nexaloid 0.1.30

Chinese tokenizer runtime

nexaloid Python

Python bindings for the Nexaloid Chinese tokenizer.

The wheel bundles the native library and the default dictionary, so normal use does not need a separate dictionary file.

Install

pip install nexaloid

Usage

from nexaloid import Mode, Tokenizer

tokenizer = Tokenizer()

print([token.text for token in tokenizer.tokenize("南京市长江大桥")])
print([token.text for token in tokenizer.tokenize("中国科学院计算技术研究所", Mode.SEARCH)])
print([token.text for token in tokenizer.tokenize("中国科学院计算技术研究所", Mode.RECALL_SEARCH)])

for token in tokenizer.tokenize("昨日中概股集体跌超百分之五", Mode.SEARCH):
    print(token.text, token.source, token.flags)

The default tokenizer uses the packaged data/dict/nexaloid.nxdict. Pass dict_path when you need a custom dictionary:

tokenizer = Tokenizer(dict_path="data/dict/nexaloid.tsv")

Whitespace tokens are filtered by default. Enable jieba-like whitespace retention when needed:

tokenizer = Tokenizer(preserve_whitespace=True)
print(tokenizer.lcut("中文 English\t混排\n第二行"))

Jieba-style API

import nexaloid.compat_jieba as jieba

print(jieba.lcut("我爱北京天安门"))
print(jieba.lcut("小明硕士毕业", HMM=True))
print(list(jieba.cut_for_search("中国科学院计算技术研究所")))

HMM=True loads the bundled BMES HMM plugin and artifact to recover unknown words such as short names and domain terms.

Token Contract

Mode.SEARCH preserves every non-whitespace token on the Accurate path, including single-character and repeated-position tokens, and adds in-boundary Han 2-gram / 3-gram expansions. Mode.RECALL_SEARCH also adds explicit lattice candidates. cut_for_search() keeps search-term behavior by filtering one-character terms and deduplicating text.

Each token exposes the stable source name in source and source-specific metadata in flags. For source == "rule", a nonzero flags value is the custom rule's 1-based JSON array index. Plugin tokens use flags for plugin-defined subtypes.

Development

cd bindings/python
python -m build
cd ../..
$env:PYTHONPATH = "$PWD\bindings\python\src"
python tools\badcase_runner.py
python tools\regression_checks.py