Safe Haskell	None
Language	Haskell2010

NLP.Types

Synopsis

Documentation

data POSTagger t Source

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.

Constructors

POSTagger

Fields

posTagger :: [Sentence] -> [TaggedSentence t]: The initial part-of-speech tagger.
posTrainer :: [TaggedSentence t] -> IO (POSTagger t): Training function to train the immediate POS tagger.
posBackoff :: Maybe (POSTagger t): A tagger to invoke on unknown tokens.
posTokenizer :: Text -> Sentence: A tokenizer; (words will work.)
posSplitter :: Text -> [Text]: A sentence splitter. If your input is formatted as one sentence per line, then use lines, otherwise try Erik Kow's fullstop library.
posSerialize :: ByteString: Store this POS tagger to a bytestring. This does not serialize the backoff taggers.
posID :: ByteString: A unique id that will identify the algorithm used for this POS Tagger. This is used in deserialization

data Corpus Source

Document corpus.

This is a simple hashed corpus, the document content is not stored.

Constructors

Corpus
Fields corpLength :: Int The number of documents in the corpus. corpTermCounts :: Map Text Int A count of the number of documents each term occurred in.

Instances

Eq Corpus
Ord Corpus
Read Corpus
Show Corpus
Generic Corpus
NFData Corpus
Arbitrary Corpus
Serialize Corpus
type Rep 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.

addTerms :: Map Text Int -> Set Text -> Map Text Int Source

addTerm :: Map Text Int -> Text -> Map Text Int Source

module NLP.Types.Tags

module NLP.Types.General

module NLP.Types.Tree