: basic intuition
Measure the number of bits needed to identify an event
coming from p, if we code it according to q:
We draw sequences according to p;
but we sum the log of their probability according to q.
This estimate is called cross-entropy H(p,q)
Cross-entropy: p vs. q Cross-entropy is an upper bound on the entropy of the true
distribution p:
H(p) ≤ H(p,q)
if our model distribution (q) is good, H(p,q) ≈ H(p)
We estimate cross-entropy based on our test data.
Gives an estimate of the distance of our language model from the distribution in the test sample.
Estimating cross-entropy
x
xqxpqpH )(log)(),(
Probability accordingto p (test set)
Entropy accordingto q (language model)
Perplexity
The perplexity of a language model with probability distribution q, relative to a test set with probability distribution p is:
A perplexity value of k (obtained on a test set) tells us: our model is as surprised on average as it would be if it had to make k guesses for every
sequence (n-gram) in the test data.
The lower the perplexity, the better the language model (the lower the surprise on our test data).
),(2 qpH
Perplexity example (Jurafsky & Martin, 2000,
p. 228)
Trained unigram, bigram and trigram models from a corpus of news text (Wall Street Journal)
applied smoothing
38 million words
Vocab of 19,979 (low-frequency words mapped to UNK).
Computed perplexity on a test set of 1.5 million words.
J&M’s results
Trigrams do best of all.
Value suggests the extent to which the model can fit the data in the test set.
Note: with unigrams, the model has to make lots of guesses!
N-Gram model
Perplexity
Unigram 962
Bigram 170
Trigram 109
Summary Main point about Markov-based language models:
data sparseness is always a problem
smoothing techniques are required to estimate probability of unseen events
Next part discusses more refined smoothing techniques than those seen so far.