nltk/nltk — Issue #43: Off-the-shelf models
Author: alexrudnick
Created: 2012-01-17 06:14:11+00:00
State: open

Add off-the-shelf machine learning models for a variety of NLP tasks that are trained on a large
training corpus, and get reasonably good performance. E.g.:

- POS Tagging
- Tokenization
- Parsing?
- SRL
- Chunking
- NE detection

Migrated from http://code.google.com/p/nltk/issues/detail?id=210

earlier comments

edloper said, at 2009-02-08T04:26:45.000Z:

Status update:
- Tagger & tokenizer: done and checked in.
- NE chunker: done but not checked in.
- NP chunker: still training the model.

ewan.klein said, at 2009-02-11T09:49:43.000Z:

Is there a target date for checking in the NE chunker?

StevenBird1 said, at 2009-02-11T10:51:11.000Z:

please see r7581

StevenBird1 said, at 2009-02-21T01:44:59.000Z:

Carried forward to 0.9.9

StevenBird1 said, at 2010-03-02T08:48:19.000Z:

The code for off-the-shelf models should live in the source repository, so it can be
inspected and updated. E.g. how was the maxent tagger model used by NLTK's default
POS tagger (nltk.pos_tag) created?