Safe Haskell | None |
---|---|
Language | Haskell2010 |
- data POSTagger t = POSTagger {
- posTagger :: [Sentence] -> [TaggedSentence t]
- posTrainer :: [TaggedSentence t] -> IO (POSTagger t)
- posBackoff :: Maybe (POSTagger t)
- posTokenizer :: Text -> Sentence
- posSplitter :: Text -> [Text]
- posSerialize :: ByteString
- posID :: ByteString
- data Corpus = Corpus {
- corpLength :: Int
- corpTermCounts :: Map Text Int
- termCounts :: Corpus -> Text -> Int
- addDocument :: Corpus -> [Text] -> Corpus
- mkCorpus :: [[Text]] -> Corpus
- addTerms :: Map Text Int -> Set Text -> Map Text Int
- addTerm :: Map Text Int -> Text -> Map Text Int
- module NLP.Types.Tags
- module NLP.Types.General
- module NLP.Types.Tree
Documentation
Part of Speech tagger, with back-off tagger.
A sequence of pos taggers can be assembled by using backoff taggers. When tagging text, the first tagger is run on the input, possibly tagging some tokens as unknown ('Tag Unk'). The first backoff tagger is then recursively invoked on the text to fill in the unknown tags, but that may still leave some tokens marked with 'Tag Unk'. This process repeats until no more taggers are found. (The current implementation is not very efficient in this respect.).
Back off taggers are particularly useful when there is a set of domain specific vernacular that a general purpose statistical tagger does not know of. A LitteralTagger can be created to map terms to fixed POS tags, and then delegate the bulk of the text to a statistical back off tagger, such as an AvgPerceptronTagger.
POSTagger
values can be serialized and deserialized by using
serialize
and NLP.POS.deserialize`. This is a bit tricky
because the POSTagger abstracts away the implementation details of
the particular tagging algorithm, and the model for that tagger (if
any). To support serialization, each POSTagger value must provide
a serialize value that can be used to generate a ByteString
representation of the model, as well as a unique id (also a
ByteString
). Furthermore, that ID must be added to a `Map
ByteString (ByteString -> Maybe POSTagger -> Either String
POSTagger)` that is provided to deserialize
. The function in the
map takes the output of posSerialize
, and possibly a backoff
tagger, and reconstitutes the POSTagger that was serialized
(assigning the proper functions, setting up closures as needed,
etc.) Look at the source for taggerTable
and
readTagger
for examples.
POSTagger | |
|
Document corpus.
This is a simple hashed corpus, the document content is not stored.
Corpus | |
|
termCounts :: Corpus -> Text -> Int Source
Get the number of documents that a term occurred in.
addDocument :: Corpus -> [Text] -> Corpus Source
Add a document to the corpus.
This can be dangerous if the documents are pre-processed differently. All corpus-related functions assume that the documents have all been tokenized and the tokens normalized, in the same way.
mkCorpus :: [[Text]] -> Corpus Source
Create a corpus from a list of documents, represented by normalized tokens.
module NLP.Types.Tags
module NLP.Types.General
module NLP.Types.Tree