[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-2/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «Building a Large Annotated Corpus of English: The Penn Treebank» · page 2

# Who cites «Building a Large Annotated Corpus of English: The Penn Treebank» · page 2

2515 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/bank-1993-marcus-building-large-annotated-corpus-english/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-7/) · [8](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-8/) · [9](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-9/) · [10](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-10/) · [11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-11/) · [12](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-12/) · [13](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-13/) · [14](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-14/) · [15](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-15/) · [16](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-16/) · [17](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-17/) · [18](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-18/) · [19](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-19/) · [20](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-20/) · [21](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-21/) · [22](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-22/) · [23](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-23/) · [24](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-24/) · [25](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-25/) · [26](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-26/)

1. [A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches](https://aclanthology.org/2023.eacl-main.44/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
2. [A Neural CRF-based Hierarchical Approach for Linear Text Segmentation](https://aclanthology.org/2023.findings-eacl.65/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
3. [A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification](https://arxiv.org/abs/2307.03378) *— 2023 · arXiv:2307.03378*
4. [A Side-by-side Comparison of Transformers for Implicit Discourse Relation Classification](https://aclanthology.org/2023.codi-1.2/) *— 2023 · Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)*
5. [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) *— 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)*
6. [An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models](https://arxiv.org/abs/2301.09211) *— 2023 · arXiv:2301.09211*
7. [An Extended Sequence Tagging Vocabulary for Grammatical Error Correction](https://aclanthology.org/2023.findings-eacl.119/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
8. [Auto-apprentissage et renforcement pour une analyse jointe sur données disjointes : étiquetage morpho-syntaxique et analyse syntaxique](https://aclanthology.org/2023.jeptalnrecital-short.9/) *— 2023 · Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 2 : travaux de recherche originaux – articles courts*
9. [Bridging Information-Theoretic and Geometric Compression in Language Models](https://aclanthology.org/2023.emnlp-main.762/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
10. [Conjunct Lengths in English, Dependency Length Minimization, and Dependency Structure of Coordination](https://md.mova.elamur.ai/free/en/docs/papers/przepiorkowski-2023-conjunct-lengths-english-dependency-length/) *— our card · 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [Connecting Symbolic Statutory Reasoning with Legal Information Extraction](https://aclanthology.org/2023.nllp-1.12/) *— 2023 · Proceedings of the Natural Legal Language Processing Workshop 2023*
12. [Constituency Parsing using LLMs](https://arxiv.org/abs/2310.19462) *— 2023 · arXiv:2310.19462*
13. [Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers](https://aclanthology.org/2023.acl-long.285/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment](https://aclanthology.org/2023.acl-long.146/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [DMDD: A Large-Scale Dataset for Dataset Mentions Detection](https://aclanthology.org/2023.tacl-1.64/) *— 2023 · Transactions of the Association for Computational Linguistics*
16. [DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions](https://aclanthology.org/2023.acl-long.573/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://aclanthology.org/2023.acl-long.736/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Do Transformers Parse while Predicting the Masked Word?](https://aclanthology.org/2023.emnlp-main.1029/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
19. [Don't Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection](https://aclanthology.org/2023.acl-long.469/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [Efficient Semiring-Weighted Earley Parsing](https://aclanthology.org/2023.acl-long.204/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2023.conll-babylm.1/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
22. [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://md.mova.elamur.ai/free/en/docs/papers/treebanks-2023-aoyama-gentle-genre-diverse-multilayer-challenge/) *— our card · 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
23. [Hexatagging: Projective Dependency Parsing as Tagging](https://md.mova.elamur.ai/free/en/docs/papers/acl-2023-amini-hexatagging-projective-dependency-parsing-as/) *— our card · 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
24. [Impact of Adversarial Training on Robustness and Generalizability of Language Models](https://aclanthology.org/2023.findings-acl.496/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
25. [Impact of Co-occurrence on Factual Knowledge of Large Language Models](https://aclanthology.org/2023.findings-emnlp.518/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
26. [Improving Low-resource RRG Parsing with Structured Gloss Embeddings](https://aclanthology.org/2023.fieldmatters-1.6/) *— 2023 · Proceedings of the Second Workshop on NLP Applications to Field Linguistics*
27. [Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions](https://aclanthology.org/2023.findings-emnlp.703/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
28. [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://aclanthology.org/2023.findings-acl.202/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
29. [LLM-Pruner: On the Structural Pruning of Large Language Models](https://arxiv.org/abs/2305.11627) *— 2023 · arXiv:2305.11627*
30. [LLM-enhanced Self-training for Cross-domain Constituency Parsing](https://aclanthology.org/2023.emnlp-main.508/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
31. [McGill BabyLM Shared Task Submission: The Effects of Data Formatting and Structural Biases](https://aclanthology.org/2023.conll-babylm.18/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
32. [MetaPro Online: A Computational Metaphor Processing Online System](https://aclanthology.org/2023.acl-demo.12/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
33. [NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints](https://aclanthology.org/2023.acl-long.528/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://nejlt.ep.liu.se/article/view/4725) *— 2023 · Northern European Journal of Language Technology*
35. [On ``Scientific Debt'''' in NLP: A Case for More Rigour in Language Model Pre-Training Research](https://aclanthology.org/2023.acl-long.477/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Parsing Early New High German: Benefits and limitations of cross-dialectal training](https://aclanthology.org/2023.tlt-1.6/) *— 2023 · Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023)*
37. [Personality Traits in Large Language Models](https://arxiv.org/abs/2307.00184) *— 2023 · arXiv:2307.00184*
38. [Physics of Language Models: Part 1, Learning Hierarchical Language Structures](https://arxiv.org/abs/2305.13673) *— 2023 · arXiv:2305.13673*
39. [PoS to UPoS Conversion and Creation of UPoS Tagged Resources for Assamese Language](https://aclanthology.org/2023.icon-1.38/) *— 2023 · Proceedings of the 20th International Conference on Natural Language Processing (ICON)*
40. [Pushing the Limits of ChatGPT on NLP Tasks](https://arxiv.org/abs/2306.09719) *— 2023 · arXiv:2306.09719*
41. [QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models](https://arxiv.org/abs/2310.08041) *— 2023 · arXiv:2310.08041*
42. [ReCode: Robustness Evaluation of Code Generation Models](https://aclanthology.org/2023.acl-long.773/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://arxiv.org/abs/2307.02477) *— 2023 · arXiv:2307.02477*
44. [Revisiting Entropy Rate Constancy in Text](https://aclanthology.org/2023.findings-emnlp.1039/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
45. [Semgrex and Ssurgeon, Searching and Manipulating Dependency Graphs](https://aclanthology.org/2023.tlt-1.7/) *— 2023 · Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023)*
46. [Sentence Identification with BOS and EOS Label Combinations](https://aclanthology.org/2023.findings-eacl.26/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
47. [Simple Hardware-Efficient PCFGs with Independent Left and Right Productions](https://aclanthology.org/2023.findings-emnlp.113/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
48. [Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training](https://aclanthology.org/2023.findings-emnlp.879/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
49. [Syntactic Substitutability as Unsupervised Dependency Syntax](https://aclanthology.org/2023.emnlp-main.144/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
50. [Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling](https://aclanthology.org/2023.repl4nlp-1.15/) *— 2023 · Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)*
51. [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://aclanthology.org/2023.eacl-main.139/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
52. [The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.mova.elamur.ai/free/en/docs/papers/braud-2023-disrpt-2023-shared-task-elementary/) *— our card · 2023 · Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)*
53. [The Dark Side of the Language: Pre-trained Transformers in the DarkNet](https://aclanthology.org/2023.ranlp-1.102/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
54. [The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources](https://aclanthology.org/2023.acl-long.841/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [The Sequence Notation: Catching Complex Meanings in Simple Graphs](https://aclanthology.org/2023.iwcs-1.20/) *— 2023 · Proceedings of the 15th International Conference on Computational Semantics*
56. [Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions](https://aclanthology.org/2023.acl-long.562/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language](https://aclanthology.org/2023.acl-long.668/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models](https://aclanthology.org/2023.conll-1.36/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
59. [Unified Syntactic Annotation of English in the CGEL Framework](https://aclanthology.org/2023.law-1.22/) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-2023-reynolds-unified-syntactic-annotation-english-cgel/) *— 2023 · LAW-XVII 2023 (Linguistic Annotation Workshop, ACL)*
60. [Unified Syntactic Annotation of English in the CGEL Framework](https://md.mova.elamur.ai/free/en/docs/papers/bank-2023-reynolds-unified-syntactic-annotation-english-cgel/) *— our card · 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
61. [What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization](https://aclanthology.org/2023.acl-long.587/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [What the DAAM: Interpreting Stable Diffusion Using Cross Attention](https://aclanthology.org/2023.acl-long.310/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [What's Hard in English RST Parsing? Predictive Models for Error Analysis](https://aclanthology.org/2023.sigdial-1.3/) *— 2023 · Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
64. [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://aclanthology.org/2023.acl-long.36/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://aclanthology.org/2023.eacl-main.227/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
66. [ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats](https://arxiv.org/abs/2307.09782) *— 2023 · arXiv:2307.09782*
67. [対称的な系列集合を用いた教師なし構文解析モデルの分岐バイアスの検証](https://www.anlp.jp/proceedings/annual_meeting/2023/#Q6-3) *— 2023 · 言語処理学会 第29回年次大会 発表論文集 (NLP2023)*
68. [A Part-of-Speech Tagger for Yiddish](https://arxiv.org/abs/2204.01175) *— 2022 · arXiv:2204.01175*
69. [A Personalized Dialogue Generator with Implicit User Persona Detection](https://aclanthology.org/2022.coling-1.29/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
70. [A Survey of Implicit Discourse Relation Recognition](https://arxiv.org/abs/2203.02982) *— 2022 · arXiv:2203.02982*
71. [A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models](https://aclanthology.org/2022.lrec-1.464/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
72. [AD for an Array Language with Nested Parallelism](https://arxiv.org/abs/2202.10297) *— 2022 · arXiv:2202.10297*
73. [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://aclanthology.org/2022.tacl-1.56/) *— 2022 · Transactions of the Association for Computational Linguistics*
74. [An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models](https://aclanthology.org/2022.emnlp-main.119/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
75. [An Empirical Study on the Transferability of Transformer Modules in Parameter-efficient Fine-tuning](https://aclanthology.org/2022.emnlp-main.726/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
76. [Analyzing Encoded Concepts in Transformer Language Models](https://aclanthology.org/2022.naacl-main.225/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
77. [Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering](https://aclanthology.org/2022.findings-emnlp.172/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
78. [Auto-correction dans un analyseur neuronal par transitions : un comportement factice ? (Self-correction in a transition-based neural parser : a spurious behaviour ?)](https://aclanthology.org/2022.jeptalnrecital-recital.2/) *— 2022 · Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 : 24e Rencontres Etudiants Chercheurs en Informatique pour le TAL (RECITAL)*
79. [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://aclanthology.org/2022.acl-long.171/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
80. [Can Explanations Be Useful for Calibrating Black Box Models?](https://aclanthology.org/2022.acl-long.429/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
81. [Challenges and Strategies in Cross-Cultural NLP](https://aclanthology.org/2022.acl-long.482/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
82. [Challenges to Open-Domain Constituency Parsing](https://aclanthology.org/2022.findings-acl.11/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
83. [Co-training an Unsupervised Constituency Parser with Weak Supervision](https://aclanthology.org/2022.findings-acl.101/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
84. [Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing](https://aclanthology.org/2022.findings-acl.112/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
85. [Controlled Text Generation Using Dictionary Prior in Variational Autoencoders](https://aclanthology.org/2022.findings-acl.10/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
86. [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://aclanthology.org/2022.tacl-1.23/) *— 2022 · Transactions of the Association for Computational Linguistics*
87. [Dependency Parsing as MRC-based Span-Span Prediction](https://aclanthology.org/2022.acl-long.173/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Dependency Parsing via Sequence Generation](https://aclanthology.org/2022.findings-emnlp.543/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
89. [Development of a Multilingual CCG Treebank via Universal Dependencies Conversion](https://aclanthology.org/2022.lrec-1.560/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
90. [Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections](https://aclanthology.org/2022.emnlp-main.806/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
91. [Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing](https://aclanthology.org/2022.coling-1.483/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
92. [Event knowledge in large language models: the gap between the impossible and the unlikely](https://arxiv.org/abs/2212.01488) *— 2022 · arXiv:2212.01488*
93. [Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation](https://aclanthology.org/2022.emnlp-main.181/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
94. [Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation](https://aclanthology.org/2022.acl-long.121/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation](https://aclanthology.org/2022.naacl-main.51/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
96. [GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing](https://aclanthology.org/2022.aacl-short.47/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
97. [Headed-Span-Based Projective Dependency Parsing](https://aclanthology.org/2022.acl-long.155/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [How well do LSTM language models learn filler-gap dependencies?](https://aclanthology.org/2022.scil-1.6/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
99. [Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking](https://aclanthology.org/2022.findings-acl.147/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
100. [Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion](https://aclanthology.org/2022.findings-emnlp.4/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-7/) · [8](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-8/) · [9](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-9/) · [10](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-10/) · [11](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-11/) · [12](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-12/) · [13](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-13/) · [14](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-14/) · [15](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-15/) · [16](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-16/) · [17](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-17/) · [18](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-18/) · [19](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-19/) · [20](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-20/) · [21](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-21/) · [22](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-22/) · [23](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-23/) · [24](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-24/) · [25](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-25/) · [26](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-26/)

- [← previous Who cites «Building a Large Annotated Corpus of English: The Penn Treebank» · page 19](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-19/)
- [next → Who cites «Building a Large Annotated Corpus of English: The Penn Treebank» · page 20](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bank-1993-marcus-building-large-annotated-corpus-english-20/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)