A Python LSTM-based POS-tagger for Udmurt language

This module contains a utility for part-of-speech tagging of Udmurt text. The tool based on LSTM neural network and takes a word order into account.

Installation

The tool could be installed with pip

pip3 install udmurttagger

Note: the model for the utility must be downloaded separately. Due to limitations on the size of the project, I could not place it on a github or PiPy. After launching the program it will download and unpack the model. No action is necessary on your part. But you will need an Internet connection and about 150 megabytes of incoming traffic.

Usage example

Tagging one sentence at a time

>>> from udmurttagger import Tagger
>>> t = Tagger()
>>> sentence = "Сьӧд зарезе."
>>> tagged_sentence = t.predict_pos(sentence)
>>> print(tagged_sentence)
[('сьӧд', 'N'), ('зарезе', 'N')]

Tagset

Tagset based on UDMURT CORPORA

• ADJ — adjective
• ADJPRO — adjectival pronoun
• ADV — adverb
• ADVPRO — adverbial pronoun
• CNJ — conjunction
• IMIT — ideophone
• INTRJ — interjection
• N — noun
• NUM — numeral
• PARENTH — parenthetic word
• PART — particle
• PN — proper noun (subtype of nouns)
• POST — postposition
• PREDIC — predicative
• PRO — pronoun
• V — verb

See the page for the details.

Model

This tool can be used for disambiguation of rule-based markup.

You can make your own wrap of the trained model.

Model's evaluation: loss: 0.2281 - acc: 0.9845 - val_loss: 0.2643 - val_acc: 0.9782.

Contacts

You can contact the contriutor of the project via email:

Boris Orekhov (nevmenandr)

@ gmail