Papers with morphological tagging
TurkishDelightNLP: A Neural Turkish NLP Toolkit (2022.naacl-demo)
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| Challenge: | a neural Turkish NLP toolkit performs computational linguistic analyses from morphological level to semantic level. |
| Approach: | They propose a neural Turkish NLP toolkit that performs computational linguistic analyses from morphological level to semantic level. |
| Outcome: | The proposed toolkit performs computational linguistic analyses from morphological level to semantic level in Turkish. |
SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes (2023.acl-demo)
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| Challenge: | SanskritShala is a neural-based Sanskrit NLP toolkit that is available as a web-based application . |
| Approach: | They propose a neural Sanskrit NLP toolkit that facilitates linguistic analyses for word segmentation, morphological tagging, dependency parsing, and compound type identification. |
| Outcome: | The proposed toolkit reports state-of-the-art performance on benchmark datasets . it is built with easy-to-use interactive data annotation features . |
GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek (2025.coling-demos)
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Lefteris Loukas, Nikolaos Smyrnioudis, Chrysa Dikonomaki, Spiros Barbakos, Anastasios Toumazatos, John Koutsikakis, Manolis Kyriakakis, Mary Georgiou, Stavros Vassos, John Pavlopoulos, Ion Androutsopoulos
| Challenge: | GR-NLP-TOOLKIT is an open-source natural language processing toolkit for modern Greek. |
| Approach: | They present GR-NLP-TOOLKIT, an open-source natural language processing toolkit for Greek. |
| Outcome: | The toolkit provides state-of-the-art performance in five core NLP tasks . it can be easily installed in Python and is accessible through a demonstration platform on HuggingFace . |
Morphology-Aware Multi-Granularity Representation Learning for Agglutinative Languages (2026.acl-srw)
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| Challenge: | Existing methods for learning low-resource agglutinative languages are limited to word and phrase levels. |
| Approach: | They propose a morphology-aware gated multi-granularity pre-training framework for agglutinative languages . framework leverages morphological knowledge and integrates a word-level encoder to capture contextual semantics . |
| Outcome: | The proposed framework improves on Mongolian and Turkish agglutinative languages . it leverages morphological knowledge and integrates tagging and segmentation to build fine-grained representations . |
Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)
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| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)
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| Challenge: | Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction. |
| Approach: | They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this . |
| Outcome: | The proposed model significantly improves target language performance for languages that are distant from the source language. |
Moving TIGER beyond Sentence-Level (L18-1)
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| Challenge: | TIGER 2.2-doc is a new set of annotations for the German TIger corpus. |
| Approach: | They propose a new set of annotations for the German TIGER corpus . they introduce new document-level annotations: authors and their gender. |
| Outcome: | The new annotations improve the TIGER corpus and its structure and authors and gender. |
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (2022.coling-1)
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Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
| Challenge: | Previously, compounding is a problem of identifying semantic relations between components of a word. |
| Approach: | They propose a multi-task learning architecture which incorporates contextual information and enriches syntactic information using morphological tagging and dependency parsing as auxiliary tasks. |
| Outcome: | The proposed architecture shows 6.1 points accuracy and 7.7 points (F1-score) absolute gain in English and Marathi languages. |
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? (D18-1)
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| Challenge: | Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn. |
| Approach: | They extend contextual decomposition technique to convolutional neural networks and bidirectional long-term memory networks to evaluate and compare these models for morphological tagging on three morphology-dependent languages. |
| Outcome: | The proposed models implicitly discover understandable linguistic rules for morphological tagging on three morphology-dependent languages. |
Typology-Aware Multilingual Morphosyntactic Parsing with Joint Abstract Node Modeling (2026.acl-long)
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| Challenge: | UniDive 2025 Morphosyntactic Parsing (MSP) shared task unifies dependency structure, morphological features, and unrealized arguments. |
| Approach: | They propose a multilingual, typology-aware joint system that integrates word-type prediction, content-only parsing, morphological tagging, and an abstract-node component within a single architecture. |
| Outcome: | The proposed model outperforms the leading submission by 3.23 percentage points in MSLAS, 3.35 in LAS, and 1.78 in FEATS macro F1. |