probability | R Documentation |

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

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

`object` |
a character vector for sentence probabilities,
a word-context conditional expression created with the
conditional operator |

`model` |
an object of class |

`.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. |

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.

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

Valerio Gherardi

```
# Usage of probability()
f <- kgram_freqs("a b b a b a b", 2)
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
```

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