After installing Stanza as dependency of UDAR which I recently described, I decided to play around with what is can do.

Installation

The installation is straightforward and is documented on the Stanza getting started page.

First,

sudo pip3 install stanza

Then install a model. For this example, I installed the Russian model:

#!/usr/local/bin/python3
import stanza
stanza.download('ru')

Usage

Part-of-speech (POS) and morphological analysis

Here’s a quick example of POS analysis for Russian. I used PrettyTable to clean up the presentation, but it’s not strictly-speaking necessary.

#!/usr/local/bin/python3
import stanza
from prettytable import PrettyTable

tab = PrettyTable()
tab.field_names = ["word","lemma","upos","xpos","features"]
for field_name in tab.field_names:
    tab.align[field_name] = "l"

nlp = stanza.Pipeline(lang='ru', processors='tokenize,pos,lemma')
doc = nlp('Моя собака внезапно прыгнула на стол.')
for sent in doc.sentences:
    for word in sent.words:
       tab.add_row([word.text, word.lemma, word.upos,
       word.xpos, word.feats if word.feats else "_"])
print(tab)

Note that upos are the universal parts of speech where xpos are language-specific parts of speech.

Named-entity recognition

Stanza can also recognize named entities - persons, organizations, and locations in the text it analyzes:

import stanza
from prettytable import PrettyTable

tab = PrettyTable()
tab.field_names = ["Entity","Type"]
for field_name in tab.field_names:
	tab.align[field_name] = "l"

nlp = stanza.Pipeline(lang='ru', processors='tokenize,ner')
doc = nlp("Владимир Путин живёт в Москве и является Президентом России.")
for sent in doc.sentences:
	for ent in sent.ents:
		tab.add_row([ent.text, ent.type])
print(tab)

which, tells us:

I’m excited to see what can be built from this for language-learning purposes.

One of the challenges that Russian learners face is the placement of syllabic stress, an essential determinate of pronunciation. Although most pedagogical texts for students have marks indicating stress, practically no tests intended for native speakers do. The placement of stress is inferred from memory and context.

I was delighted to discover Dr. Robert Reynolds’ work on natural language processing of Russian text to mark stress based on grammatical analysis of the text. What follows is a brief description of the installation and use of this work. The project page on Github has installation instructions; but I found a number of items that needed to be addressed that were not covered there. For example, this project (UDAR) depends on Stanza; which in turn requires a language-specific (Russian) model.

Installation

The first step is to installation a few dependencies:

1. Install the pexpect module:

sudo pip3 install pexpect

2. Install stanza

sudo pip3 install stanza

3. Install Stanza’s Russian model:

#!/usr/local/bin/python3
import stanza
stanza.download('ru')

Note the my python3 is the Homebrew version; so your hashbang may be different.

4. The project depends on hfst¹ and vislcg3² which can be installed by downloading the following script, i.e.. I had to download the script and run it in CodeRunner.

5. Install udar:

sudo pip3 install --user git+https://github.com/reynoldsnlp/udar

Basic usage

See the project page on Github for more comprehensive details; but I was quickly able to create my own example following the documentation. For example:

#!/usr/local/bin/python3
import udar
doc1 = udar.Document('Моя собака внезапно прыгнула на стол.')
print(doc1.stressed())

which prints the correctly-marked Моя соба́ка внеза́пно пры́гнула на сто́л.

I’m looking forward to exploring the capabilities of this NLP tool further.

References

• Reynolds, Robert J. “Russian natural language processing for computer-assisted language learning: capturing the benefits of deep morphological analysis in real-life applications” PhD Diss., UiT–The Arctic University of Norway, 2016. https://hdl.handle.net/10037/9685
• UDAR - NLP system for applying syllabic stress markings