AsPOS: Assamese POS Tagger
| Property | Value |
|---|---|
| Author | dpathak |
| Performance | 74.62% F1-score |
| Paper | IEEE/ACS AICCSA 2022 |
| Language | Assamese |
What is aspos_assamese_pos_tagger?
AsPOS is a specialized Part-of-Speech (POS) tagging model designed specifically for the Assamese language. It leverages advanced neural architectures combining stacked embeddings (MuRIL + FlairEmbedding) with a BiLSTM-CRF model to achieve state-of-the-art performance in Assamese text analysis.
Implementation Details
The model employs a sophisticated architecture that combines multiple components:
- Stacked embedding approach using MuRIL and FlairEmbedding
- BiLSTM-CRF architecture for sequence labeling
- Support for 41 distinct POS tags
- Python 3.6+ compatibility
- Built on Flair framework (Version 0.9.0)
Core Capabilities
- Accurate POS tagging for Assamese text with 74.62% F1-score
- Handles complex Assamese morphological structures
- Supports both academic and practical applications
- Easy integration through Flair's SequenceTagger interface
Frequently Asked Questions
Q: What makes this model unique?
AsPOS is the first dedicated POS tagger for Assamese language using modern deep learning approaches. Its combination of MuRIL and FlairEmbedding makes it particularly effective for capturing Assamese language nuances.
Q: What are the recommended use cases?
The model is ideal for linguistic research, text analysis of Assamese content, and natural language processing applications specifically targeting Assamese language processing. It's particularly useful for academic research and computational linguistics projects.
