apertium-streamparser 5.0.2

Apertium流分析器

python 3库解析Apertium stream format，生成LexicalUnits。

安装

streamparser可通过PyPi：

$ pip install apertium-streamparser
$ apertium-streamparser
$^vino/vino<n><m><sg>/venir<vblex><ifi><p3><sg>$
[[SReading(baseform='vino', tags=['n', 'm', 'sg'])], [SReading(baseform='venir', tags=['vblex', 'ifi', 'p3', 'sg'])]]

通过pypi安装还将安装streamparser模块。

用法

作为图书馆

带字符串输入

>>>fromstreamparserimportparse>>>lexical_units=parse('^hypercholesterolemia/*hypercholesterolemia$\[\]\^\$[^ignoreme/yesreally$]^a\/s/a\/s<n><nt>$^vino/vino<n><m><sg>/venir<vblex><ifi><p3><sg>$.eefe^dímelo/decir<vblex><imp><p2><sg>+me<prn><enc><p1><mf><sg>+lo<prn><enc><p3><nt>/decir<vblex><imp><p2><sg>+me<prn><enc><p1><mf><sg>+lo<prn><enc><p3><m><sg>$')>>>forlexical_unitinlexical_units:print('%s (%s) → %s'%(lexical_unit.wordform,lexical_unit.knownness,lexical_unit.readings))

hypercholesterolemia (<class 'streamparser.unknown'>) → [[SReading(baseform='*hypercholesterolemia', tags=[])]]
a\/s (<class 'streamparser.known'>) → [[SReading(baseform='a\\/s', tags=['n', 'nt'])]]
vino (<class 'streamparser.known'>) → [[SReading(baseform='vino', tags=['n', 'm', 'sg'])], [SReading(baseform='venir', tags=['vblex', 'ifi', 'p3', 'sg'])]]
dímelo (<class 'streamparser.known'>) → [[SReading(baseform='decir', tags=['vblex', 'imp', 'p2', 'sg']), SReading(baseform='me', tags=['prn', 'enc', 'p1', 'mf', 'sg']), SReading(baseform='lo', tags=['prn', 'enc', 'p3', 'nt'])], [SReading(baseform='decir', tags=['vblex', 'imp', 'p2', 'sg']), SReading(baseform='me', tags=['prn', 'enc', 'p1', 'mf', 'sg']), SReading(baseform='lo', tags=['prn', 'enc', 'p3', 'm', 'sg'])]]

使用文件输入

>>>fromstreamparserimportparse_file>>>lexical_units=parse_file(open('~/Downloads/analyzed.txt'))>>>forlexical_unitinlexical_units:print('%s (%s) → %s'%(lexical_unit.wordform,lexical_unit.knownness,lexical_unit.readings))

Høgre (<class 'streamparser.known'>) → [[SReading(baseform='Høgre', tags=['np'])], [SReading(baseform='høgre', tags=['n', 'nt', 'sp'])], [SReading(baseform='høg', tags=['un', 'sint', 'sp', 'comp', 'adj'])], [SReading(baseform='høgre', tags=['f', 'n', 'ind', 'sg'])], [SReading(baseform='høgre', tags=['f', 'n', 'ind', 'sg'])], [SReading(baseform='høgre', tags=['sg', 'nt', 'ind', 'posi', 'adj'])], [SReading(baseform='høgre', tags=['mf', 'sg', 'ind', 'posi', 'adj'])], [SReading(baseform='høgre', tags=['un', 'ind', 'pl', 'posi', 'adj'])], [SReading(baseform='høgre', tags=['un', 'def', 'sp', 'posi', 'adj'])]]
kolonne (<class 'streamparser.known'>) → [[SReading(baseform='kolonne', tags=['m', 'n', 'ind', 'sg'])], [SReading(baseform='kolonne', tags=['m', 'n', 'ind', 'sg'])]]
Grunnprinsipp (<class 'streamparser.known'>) → [[SReading(baseform='grunnprinsipp', tags=['n', 'nt', 'ind', 'sg'])], S[Reading(baseform='grunnprinsipp', tags=['n', 'nt', 'pl', 'ind'])], [SReading(baseform='grunnprinsipp', tags=['n', 'nt', 'ind', 'sg'])], [SReading(baseform='grunnprinsipp', tags=['n', 'nt', 'pl', 'ind'])]]
7 (<class 'streamparser.known'>) → [[SReading(baseform='7', tags=['qnt', 'pl', 'det'])]]
px (<class 'streamparser.unknown'>) → []

从终端

使用标准输入

$ bzcat ~/corpora/nnclean2.txt.bz2 | apertium-deshtml | lt-proc -we /usr/share/apertium/apertium-nno/nno.automorf.bin | python3 streamparser.py
[[SReading(baseform='Høgre', tags=['np'])],
 [SReading(baseform='høgre', tags=['n', 'sp', 'nt'])],
 [SReading(baseform='høg', tags=['un', 'sp', 'adj', 'comp', 'sint'])],
 [SReading(baseform='høgre', tags=['n', 'f', 'ind', 'sg'])],
 [SReading(baseform='høgre', tags=['n', 'f', 'ind', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'nt', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'mf', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'un', 'pl'])],
 [SReading(baseform='høgre', tags=['posi', 'def', 'sp', 'adj', 'un'])]][[SReading(baseform='kolonne', tags=['n', 'm', 'ind', 'sg'])],
 [SReading(baseform='kolonne', tags=['n', 'm', 'ind', 'sg'])]]
...

在终端中输入文件

$ bzcat ~/corpora/nnclean2.txt.bz2 | apertium-deshtml | lt-proc -we /usr/share/apertium/apertium-nno/nno.automorf.bin > analyzed.txt
$ python3 streamparser.py analyzed.txt
[[SReading(baseform='Høgre', tags=['np'])],
 [SReading(baseform='høgre', tags=['n', 'sp', 'nt'])],
 [SReading(baseform='høg', tags=['un', 'sp', 'adj', 'comp', 'sint'])],
 [SReading(baseform='høgre', tags=['n', 'f', 'ind', 'sg'])],
 [SReading(baseform='høgre', tags=['n', 'f', 'ind', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'nt', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'mf', 'sg'])],
 [SReading(baseform='høgre', tags=['posi', 'ind', 'adj', 'un', 'pl'])],
 [SReading(baseform='høgre', tags=['posi', 'def', 'sp', 'adj', 'un'])]][[SReading(baseform='kolonne', tags=['n', 'm', 'ind', 'sg'])],
 [SReading(baseform='kolonne', tags=['n', 'm', 'ind', 'sg'])]]
...

贡献

streamparser使用TravisCI进行连续集成。本地，使用 make test运行它所做的相同检查。使用pip install -r requirements.txt 安装开发所需的要求，例如绒布。

标签：

• 格式
• tags
• sg
• ind
• gre
• apertium
• baseform
• adj

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