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# Who cites «A Maximum Entropy Model for Prepositional Phrase Attachment»

74 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/aclbulk-1994-ratnaparkhi-maximum-entropy-model-prepositional-phrase/)

1. [Can LLMs Disambiguate Grounded Language? The Case of PP Attachment](https://aclanthology.org/2025.ranlp-1.20/) *— 2025 · Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era*
2. [Tree Transformer's Disambiguation Ability of Prepositional Phrase Attachment and Garden Path Effects](https://aclanthology.org/2024.acl-long.664/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2021-ratnaparkhi-resolving-prepositional-phrase-attachment-ambiguities/) *— our card · 2021*
4. [Ontology-Aware Token Embeddings for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/onto-2017-dasigi-ontology-aware-token-embeddings-prepositional/) *— our card · 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Prepositional Phrase Attachment over Word Embedding Products](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2017-madhyastha-prepositional-phrase-attachment-over-word/) *— our card · 2017*
6. [TAG Parsing with Neural Networks and Vector Representations of Supertags](https://aclanthology.org/D17-1180/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
7. [Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes](https://aclanthology.org/D16-1156/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
8. [A Knowledge-Intensive Model for Prepositional Phrase Attachment](https://aclanthology.org/P15-1036/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
9. [Prepositional Phrase Attachment Problem Revisited: how Verbnet can Help](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2015-bailey-prepositional-phrase-attachment-problem-revisited/) *— our card · 2015 · Proceedings of the 11th International Conference on Computational Semantics*
10. [Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2014-belinkov-exploring-compositional-architectures-word-vector/) *— our card · 2014 · Transactions of the Association for Computational Linguistics*
11. [Learning Task-specific Bilexical Embeddings](https://aclanthology.org/C14-1017/) *— 2014 · Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers*
12. [Exploiting Partial Annotations with EM Training](https://aclanthology.org/W12-1905/) *— 2012 · Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure*
13. [Improving Dependency Parsing with Semantic Classes](https://aclanthology.org/P11-2123/) *— 2011 · Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies*
14. [Parse Correction with Specialized Models for Difficult Attachment Types](https://aclanthology.org/D11-1113/) *— 2011 · Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing*
15. [Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language](https://arxiv.org/abs/1105.5444) *— 2011 · arXiv:1105.5444 (Journal Of Artificial Intelligence Research, Volume 11, pages 95-130, 1999)*
16. [Corpus Creation for New Genres: A Crowdsourced Approach to PP Attachment](https://aclanthology.org/W10-0702/) *— 2010 · Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk*
17. [Towards Semi-Automated Annotation for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2010-rosenthal-semi-automated-annotation-prepositional-phrase/) *— our card · 2010 · Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)*
18. [Human Evaluation of Article and Noun Number Usage: Influences of Context and Construction Variability](https://aclanthology.org/W09-3010/) *— 2009 · Proceedings of the Third Linguistic Annotation Workshop (LAW III)*
19. [Improving Dependency Parsing with Subtrees from Auto-Parsed Data](https://aclanthology.org/D09-1060/) *— 2009 · Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing*
20. [Prepositions in Applications: A Survey and Introduction to the Special Issue](https://aclanthology.org/J09-2001/) *— 2009 · Computational Linguistics*
21. [Improving Parsing and PP Attachment Performance with Sense Information](https://aclanthology.org/P08-1037/) *— 2008 · Proceedings of ACL-08: HLT*
22. [Experiments with a Higher-Order Projective Dependency Parser](https://aclanthology.org/D07-1101/) *— 2007 · Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)*
23. [Prepositional Phrase Attachment without Oracles](https://md.mova.elamur.ai/free/en/docs/papers/cl-2007-atterer-squibs-prepositional-phrase-attachment-without/) *— our card · 2007*
24. [A Progressive Feature Selection Algorithm for Ultra Large Feature Spaces](https://aclanthology.org/P06-1071/) *— 2006 · Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics*
25. [Competitive generative models with structure learning for NLP classification tasks](https://aclanthology.org/W06-1668/) *— 2006 · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing*
26. [How Bad is the Problem of PP-Attachment? A Comparison of English, German and Swedish](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-volk-how-bad-is-problem-pp/) *— our card · 2006 · Proceedings of the Third ACL-SIGSEM Workshop on Prepositions*
27. [The Benefit of Stochastic PP Attachment to a Rule-Based Parser](https://aclanthology.org/P06-2029/) *— 2006 · Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions*
28. [The Effect of Corpus Size in Combining Supervised and Unsupervised Training for Disambiguation](https://aclanthology.org/P06-2004/) *— 2006 · Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions*
29. [The Notion of Argument in Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/cl-2006-merlo-notion-argument-prepositional-phrase-attachment/) *— our card · 2006 · Computational Linguistics*
30. [Clustering Syntactic Positions with Similar Semantic Requirements](https://aclanthology.org/J05-1005/) *— 2005 · Computational Linguistics*
31. [Maximum Expected F-Measure Training of Logistic Regression Models](https://aclanthology.org/H05-1087/) *— 2005 · Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing*
32. [PP-Attachment Disambiguation Boosted by a Gigantic Volume of Unambiguous Examples](https://aclanthology.org/I05-1017/) *— 2005 · Second International Joint Conference on Natural Language Processing: Full Papers*
33. [PP-attachment Disambiguation using Large Context](https://aclanthology.org/H05-1035/) *— 2005 · Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing*
34. [Using the Web as an Implicit Training Set: Application to Structural Ambiguity Resolution](https://aclanthology.org/H05-1105/) *— 2005 · Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing*
35. [A Labelled Corpus for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2004-mitchell-labelled-corpus-prepositional-phrase-attachment/) *— our card · 2004*
36. [Thesauruses for Prepositional Phrase Attachment](https://aclanthology.org/W04-2410/) *— 2004 · Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004*
37. [Generalised PP-attachment Disambiguation Using Corpus-based Linguistic Diagnostics](https://aclanthology.org/E03-1079/) *— 2003 · 10th Conference of the European Chapter of the Association for Computational Linguistics*
38. [Information Extraction from Voicemail](https://aclanthology.org/P01-1039/) *— 2001 · Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics*
39. [A Class-based Probabilistic approach to Structural Disambiguation](https://aclanthology.org/C00-1029/) *— 2000 · COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics*
40. [A Classification Approach to Word Prediction](https://aclanthology.org/A00-2017/) *— 2000 · 1st Meeting of the North American Chapter of the Association for Computational Linguistics*
41. [An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2000-pantel-unsupervised-approach-prepositional-phrase-attachment/) *— our card · 2000 · Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics*
42. [An Unsupervised Model for Statistically Determining Coordinate Phrase Attachment](https://aclanthology.org/P99-1081/) *— 1999 · Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics*
43. [Hybrid Disambiguation of Prepositional Phrase Attachment and Interpretation](https://aclanthology.org/W99-0614/) *— 1999 · 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora*
44. [PP-Attachment: A Committee Machine Approach](https://aclanthology.org/W99-0628/) *— 1999 · 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora*
45. [A Comparison of Criteria for Maximum Entropy/ Minimum Divergence Feature Selection](https://aclanthology.org/W98-1512/) *— 1998 · Proceedings of the Third Conference on Empirical Methods for Natural Language Processing*
46. [A Connectionist Approach to Prepositional Phrase Attachment for Real World Texts](https://aclanthology.org/P98-2201/) *— 1998 · 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2*
47. [Generalizing Case Frames Using a Thesaurus and the MDL Principle](https://aclanthology.org/J98-2002/) *— 1998*
48. [Some Properties of Preposition and Subordinate Conjunction Attachments](https://aclanthology.org/P98-2234/) *— 1998 · 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2*
49. [Statistical Models for Unsupervised Prepositional Phrase Attachment](https://aclanthology.org/P98-2177/) *— 1998 · 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2*
50. [Memory-Based Learning: Using Similarity for Smoothing](https://aclanthology.org/P97-1056/) *— 1997 · 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics*
51. [Resolving PP attachment Ambiguities with Memory-Based Learning](https://aclanthology.org/W97-1016/) *— 1997 · CoNLL97: Computational Natural Language Learning*
52. [A Maximum Entropy Model for Part-Of-Speech Tagging](https://aclanthology.org/W96-0213/) *— 1996 · Conference on Empirical Methods in Natural Language Processing*
53. [The Measure of a Model](https://aclanthology.org/W96-0210/) *— 1996 · Conference on Empirical Methods in Natural Language Processing*
54. [Unsupervised Learning of Syntactic Knowledge: Methods and Measures](https://aclanthology.org/W96-0203/) *— 1996 · Conference on Empirical Methods in Natural Language Processing*
55. arnold-2025-steering-prepositional-phrases-language-models
56. bosch-2000-induced-rules-as-complex-features
57. bourigault-2006-acquisition-et-evaluation-sur-corpus
58. chen-1995-structural-ambiguity-conceptual-information-retrieval
59. coppola-2011-simple-semi-supervised-learning-prepositional
60. costa-2006-decomposition-kernels-natural-language-processing
61. do-2020-parsers-know-best-german-pp
62. ferraro-2013-virtual-manipulative-learning-log-linear
63. greenberg-2014-disambiguating-prepositional-phrase-attachment-sites
64. hempelmann-2005-evaluating-state-art-treebank-style
65. herwijnen-2003-learning-pp-attachment-filtering-prosodic
66. kayaalp-1997-statistical-decision-making-method-case
67. li-1996-probabilistic-disambiguation-method-based-psycholinguistic
68. lima-1997-assigning-grammatical-relations-back-off
69. mitchell-2002-comparison-machine-learning-algorithms-prepositional
70. patrick-2004-thin-parsing-balance-between-wide
71. sogaard-2011-graphical-models-pp-attachment
72. uramoto-1995-automatic-learning-knowledge-example-based
73. volk-2007-comparing-french-pp-attachment-english
74. zeldes-2009-quantifying-constructional-productivity-unseen-slot
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