Multilingual natural language tools, wrapping NLTK and other systems.
## Deprecated as of June 2014
metanl is no longer actively developed or supported.
This package was created to support the language-processing needs that [ConceptNet 5](http://conceptnet5.media.mit.edu) shared with code developed at Luminoso. Those needs have diverged, to the point where it made the most sense to split the functionality again.
A simplified version of metanl has been moved into the conceptnet5 package, as conceptnet5.language.
Utilities for working with tokens:
- tokenize splits strings into tokens, using NLTK.
- untokenize rejoins tokens into a correctly-spaced string, using ad-hoc rules that aim to invert what NLTK does.
- un_camel_case splits a CamelCased string into tokens.
These functions make assumptions that work best in English, and work reasonably in other Western languages, and fail utterly in languages that don’t use spaces.
nltk_morphy is a lemmatizer (a stemmer with principles). It enables you to reduce words to their root form in English, using the Morphy algorithm that’s built into WordNet, together with NLTK’s part of speech tagger.
Morphy works best with a known part of speech. In fact, the way it works in NLTK is pretty bad if you don’t specify the part of speech. The nltk_morphy wrapper provides:
- An alignment between the POS tags that nltk.pos_tag outputs, and the input that Morphy expects
- A strategy for tagging words whose part of speech is unknown
- A small list of exceptions, for cases where Morphy returns an unintuitive or wrong result
Sometimes, the best available NLP tools are written in some other language besides Python. They may not provide a reasonable foreign function interface. What they do often provide is a command-line utility.
metanl.extprocess provides abstractions over utilities that take in natural language, and output a token-by-token analysis. This is used by two other modules in metanl.
FreeLing is an NLP tool that can analyze many European languages, including English, Spanish, Italian, Portuguese, Welsh, and Russian. This module allows you to run FreeLing in a separate process, and use its analysis results in Python.
In Japanese, NLP analyzers are particularly important, because without one you don’t even know where to split words.
MeCab is the most commonly used analyzer for Japanese text. This module runs MeCab in an external process, allowing you to get its complete analysis results, or just use it to tokenize or lemmatize text.
As part of MeCab’s operation, it outputs the phonetic spellings of the words it finds, in kana. We use this to provide a wrapper function that can romanize any Japanese text.