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metanl

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Some convenient natural language tools that build on NLTK.

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- extprocess.py
- freeling.py
- mecab.py
- nltk_morphy.py
- token_utils.py
- merge_english.py
- reformat-leeds-ja.py
- reformat_using_rosette.py
- test_extprocesses.py
- test_nltk_morphy.py
- test_tokens.py
.gitignore
LICENSE.txt
MANIFEST.in
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/commonsense/metanl

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd metanl

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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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`.

## metanl.token_utils

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.

## metanl.nltk_morphy

`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

## metanl.extprocess

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`.

### metanl.freeling

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.

### metanl.mecab

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.