5.7 The WORDNET lexical database of English

Knowledge Graph Embedding by Translating on Hyperplanes

Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915

Learning Entity and Relation Embeddings for Knowledge Graph Completion

Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

Huang, L., Zhao, X., & Huang, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957464 · Cited by 1,809

Siamese Recurrent Architectures for Learning Sentence Similarity

Mueller, J., & Thyagarajan, A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10350 · Cited by 935

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776

LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., et al. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/658 · Cited by 708

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1005 · Cited by 661

Detection and Resolution of Rumours in Social Media: A Survey

Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

Mostafazadeh, N., Chambers, N., He, X., Parikh, D., Batra, D., Vanderwende, L., Kohli, P., & Allen, J. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1098 · Cited by 598

Empath: Understanding Topic Signals in Large-Scale Text

Fast, E., Chen, B., & Bernstein, M. S. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858535 · Cited by 413

Knowledge Graph and Text Jointly Embedding

Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1167 · Cited by 406

Embeddings for Word Sense Disambiguation: An Evaluation Study

Iacobacci, I., Pilehvar, M. T., & Navigli, R. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1085 · Cited by 253

Query2doc: Query Expansion with Large Language Models

Wang, L., Yang, N., & Wei, F. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.585 · Cited by 214

TorusE: Knowledge Graph Embedding on a Lie Group

Ebisu, T., & Ichise, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11538 · Cited by 212

emrQA: A Large Corpus for Question Answering on Electronic Medical Records

Pampari, A., Raghavan, P., Liang, J., & Peng, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1258 · Cited by 191

A survey on aspect-based sentiment classification

Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

Yang, A., Wang, Q., Liu, J., Liu, K., Lyu, Y., Wu, H., She, Q., & Li, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1226 · Cited by 155

Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.416 · Cited by 149

End-to-end generation of multiple-choice questions using text-to-text transfer transformer models

Rodriguez-Torrealba, R., Garcia-Lopez, E., & Garcia-Cabot, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118258 · Cited by 118

Improving code readability models with textual features

Scalabrino, S., Linares-Vasquez, M., Poshyvanyk, D., & Oliveto, R. (2016) · 2016 IEEE 24th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2016.7503707 · Cited by 114

Knowledge Graph Representation with Jointly Structural and Textual Encoding

Xu, J., Qiu, X., Chen, K., & Huang, X. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/183 · Cited by 121

Demystifying Prompts in Language Models via Perplexity Estimation

Gonen, H., Iyer, S., Blevins, T., Smith, N., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.679 · Cited by 109

Task-Oriented Query Reformulation with Reinforcement Learning

Nogueira, R., & Cho, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1061 · Cited by 120

When Do You Need Billions of Words of Pretraining Data?

Zhang, Y., Warstadt, A., Li, X., & Bowman, S. R. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.90 · Cited by 85

Imitation Attacks and Defenses for Black-box Machine Translation Systems

Wallace, E., Stern, M., & Song, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.446 · Cited by 75

TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics

Yang, S., Tian, J., Zhang, H., Yan, J., He, H., & Jin, Y. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/268 · Cited by 75

On Multi-Relational Link Prediction with Bilinear Models

Wang, Y., Gemulla, R., & Li, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11738 · Cited by 65

Exploiting Image Generality for Lexical Entailment Detection

Kiela, D., Rimell, L., Vulić, I., & Clark, S. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.3115/v1/p15-2020 · Cited by 55

Text analysis for detecting terrorism-related articles on the web

Choi, D., Ko, B., Kim, H., & Kim, P. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2013.05.007 · Cited by 50

ReCode: Robustness Evaluation of Code Generation Models

Wang, S., Li, Z., Qian, H., Yang, C., Wang, Z., Shang, M., Kumar, V., Tan, S., Ray, B., Bhatia, P., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.773 · Cited by 42

Untitled work

(2019) · doi:10.18653/v1/d19-1292 · Cited by 36

Context-aware Path Ranking for Knowledge Base Completion

Mazumder, S., & Liu, B. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/166 · Cited by 35

Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension

Zhou, X., Luo, S., & Wu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6522 · Cited by 34

“A theme-rewriting approach for generating algebra word problems,”

Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1168 · Cited by 30

Analyzing Encoded Concepts in Transformer Language Models

Sajjad, H., Durrani, N., Dalvi, F., Alam, F., Khan, A., & Xu, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.225 · Cited by 24

Textual aggregation approaches in OLAP context: A survey

Bouakkaz, M., Ouinten, Y., Loudcher, S., & Strekalova, Y. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.06.005 · Cited by 16

TwitchChat: A Dataset for Exploring Livestream Chat

Ringer, C., Nicolaou, M., & Walker, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v16i1.7439 · Cited by 11

Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions

Chang, H.-S., & McCallum, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.554 · Cited by 11

LISA: Lexically Intelligent Story Assistant

Sanghrajka, R., Hidalgo, D., Chen, P., & Kapadia, M. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v13i1.12956 · Cited by 10

Unlearnable examples: protecting open-source software from unauthorized neural code learning

Ji, Z., Ma, P., & Wang, S. (2022) · International Conferences on Software Engineering and Knowledge Engineering · doi:10.18293/seke2022-066 · Cited by 5

The Value of Paraphrase for Knowledge Base Predicates

Xue, B., Hu, S., Zou, L., & Cheng, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6475 · Cited by 4

Box-To-Box Transformations for Modeling Joint Hierarchies

Dasgupta, S. S., Li, X. L., Boratko, M., Zhang, D., & McCallum, A. (2021) · Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021) · doi:10.18653/v1/2021.repl4nlp-1.28 · Cited by 2