L - the type of language model used to generate text from categoriesM - the multivariate distribution over categoriespublic class LMClassifier<L extends LanguageModel,M extends MultivariateDistribution> extends Object implements JointClassifier<CharSequence>
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 cs in the language model for category
cat and where P(cat) is the probability
assigned by the multivariate distribution over categories. Scores
are defined to be adjusted sample cross-entropy rates:
score(cs,cat)
= (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
cs is constant:
ARGMAXcat P(cat|cs)
= ARGMAXcat P(cs,cat) / P(cs)
= ARGMAXcat P(cs,cat)
P(cat|cs) given
the joint estimates is defined in JointClassification.
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 BinaryLMClassifier.
Language model classifiers may be trained using DynamicLMClassifier using a trainable multivariate estimator and
dynamic language models.
| Constructor and Description |
|---|
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.
|
| Modifier and Type | Method and Description |
|---|---|
String[] |
categories()
Returns the array of categories for this classifier.
|
M |
categoryDistribution()
Returns a multivariate distribution over categories for this
classifier.
|
JointClassification |
classify(CharSequence cSeq)
Returns the joint classification of the specified character sequence.
|
JointClassification |
classifyJoint(char[] cs,
int start,
int end)
A convenience method returning a joint classification over a
character array slice.
|
L |
languageModel(String category)
Returns the language model for the specified category.
|
public LMClassifier(String[] categories, L[] languageModels, M categoryDistribution)
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 length, or if there are duplicate categories.public String[] categories()
This method copies the array and thus changes to it do not affect the categories for this classifier. Thus if a client needs repeated access to a classifier's categores, this method should be called once and the resulting array reused.
public L languageModel(String category)
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.IllegalArgumentException - If the category is not known.public M categoryDistribution()
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.
public JointClassification classify(CharSequence cSeq)
classify in interface BaseClassifier<CharSequence>classify in interface ConditionalClassifier<CharSequence>classify in interface JointClassifier<CharSequence>classify in interface RankedClassifier<CharSequence>classify in interface ScoredClassifier<CharSequence>cSeq - Character sequence being classified.IllegalArgumentException - If the specified object is not
a character sequence.public JointClassification classifyJoint(char[] cs, int start, int end)
estimateJoint(cs,start,end) returns the same result
as esimtateJoint(new String(cs,start,end-start)).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.Copyright © 2019 Alias-i, Inc.. All rights reserved.