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.

Document corpus.

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

Get the number of documents that a term occurred in.

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.

Create a corpus from a list of documents, represented by normalized tokens.