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Recent Literature on WSD - Coggle Diagram

- - - - Ponzetto (2010):
        Automated WordNet expansion using Wikipedia for WSD improvement.
      - Pal (2017):
        Bengali WSD using Bengali WordNet; 75% accuracy.
      - Pasini (2021):
        Statistical-contextual hybrid approach for word disambiguation.
    - - Khanal (2020):
        Machine learning in WSD: adaptation, pattern recognition, data and feature challenges.
      - Vijayan (2017):
        Data-driven ML shift in WSD; employing Naive Bayes, Decision Trees, SVMs.
      - Kohli et al. (2021):
        Transfer learning with Transformer models in WSD; emphasis on contextual embeddings.
      - Boruah (2022):
        WSD system integrating morphological analysis for complex word forms; adaptable to low-resource languages.
      - Song (2021):
        Contextual embeddings and ensemble methods for enhanced WSD accuracy; multiple language model usage.
    - - Taghipour (2015):
        Supervised WSD system performance; focus on annotated data and open-source IMS WSD system.
      - Yuan (2016):
        Semi-supervised WSD using LSTM neural networks for sequential and syntactic pattern capture.
      - Raganato (2017):
        Sequence learning for adaptable all-words WSD models; exploration of neural architectures.
      - Singh (2020):
        Punjabi WSD using deep learning models; MLP and LSTM networks for noun disambiguation.
      - Wang (2021):
        Graph-based algorithms in WSD; leveraging GNNs for semantic graph processing.
  - - - Abid (2017):
        Evolution from traditional WSD to deep learning in Urdu; early stage of Urdu NLE and WSD.
      - Dar (2017):
        DFA-based algorithm for Urdu stop word removal; Finite State Machine application.
      - Shafi (2023):
        USA-23 corpus for Urdu semantic tagging; high accuracy in multi-target classifiers.
    - - Jawaid (2014):
        Published tagged Urdu corpus; ensemble model and 88.74% accuracy solo tagger.
      - Arif et al. (2016):
        Two-stage WSD in Urdu; lexicon-based detection and SVM for classification.
    - - Saeed et al. (2021):
        Deep learning for Urdu WSD; LSTM excels in Lexical Sample and All-Words tasks.
      - Shaukat (2023):
        Novel Urdu lemmatization approach; dictionary-based with 76.44% accuracy.
      - Chawla et al. (2021):
        Comparative study of Transformer models in WSD; excellence in SensEval tasks with kNN and CWEs.