This is a rule-based morphological analyzer for Erzya (myv; Uralic > Mordvinic). It is based on a formalized description of literary Erzya morphology, which also includes a number of dialectal elements, and uses uniparser-morph for parsing. It performs full morphological analysis of Erzya words (lemmatization, POS tagging, grammatical tagging, glossing).
The analyzer is available as a Python package. If you want to analyze Erzya texts in Python, install the module:
pip3 install uniparser-erzya
Import the module and create an instance of ErzyaAnalyzer class. Set mode='strict' if you are going to process text in standard orthography, or mode='nodiacritics' if you expect some words to lack the diacritics (which often happens in social media). After that, you can either parse tokens or lists of tokens with analyze_words(), or parse a frequency list with analyze_wordlist(). Here is a simple example:
from uniparser_erzya import ErzyaAnalyzer
a = ErzyaAnalyzer(mode='strict')
analyses = a.analyze_words('Морфологиянть')
# The parser is initialized before first use, so expect
# some delay here (usually several seconds)
# You will get a list of Wordform objects
# The analysis attributes are stored in its properties
# as string values, e.g.:
for ana in analyses:
print(ana.wf, ana.lemma, ana.gramm, ana.gloss)
# You can also pass lists (even nested lists) and specify
# output format ('xml' or 'json')
# If you pass a list, you will get a list of analyses
# with the same structure
analyses = a.analyze_words([['А'], ['Мон', 'тонь', 'вечктян', '.']],
format='xml')
analyses = a.analyze_words(['Морфологиянть', [['А'], ['Мон', 'тонь', 'вечктян', '.']]],
format='json')Refer to the uniparser-morph documentation for the full list of options.
Disambiguation is not yet available for this language.
Alternatively, you can use a preprocessed word list. The wordlists directory contains a list of words from a 2.3-million-word Erzya corpus (wordlist_main.csv), list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer is 93.6% on literary texts and 90.7% on social media texts.
The description is carried out in the uniparser-morph format and involves a description of the inflection (paradigms.txt), a grammatical dictionary (kpv_lexemes_XXX.txt files), a list of rules that annotate combinations of lexemes and grammatical values with additional Russian translations (lex_rules.txt), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical/borrowing information, its inflectional type (paradigm), and Russian translation. See more about the format in the uniparser-morph documentation.