Warning: This repository might not contain the newest version of the source code. The development is continued at https://github.com/dlt-rilmta/emmorphpy

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emMorphPy

A wrapper and lemmatizer implemented in Python for emMorph (Humor) Hungarian morphological analyzer

Requirements

• (Included in this repository) The compiled FST (hu.hfstol): go to https://github.com/dlt-rilmta/emMorph for compilation details
• (Included in this repository) The lemmatizer config file: available at https://github.com/dlt-rilmta/hunlp-GATE/blob/master/Lang_Hungarian/resources/hfst/hfst-wrapper.props
• hfst-lookup 0.6 (hfst 3.13.0) or higher: On Ubuntu 18.04 LTS or higher just sudo apt install hfst
• Python 3 (>=3.5, tested with 3.6)
• Pip to install the additional requirements in requirements.txt
• (Optional) a cloud service like Heroku for hosting the API

Features

• Stemming and returning the detailed morphological analyses with the proper transducer and config file
• Handling extra and exceptional lexicons statically and dynamically (see emmorphpy/emmorphpy.py for details)
• Can be used through REST API (using xtsv), or from Python as a library (see usage examples below)

Install on local machine

• Clone the repository
• Run: sudo pip3 install -r requirements.txt
• Use from Python

Install to Heroku

• Register
• Download Heroku CLI
• Login to Heroku from the CLI
• Create an app
• Clone the repository
• Add Heroku as remote origin
• Add APT buildpack: heroku buildpacks:add --index 1 https://github.com/heroku/heroku-buildpack-apt
• Add Python buildpack: heroku buildpacks:add --index 2 heroku/python
• Push the repository to Heroku
• Enjoy!

Usage

• From browser or anyhow through the REST API:

  • Lemmatization: https://emmorph.herokuapp.com/stem/működik
  • Detailed analysis: https://emmorph.herokuapp.com/analyze/működik
  • Lemmatisation with the corresponding detailed analysis: https://emmorph.herokuapp.com/dstem/működik
  • The library also support HTTP POST requests to handle multiple words at once. (See examples for details.)

  >>> import requests
  >>> import json
  >>> word = 'működik'
  >>> json.loads(requests.get('https://emmorph.herokuapp.com/stem/' + word).text)[word]
  [{'lemma': 'működik', 'tag': '[/V][Prs.Def.3Pl]'}, {'lemma': 'működik', 'tag': '[/V][Prs.NDef.3Sg]'}]
  >>> json.loads(requests.get('https://emmorph.herokuapp.com/analyze/' + word).text)[word]
  [{'morphana': 'működik[/V]=működ+ik[Prs.Def.3Pl]=ik'}, {'morphana': 'működik[/V]=működ+ik[Prs.NDef.3Sg]=ik'}]
  >>> json.loads(requests.get('https://emmorph.herokuapp.com/dstem/' + word).text)[word]
  [{'lemma': 'működik', 'tag': '[/V][Prs.Def.3Pl]', 'morphana': 'működik[/V]=működ+ik[Prs.Def.3Pl]=ik', 'readable': 'működik[/V]=működ + ik[Prs.Def.3Pl]', 'twolevel': 'm:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.Def.3Pl]'}, {'lemma': 'működik', 'tag': '[/V][Prs.NDef.3Sg]', 'morphana': 'működik[/V]=működ+ik[Prs.NDef.3Sg]=ik', 'readable': 'működik[/V]=működ + ik[Prs.NDef.3Sg]', 'twolevel': 'm:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.NDef.3Sg]'}]
  >>> words = '\n'.join(('form', word, 'word2', ''))  # One word per line (first line is header, trailing newline is needed!)
  >>> words_out = requests.post('https://emmorph.herokuapp.com/stem', files={'file': words}).text.split('\n')
  >>> print(words_out[1].split('\t'))
  ['működik', '[{"lemma": "működik", "tag": "[/V][Prs.Def.3Pl]"}, {"lemma": "működik", "tag": "[/V][Prs.NDef.3Sg]"}]']
  >>> words_out = requests.post('https://emmorph.herokuapp.com/analyze', files={'file': words}).text.split('\n')
  >>> print(words_out[1].split('\t'))
  ['működik', '[{"morphana": "működik[/V]=működ+ik[Prs.Def.3Pl]=ik"}, {"morphana": "működik[/V]=működ+ik[Prs.NDef.3Sg]=ik"}]']
  >>> words_out = requests.post('https://emmorph.herokuapp.com/dstem', files={'file': words}).text.split('\n')
  >>> print(words_out[1].split('\t'))
  ['működik', '[{"lemma": "működik", "tag": "[/V][Prs.Def.3Pl]", "morphana": "működik[/V]=működ+ik[Prs.Def.3Pl]=ik", "readable": "működik[/V]=működ + ik[Prs.Def.3Pl]", "twolevel": "m:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.Def.3Pl]"}, {"lemma": "működik", "tag": "[/V][Prs.NDef.3Sg]", "morphana": "működik[/V]=működ+ik[Prs.NDef.3Sg]=ik", "readable": "működik[/V]=működ + ik[Prs.NDef.3Sg]", "twolevel": "m:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.NDef.3Sg]"}]']

• From Python:

  >>> import emmorphpy.emmorphpy as emmorph
  >>> m = emmorph.EmMorphPy()
  >>> m.stem('működik')     # Returns list of lemmatisations (stem and tag pairs)
  [('működik', '[/V][Prs.Def.3Pl]'), ('működik', '[/V][Prs.NDef.3Sg]')]
  >>> m.analyze('működik')  # Returns list of detailed analyzes (word by morphemes)
  ['működik[/V]=működ+ik[Prs.Def.3Pl]=ik', 'működik[/V]=működ+ik[Prs.NDef.3Sg]=ik']
  >>> m.dstem('működik')    # Returns list of lemmatisations with the corresponding detailed analyzes (stem, tag and detailed analyzes triples)
  [('működik', '[/V][Prs.Def.3Pl]', 'működik[/V]=működ+ik[Prs.Def.3Pl]=ik', 'működik[/V]=működ + ik[Prs.Def.3Pl]', 'm:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.Def.3Pl]'), ('működik', '[/V][Prs.NDef.3Sg]', 'működik[/V]=működ+ik[Prs.NDef.3Sg]=ik', 'működik[/V]=működ + ik[Prs.NDef.3Sg]', 'm:m ű:ű k:k ö:ö d:d :i :k :[/V] i:i k:k :[Prs.NDef.3Sg]')]
  >>> # Add new analyses to the lexicon (Not a paradigm, but a single analysis!) Format: [('STEM', 'TAG', 'DETAILED_ANALYSIS', 'HFST-OUTPUT')]
  >>> m.lexicon['Obamával'] = [('Obama', '[/N][Nom]', '', ''), ('Obam', '[/N][Nom]', '', ''), ('Obamá', '[/N][Nom]', '', '')]
  >>> # Add new exceptions to the lexicon (Exact matches will be filtered out ASAP!) Format: ('HFST-OUTPUT')
  >>> m.exceptions['almával'] = {'a:a l:l :o m:m :[/N] á:a :[Poss.3Sg] v:v a:a l:l :[Ins]'}  

License

This Python wrapper, the lemmatizer implementation is licensed under the LGPL 3.0 license. xtsv, HFST, the database and the lemmatizer configuration has their own license.