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L- the type of language model used to generate text from categories
M- the multivariate distribution over categories
public class LMClassifier<L extends LanguageModel,M extends MultivariateDistribution>
LMClassifier performs joint probability-based
classification of character sequences into non-overlapping
categories based on language models for each category and a
multivariate distribution over categories. Thus the subclass of
Classification returned by the classify method is
JointClassification. In addition to joint and conditional
probabilities of categories given the input, the score of the
returned joint classification is the character plus category
sample entropy rate.
A language-model classifier is constructed from a fixed, finite set of categories which are assumed to have disjoint (non-overlapping) sets of members. The categories are represented as simple strings. Each category is assigned a language model. Furthermore, a multivariate distribution over the set of categories assigns marginal category probabilities.
Joint log probabilities are determined in the usual way:
log2 P(cs,cat) = log2 P(cs|cat) + log2 P(cat)
P(cs|cat)is the probability of the character sequence
csin the language model for category
P(cat)is the probability assigned by the multivariate distribution over categories. Scores are defined to be adjusted sample cross-entropy rates:
Note that the contribution of the category probability to the score approaches zero as the sample size grows and the data overwhelms the pre-data expectation. Also note that each category has its estimate divided by the same amount, so the probabilistic ordering is preserved. If the language models are process models, the cross-entroy rate is just
= (log2 P(cs,cat)) / (cs.length() + 2)
= (log2 P(cs|cat) + log2 P(cat)) / (cs.length() + 2)
(log2P(cs|cat))/cs.length(); for process models, add one to the denominator to account for figuratively generating the end-of-character-sequence symbol.
Note that maximizing joint probabilities is the same as maximizing conditional probabilities because the character sequence
csis constant:A computation of conditional estimates
= ARGMAXcat P(cs,cat) / P(cs)
= ARGMAXcat P(cs,cat)
P(cat|cs)given the joint estimates is defined in
To ensure consistent estimates, all of the language models should either be process language models or sequence language models over the same set of characters, depending on whether probability normalization is over fixed length sequences or over all strings. On the other hand, the models themselves may be a mixture of n-gram lengths and smoothing parameters, or even in the case of sequence models, tokenized models and sequence character models.
Boolean classifiers for membership can be constructed with this class by means of a positive language model and a negative model. A character sequence is considered an instance of the category if they are more likely in the positive model than the negative model. There are several strategies for constructing anti-models. The most common methodology is to build an anti-model from an unbiased sample of negative cases, but this requires supervision for negative cases and tends to bias toward the model with more training data given the way language model cross-entropy tends to go down with more training data in general. Another approach is to build a weaker model from the same training data as the positive model, for instance by using lower order n-grams for the negative model. The simplest approach is to use a uniform negative model, which amounts to a cross-entropy rejection threshold; this is the basis of
Language model classifiers may be trained using
DynamicLMClassifierusing a trainable multivariate estimator and dynamic language models.
- Bob Carpenter
LMClassifier(String categories, L languageModels, M categoryDistribution)
Construct a joint classifier for character sequences classifying over a specified set of categories, with a multivariate distribution over those categories and a language model per category.
Returns the array of categories for this classifier.
Returns a multivariate distribution over categories for this classifier.
Returns the joint classification of the specified character sequence.
classifyJoint(char cs, int start, int end)
A convenience method returning a joint classification over a character array slice.
Returns the language model for the specified category.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
LMClassifierpublic LMClassifier(String categories, L languageModels, M categoryDistribution)
- Construct a joint classifier for character sequences classifying over a specified set of categories, with a multivariate distribution over those categories and a language model per category. The language models are supplied in a parallel array to the categories.
The category distribution is the marginal over categories, and each language model provides a conditional estimate given its category. Categorization is as described in the class documentation.
categories- Array of categories for classification.
languageModels- A parallel array of language models for the categories.
categoryDistribution- The marginal distribution over the categories for classification.
IllegalArgumentException- If there are not at least two categories, or if the category and language model arrays are not the same lenght.
categoriespublic String categories()
- Returns the array of categories for this classifier.
This method copies the array and thus changes to it do not affect the categories for this classifier.
- The array of categories for this classifier.
languageModelpublic L languageModel(String category)
- Returns the language model for the specified category. The model for a specified category is used to provide estimates of
P(cSeq|category), the conditional probability of a character sequence given the specified category as described in the class documentation above.
Changes to the returned model affect this classifier's behavior.
category- The specified category.
- The language model for the specified category.
IllegalArgumentException- If the category is not known.
categoryDistributionpublic M categoryDistribution()
- Returns a multivariate distribution over categories for this classifier. This is method returns
P(category), the marginal distribution over categories used during classification as described in the class documentation.
Changes to the returned distribution affect this classifier's behavior.
- The distribution over categories.
classifypublic JointClassification classify(CharSequence cSeq)
- Returns the joint classification of the specified character sequence.
- Specified by:
- Specified by:
- Specified by:
- Specified by:
- Specified by:
- Specified by:
cSeq- Character sequence being classified.
- Joint classification of the specified character sequence.
IllegalArgumentException- If the specified object is not a character sequence.
classifyJointpublic JointClassification classifyJoint(char cs, int start, int end)
- A convenience method returning a joint classification over a character array slice. Note that
estimateJoint(cs,start,end)returns the same result as
cs- Underlying character array.
start- Index of first character in slice.
end- One plus the index of the last character in the slice.
IllegalArgumentException- If the start index is less than zero or greater than the end index or if the end index is not within bounds.
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