SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods | Paper Citation Record

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Reference 10

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Reference 14

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer

Reference 21

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods Towards a Unified View of Parameter-Efficient Transfer Learning

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods Khapra, and Pratyush Kumar

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages

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This paper cites https://huggingface.co/datasets/azcorpus/azcorpus_v0 azcorpus: The largest open-source nlp corpus for azerbaijani (1.9m documents, 18m sentences).

SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods https://huggingface.co/datasets/azcorpus/azcorpus_v0 azcorpus: The largest open-source nlp corpus for azerbaijani (1.9m documents, 18m sentences)

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods A comparison of estimators of mean and its functions in finite populations

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SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages

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Observation e0a67a04-1a24-4de7-8aa4-deb63b4c9ae9 · outbound

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Observation 35a13d76-0c76-4b6c-80c6-50c19523bb26 · outbound

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Reference 76

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Observation f11a9db5-ed55-4a71-945e-a67f98adf2eb · outbound

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Reference 77

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Observation 77e75c6f-0d80-47c5-92a1-3d2bece104fd · outbound

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Reference 78

Resolution: verified exact; doi, observed 2026-08-07T12:44:33.438710Z

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Observation ae5573d3-fa07-43e2-a4ad-11f97e55abf3 · outbound

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Reference 79

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Observation 2b816838-2e23-43f0-ae3f-b88e9a21f2d8 · outbound

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Reference 80

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Observation 6a6cb5df-0c64-4103-9b80-ab5672c2cad2 · outbound

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Reference 81

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Observation f1969147-bb3a-4ffa-8e77-6b67a33cb6d9 · outbound

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Reference 82

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Reference 83

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Observation fc063fae-c86c-431e-984c-67c22b54d2d2 · outbound

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Reference 84

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Observation 7e9e8a83-54e6-4ca7-836c-fb0bd2768788 · outbound

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Reference 85

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Observation 769b192f-4d85-4b2a-a7c0-5f85dc17a1cf · outbound

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Reference 86

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Observation 3570348f-0726-4f23-9ae2-8b6e021ecc91 · outbound

This paper cites an unresolved cited work.

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Reference 87

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Coverage vector

Reference resolution

Outbound references

Pith citing papers