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REFERENCES.md

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References

Academic papers, datasets, and software cited across the anno codebase.

Named Entity Recognition

  • E. F. Tjong Kim Sang and F. De Meulder. "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition." CoNLL, 2003. [PDF] — Evaluation benchmark and span-level F1 definition used throughout anno-eval.

  • R. Grishman and B. Sundheim. "Message Understanding Conference — 6: A Brief History." COLING, 1996. [PDF] — Original motivation for standardised NER evaluation.

  • U. Zaratiana, N. Tomeh, P. Holat, and T. Charnois. "GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer." NAACL, 2024. [arXiv:2311.08526] — Architecture basis for the gliner and gliner-candle backends.

  • U. Zaratiana, N. Tomeh, P. Holat, and T. Charnois. "GLiNER2: Multi-task Information Extraction with Generalist Models." 2025. [arXiv:2507.18546] — Basis for the gliner2 multi-task backend (NER + classification + structure extraction).

  • D. Bogdanov, A. Mokhov, et al. "NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data." 2024. [arXiv:2402.15343] — Basis for the nuner zero-shot token-classification backend.

  • J. Li, Y. Fei, et al. "Unified Named Entity Recognition as Word-Word Relation Classification." AAAI, 2022. [arXiv:2112.10070] — Basis for the w2ner backend (nested and discontinuous entities via handshaking matrix).

  • J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." NAACL, 2019. [arXiv:1810.04805] — Underlying architecture for bert-onnx, deberta-v3, albert, and candle-ner backends.

  • P. He, X. Liu, J. Gao, and W. Chen. "DeBERTa: Decoding-enhanced BERT with Disentangled Attention." ICLR, 2021. [arXiv:2006.03654] — Architecture basis for the deberta-v3 backend. Disentangled attention separately encodes content and position.

  • Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut. "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations." ICLR, 2020. [arXiv:1909.11942] — Architecture basis for the albert backend. Cross-layer parameter sharing and factorized embeddings.

Classical Sequence Models

  • J. Lafferty, A. McCallum, and F. Pereira. "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data." ICML, 2001. [PDF] — Basis for the crf backend.

  • Z. Huang, W. Xu, and K. Yu. "Bidirectional LSTM-CRF Models for Sequence Tagging." 2015. [arXiv:1508.01991] — Foundational architecture for the bilstm_crf backend.

  • G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer. "Neural Architectures for Named Entity Recognition." NAACL-HLT, 2016. [PDF] — BiLSTM-CRF with character-level embeddings; eliminates need for hand-crafted features.

  • X. Ma and E. Hovy. "End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF." ACL, 2016. [arXiv:1603.01354] — Combined character-level CNN + word-level BiLSTM + CRF pipeline for sequence labeling.

  • L. R. Rabiner. "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition." Proceedings of the IEEE 77(2), 1989. [PDF] — Basis for the hmm backend.

  • A. J. Viterbi. "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm." IEEE Transactions on Information Theory, 1967. — Viterbi decoding algorithm used in HMM inference.

Coreference Resolution

  • K. Lee, L. He, M. Lewis, and L. Zettlemoyer. "End-to-end Neural Coreference Resolution." EMNLP, 2017. [arXiv:1707.07045] — Inspiration for the mention-ranking coreference architecture.

  • K. Raghunathan, H. Lee, S. Rangarajan, N. Chambers, M. Surdeanu, D. Jurafsky, and C. Manning. "A Multi-Pass Sieve for Coreference Resolution." EMNLP, 2010. [PDF] — Basis for the rule-based sieve architecture in SimpleCorefResolver.

  • S. Otmazgin, A. Cattan, and Y. Goldberg. "F-COREF: Fast, Accurate and Easy to Use Coreference Resolution." AACL-IJCNLP, 2022. [arXiv:2209.04280] — Basis for the FCoref neural coreference backend. LingMess mention detection with DistilRoBERTa.

  • O. Bourgois and T. Poibeau. "Coreference Resolution for Machine Reading: A Survey." 2025. — Contemporary reference for the coreference approach in anno.

  • D. Jurafsky and J. H. Martin. Speech and Language Processing, Ch. 21 (Coreference Resolution), 3rd ed. draft, 2024. [Online] — Textbook reference for coreference fundamentals.

Discourse Analysis

  • B. J. Grosz, A. K. Joshi, and S. Weinstein. "Centering: A Framework for Modeling the Local Coherence of Discourse." Computational Linguistics 21(2), 1995. [PDF] — Theoretical basis for the discourse::centering module. Defines Cb, Cf, Cp, and transition types.

Relation Extraction

  • Y. Wang, Y. Yu, et al. "TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking." COLING, 2020. [arXiv:2010.13415] — Architecture basis for the tplinker backend.

Evaluation Frameworks and Datasets

  • A. Akbik, T. Bergmann, D. Blythe, K. Rasul, S. Schweter, and R. Vollgraf. "FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP." NAACL, 2019. [PDF] — Referenced in the Scope section as an upstream training framework.

  • O. Uzuner, B. R. South, S. Shen, and S. L. DuVall. "2010 i2b2/VA Challenge on Concepts, Assertions, and Relations in Clinical Text." JAMIA, 2011. — Motivates discontinuous entity support (clinical text has complex mention structures).

Software