Pie Model for Lemmatization, POS Tagging, and Morphological Analysis of Western Armenian
- 1. École nationale des chartes-PSL / Calfa / LIPN, CNRS UMR 7030
- 2. Université Sorbonne Paris Nord
- 3. SeDyL, UMR8202, INALCO, CNRS, IRD
Description
Data :
- Yavrumyan Marat : Github Repository
Results :
For detailed experiments and results, please refer to the linked publication. The following table displays accuracy (f1-score).
|
task_name |
all |
ambiguous-tokens |
known-tokens |
unknown-tokens |
|
abbr |
0.9977 (0.8399) |
0.7045 (0.5866) |
0.998 (0.8563) |
0.987 (0.4967) |
|
adptype |
0.9969 (0.9628) |
0.9147 (0.9216) |
0.9968 (0.9631) |
0.9982 (0.333) |
|
animacy |
0.9851 (0.9655) |
0.9586 (0.9258) |
0.99 (0.977) |
0.7918 (0.7638) |
|
aspect |
0.9982 (0.8154) |
0.9893 (0.9566) |
0.9987 (0.8496) |
0.9768 (0.7511) |
|
case |
0.9902 (0.9388) |
0.9636 (0.9342) |
0.993 (0.9555) |
0.8812 (0.5959) |
|
connegative |
0.9969 (0.4992) |
0.5073 (0.3366) |
0.9969 (0.4992) |
0.9953 (0.4988) |
|
definite |
0.9756 (0.9679) |
0.9156 (0.864) |
0.9781 (0.9707) |
0.8783 (0.8836) |
|
degree |
0.9632 (0.1962) |
0.2944 (0.1516) |
0.963 (0.2453) |
0.9696 (0.2461) |
|
deixis |
0.9979 (0.9506) |
0.8254 (0.7873) |
0.9979 (0.9523) |
0.9964 (0.2496) |
|
hyph |
0.9987 (0.9116) |
0.9716 (0.9049) |
0.9988 (0.9158) |
0.9971 (0.4993) |
|
lemma |
0.9879 (0.9098) |
0.9173 (0.5108) |
0.991 (0.9413) |
0.865 (0.7453) |
|
mood |
0.9964 (0.6131) |
0.9744 (0.7198) |
0.9968 (0.6152) |
0.9833 (0.8434) |
|
number |
0.9895 (0.9777) |
0.9626 (0.9172) |
0.9931 (0.9847) |
0.8505 (0.7682) |
|
numform |
0.9974 (0.876) |
0.8033 (0.597) |
0.9975 (0.879) |
0.9953 (0.6234) |
|
numtype |
0.9967 (0.5264) |
0.7854 (0.3376) |
0.9967 (0.5264) |
0.9964 (0.5552) |
|
person |
0.9978 (0.9899) |
0.9675 (0.9414) |
0.9981 (0.9919) |
0.9862 (0.9073) |
|
person[psor] |
0.9919 (0.249) |
0.5312 (0.2313) |
0.9921 (0.249) |
0.983 (0.2479) |
|
polarity |
0.9969 (0.9928) |
0.9804 (0.9687) |
0.9973 (0.9936) |
0.9776 (0.9602) |
|
polite |
0.9991 (0.4998) |
0.6014 (0.3755) |
0.9991 (0.4998) |
0.9982 (0.4995) |
|
pos |
0.9933 (0.9897) |
0.9874 (0.9611) |
0.9967 (0.9949) |
0.8606 (0.5483) |
|
poss |
0.9974 (0.9541) |
0.8635 (0.7017) |
0.9974 (0.9551) |
0.9975 (0.4994) |
|
prontype |
0.9949 (0.9189) |
0.882 (0.742) |
0.995 (0.9211) |
0.9873 (0.317) |
|
reflex |
0.9957 (0.871) |
0.6424 (0.537) |
0.9956 (0.8716) |
0.9989 (0.4997) |
|
style |
0.9918 (0.1423) |
0.4598 (0.126) |
0.9921 (0.1423) |
0.9801 (0.1414) |
|
subcat |
0.9957 (0.9825) |
0.9008 (0.8952) |
0.9969 (0.9874) |
0.9493 (0.875) |
|
tense |
0.9979 (0.9913) |
0.9833 (0.7189) |
0.998 (0.9921) |
0.9913 (0.9613) |
|
typo |
0.9985 (0.4996) |
0.8889 (0.4706) |
0.9986 (0.4996) |
0.9975 (0.4994) |
|
verbform |
0.9956 (0.7889) |
0.9731 (0.9462) |
0.9961 (0.9874) |
0.9783 (0.7649) |
|
voice |
0.99 (0.6858) |
0.8393 (0.7731) |
0.9917 (0.693) |
0.9232 (0.4915) |
Models can be used on Deucalion, the lemmatization service from École nationale des chartes-PSL.
Selected Bibliography:
Vidal-Gorène, C., Khurshudyan, V., & Donabédian-Demopoulos, A. (2020, December). Recycling and comparing morphological annotation models for Armenian diachronic-variational corpus processing. In Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects (pp. 90-101).
Vidal-Gorène C., Tomeh N., and Khurshudyan V. (2024, November). Cross-Dialectal Transfer and Zero-Shot Learning for Armenian Varieties: A Comparative Analysis of RNNs, Transformers and LLMs. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 438–449, Miami, USA. Association for Computational Linguistics.
Files
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Additional details
Related works
- Is described by
- Publication: https://aclanthology.org/2024.nlp4dh-1.42/ (URL)
- Is source of
- Software: https://dh.chartes.psl.eu/deucalion/fr (URL)
Funding
- DALiH – Digitizing Armenian Linguistic Heritage: Armenian Multivariational Corpus and Data Processing ANR-21-CE38-0006
- Agence Nationale de la Recherche