kgrams (version 0.1.0)

probability: Language Model Probabilities

Description

Compute sentence probabilities and word continuation conditional probabilities from a language model

Usage

probability(object, model, .preprocess = attr(model, ".preprocess"), ...)
# S3 method for kgrams_word_context
probability(object, model, .preprocess = attr(model, ".preprocess"), ...)
# S3 method for character
probability(
  object,
  model,
  .preprocess = attr(model, ".preprocess"),
  .tknz_sent = attr(model, ".tknz_sent"),
  ...
)

Arguments

object

a character vector for sentence probabilities, a word-context conditional expression created with the conditional operator %|% (see word_context). for word continuation probabilities.

model

either an object of class language_model, or a kgram_freqs object. The language model from which probabilities are computed.

.preprocess

a function taking a character vector as input and returning a character vector as output. Preprocessing transformation applied to input before computing probabilities

...

further arguments passed to or from other methods.

.tknz_sent

a function taking a character vector as input and returning a character vector as output. Optional sentence tokenization step applied before computing sentence probabilities.

Value

a numeric vector. Probabilities of the sentences or word continuations.

Details

The generic function probability() is used to obtain both sentence unconditional probabilities (such as Prob("I was starting to feel drunk")) and word continuation conditional probabilities (such as Prob("you" | "i love")). In plain words, these probabilities answer the following related but conceptually different questions:

• Sentence probability Prob(s): what is the probability that extracting a single sentence (from a corpus of text, say) we will obtain exactly 's'?

• Continuation probability Prob(w|c): what is the probability that a given context 'c' will be followed exactly by the word 'w'?

In order to compute continuation probabilities (i.e. Prob(w|c)), one must create conditional expressions with the infix operator %|%, as shown in the examples below. Both probability and %|% are vectorized with respect to words (left hand side of %|%), but the context must be a length one character (right hand side of %|%).

The input is treated as in query for what concerns word tokenization: anything delimited by (one or more) white space(s) is tokenized as a word. For sentence probabilities, Begin-Of-Sentence and End-Of-Sentence paddings are implicitly added to the input, but specifying them explicitly does not produce wrong results as BOS and EOS tokens are ignored by probability() (see the examples below). For continuation probabilities, any context of more than N - 1 words (where N is the k-gram order the language model) is truncated to the last N - 1 words.

By default, the same .preprocess() and .tknz_sent() functions used during model building are applied to the input, but this can be overriden with arbitrary functions. Notice that the .tknz_sent can be useful (for sentence probabilities) if e.g. the input is a length one unprocessed character vector.

Examples

# NOT RUN {
# Usage of probability()

f <- kgram_freqs("a b b a b a b", 2)



### Compute probabilities directly from 'f'. These are ML probabilities

###### Sentence probabilities
( p <- probability("a b b a b", f) )
identical(p, probability(BOS() %+% "a b b a b" %+% EOS(), f)) # TRUE

###### Continuation probabilities
( p <- probability("a" %|% "b", f) ) # Continuation probabilities
identical(p, probability("a" %|% "a b a b b a b", f)) # TRUE
probability(c("a", "b", EOS(), UNK()) %|% BOS(), f) # c(1, 0, 0, 0)
probability("a" %|% UNK(), f) # NA, as context has count zero


### Compute probabilities from a language model

m <- language_model(f, "add_k", k = 1)
probability(c("a", "b", EOS(), UNK()) %|% BOS(), m) # c(0.4, 0.2, 0.2, 0.2)
probability("a" %|% UNK(), m) # not NA

# }