---
title: "Psychology — papers, page 2"
description: "Psychology: 593 papers of the Elamur Research library · page 2 of 7 · 26–125, the most cited first — our summary of each, its ideas, references and who…"
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---
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# 💭 Psychology

593 papers · page 2 of 7 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Mind and behaviour: perception, memory, learning, development, language in the mind.

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/psychology/) [1](https://md.research.elamur.ai/free/en/shelves/psychology/) **2** [3](https://md.research.elamur.ai/free/en/shelves/psychology/page/3/) … [7](https://md.research.elamur.ai/free/en/shelves/psychology/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/psychology/page/3/)

26. [**TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification**Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, et al.*2020 · 952 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2020-tweeteval-unified-benchmark-comparative-evaluation/)
27. [**Robust Sentiment Detection on Twitter from Biased and Noisy Data**Luciano Barbosa, Junlan Feng*2010 · 952 citations*](https://md.research.elamur.ai/free/en/papers/barbosa-2010-robust-sentiment-detection-twitter-biased/)
28. [**DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.*2019 · 941 citations*](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)
29. [**Dependency-Based Construction of Semantic Space Models**Sebastian Padó, Mirella Lapata*2007 · 906 citations*](https://md.research.elamur.ai/free/en/papers/pado-2007-dependency-based-construction-semantic-space/)
30. [**DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation**Deepanway Ghosal, Navonil Majumder, Soujanya Poria, et al.*2019 · 850 citations*](https://md.research.elamur.ai/free/en/papers/ghosal-2019-dialoguegcn-graph-convolutional-neural-network/)
31. [**Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis**Theresa Wilson, Janyce Wiebe, Paul Hoffmann*2009 · 824 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2009-recognizing-contextual-polarity-exploration-features/)
32. [**Quantifying Mental Health Signals in Twitter**Glen Coppersmith, Mark Dredze, Craig Harman*2014 · 821 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2014-quantifying-mental-health-signals-twitter/)
33. [**Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words**Saif Mohammad*2018 · 800 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-obtaining-reliable-human-ratings-valence/)
34. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 788 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
35. [**NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews**Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, et al.*2014 · 788 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2014-nrc-canada-2014-detecting-aspects/)
36. [**Enhanced Sentiment Learning Using Twitter Hashtags and Smileys**Dmitry Davidov, Oren Tsur, Ari Rappoport*2010 · 776 citations*](https://md.research.elamur.ai/free/en/papers/davidov-2010-enhanced-sentiment-learning-twitter-hashtags/)
37. [**Sarcasm as Contrast between a Positive Sentiment and Negative Situation**Ellen Riloff, Ashequl Qadir, Prafulla Surve, et al.*2013 · 752 citations*](https://md.research.elamur.ai/free/en/papers/riloff-2013-sarcasm-as-contrast-between-positive/)
38. [**Identifying Sarcasm in Twitter: A Closer Look**Roberto González-Ibáñez, Smaranda Muresan, Nina Wacholder*2011 · 742 citations*](https://md.research.elamur.ai/free/en/papers/ibanez-2011-identifying-sarcasm-twitter-closer-look/)
39. [**SemEval-2014 Task 9: Sentiment Analysis in Twitter**Sara Rosenthal, Alan Ritter, Preslav Nakov, et al.*2014 · 741 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2014-semeval-2014-task-9-sentiment/)
40. [**Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences**Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/)
41. [**NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets**Saif Mohammad, Svetlana Kiritchenko, Xiaodan Zhu*2013 · 660 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2013-nrc-canada-building-state-art/)
42. [**Discriminating Gender on Twitter**John D. Burger, John Henderson, George Kim, et al.*2011 · 631 citations*](https://md.research.elamur.ai/free/en/papers/burger-2011-discriminating-gender-twitter/)
43. [**Classifying Relations by Ranking with Convolutional Neural Networks**Cícero dos Santos, Bing Xiang, Bowen Zhou*2015 · 613 citations*](https://md.research.elamur.ai/free/en/papers/santos-2015-classifying-relations-ranking-convolutional-neural/)
44. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
45. [**Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos**Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, et al.*2018 · 539 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-conversational-memory-network-emotion-recognition/)
46. [**ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection**Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-icon-interactive-conversational-memory-network/)
47. [**Benchmarking Aggression Identification in Social Media**Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, et al.*2018 · 507 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2018-benchmarking-aggression-identification-social-media/)
48. [**Sentiment Analysis in the Era of Large Language Models: A Reality Check**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)
49. [**SemEval-2015 Task 10: Sentiment Analysis in Twitter**Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, et al.*2015 · 490 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2015-semeval-2015-task-10-sentiment/)
50. [**Multi-Prototype Vector-Space Models of Word Meaning**Joseph Reisinger, Raymond J. Mooney*2010 · 489 citations*](https://md.research.elamur.ai/free/en/papers/reisinger-2010-multi-prototype-vector-space-models/)
51. [**EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks**Muhammad Abdul-Mageed, Lyle Ungar*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/)
52. [**Learning Multilingual Subjective Language via Cross-Lingual Projections**Rada Mihalcea, Carmen Banea, Janyce Wiebe*2007 · 482 citations*](https://md.research.elamur.ai/free/en/papers/mihalcea-2007-learning-multilingual-subjective-language-cross/)
53. [**Inference-Time Intervention: Eliciting Truthful Answers from a Language Model**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 473 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/)
54. [**ASTD: Arabic Sentiment Tweets Dataset**Mahmoud Nabil, Mohamed Aly, Amir Atiya*2015 · 470 citations*](https://md.research.elamur.ai/free/en/papers/nabil-2015-astd-arabic-sentiment-tweets-dataset/)
55. [**Evaluating Large Language Models in Theory of Mind Tasks**Michal Kosinski*2023 · 465 citations*](https://md.research.elamur.ai/free/en/papers/kosinski-2023-evaluating-large-language-models-theory/)
56. [**Distributional Semantics in Technicolor**Elia Bruni, Gemma Boleda, Marco Baroni, et al.*2012 · 459 citations*](https://md.research.elamur.ai/free/en/papers/bruni-2012-distributional-semantics-technicolor/)
57. [**CARER: Contextualized Affect Representations for Emotion Recognition**Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, et al.*2018 · 448 citations*](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/)
58. [**Reading Level Assessment Using Support Vector Machines and Statistical Language Models**Sarah Schwarm, Mari Ostendorf*2005 · 447 citations*](https://md.research.elamur.ai/free/en/papers/schwarm-2005-reading-level-assessment-support-vector/)
59. [**Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.**Diana Maynard, Mark Greenwood*2014 · 434 citations*](https://md.research.elamur.ai/free/en/papers/maynard-2014-who-cares-about-sarcastic-tweets/)
60. [**CLPsych 2015 Shared Task: Depression and PTSD on Twitter**Glen Coppersmith, Mark Dredze, Craig Harman, et al.*2015 · 410 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2015-clpsych-2015-shared-task-depression/)
61. [**SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text**Ankush Chatterjee, Kedhar Nath Narahari, Meghana Joshi, et al.*2019 · 409 citations*](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-semeval-2019-task-3-emocontext/)
62. [**A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support**Ashish Sharma, Adam Miner, David Atkins, et al.*2020 · 408 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
63. [**Depression and Self-Harm Risk Assessment in Online Forums**Andrew Yates, Arman Cohan, Nazli Goharian*2017 · 401 citations*](https://md.research.elamur.ai/free/en/papers/yates-2017-depression-self-harm-risk-assessment/)
64. [**URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors**Patrick Littell, David R. Mortensen, Ke Lin, et al.*2017 · 394 citations*](https://md.research.elamur.ai/free/en/papers/littell-2017-uriel-lang2vec-representing-languages-as/)
65. [**Literal and Metaphorical Sense Identification through Concrete and Abstract Context**Peter Turney, Yair Neuman, Dan Assaf, et al.*2011 · 394 citations*](https://md.research.elamur.ai/free/en/papers/turney-2011-literal-metaphorical-sense-identification-through/)
66. [**Towards Emotional Support Dialog Systems**Siyang Liu, Chujie Zheng, Orianna Demasi, et al.*2021 · 391 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-emotional-support-dialog-systems/)
67. [**That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets**William Yang Wang, Diyi Yang*2015 · 391 citations*](https://md.research.elamur.ai/free/en/papers/wang-2015-that-s-so-annoying-lexical/)
68. [**Learning from Bullying Traces in Social Media**Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, et al.*2012 · 381 citations*](https://md.research.elamur.ai/free/en/papers/xu-2012-learning-bullying-traces-social-media/)
69. [**Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations**Peixiang Zhong, Di Wang, Chunyan Miao*2019 · 370 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2019-knowledge-enriched-transformer-emotion-detection/)
70. [**Metaphor Detection with Cross-Lingual Model Transfer**Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, et al.*2014 · 369 citations*](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
71. [**WASSA-2017 Shared Task on Emotion Intensity**Saif Mohammad, Felipe Bravo-Marquez*2017 · 368 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2017-wassa-2017-shared-task-emotion/)
72. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 361 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)
73. [**Directed Acyclic Graph Network for Conversational Emotion Recognition**Weizhou Shen, Siyue Wu, Yunyi Yang, et al.*2021 · 344 citations*](https://md.research.elamur.ai/free/en/papers/shen-2021-directed-acyclic-graph-network-conversational/)
74. [**Language Is Not All You Need: Aligning Perception with Language Models**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 319 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/)
75. [**Correcting ESL Errors Using Phrasal SMT Techniques**Chris Brockett, William B. Dolan, Michael Gamon*2006 · 319 citations*](https://md.research.elamur.ai/free/en/papers/brockett-2006-correcting-esl-errors-phrasal-smt/)
76. [**Faith and Fate: Limits of Transformers on Compositionality**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/)
77. [**Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)**Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.*2019 · 313 citations*](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/)
78. [**A Lexicographer-Friendly Association Score**Pavel Rychlý*2008 · 312 citations*](https://md.research.elamur.ai/free/en/papers/rychly-2008-logdice/)
79. [**On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition**Sowmya Vajjala, Detmar Meurers*2012 · 311 citations*](https://md.research.elamur.ai/free/en/papers/vajjala-2012-improving-accuracy-readability-classification-insights/)
80. [**Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation**Wang Ling, Tiago Luis, Luis Marujo, et al.*2015 · 301 citations*](https://md.research.elamur.ai/free/en/papers/ling-2015-finding-function-form-compositional-character/)
81. [**Predictive power of word surprisal for reading times is a linear function of language model quality**Adam Goodkind, Klinton Bicknell*2018 · 294 citations*](https://md.research.elamur.ai/free/en/papers/goodkind-2018-predictive-power-word-surprisal-reading/)
82. [**Black Holes and White Rabbits: Metaphor Identification with Visual Features**Ekaterina Shutova, Douwe Kiela, Jean Maillard*2016 · 292 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2016-black-holes-white-rabbits-metaphor/)
83. [**Emotion Intensities in Tweets**Saif Mohammad, Felipe Bravo-Marquez*2017 · 290 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2017-emotion-intensities-tweets/)
84. [**Detecting Sarcasm in Multimodal Social Platforms**Rossano Schifanella, Paloma de Juan, Joel Tetreault, et al.*2016 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schifanella-2016-detecting-sarcasm-multimodal-social-platforms/)
85. [**Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings**Han-Chin Shing, Suraj Nair, Ayah Zirikly, et al.*2018 · 288 citations*](https://md.research.elamur.ai/free/en/papers/shing-2018-expert-crowdsourced-machine-assessment-suicide/)
86. [**Metaphor as a Medium for Emotion: An Empirical Study**Saif Mohammad, Ekaterina Shutova, Peter Turney*2016 · 285 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2016-metaphor-as-medium-emotion-empirical/)
87. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 283 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
88. [**Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation**Svetlana Kiritchenko, Saif Mohammad*2017 · 282 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2017-best-worst-scaling-more-reliable/)
89. [**Lexical Influences on the Perception of Sarcasm**Roger Kreuz, Gina Caucci*2007 · 278 citations*](https://md.research.elamur.ai/free/en/papers/kreuz-2007-lexical-influences-perception-sarcasm/)
90. [**Similarity of Semantic Relations**Peter D. Turney*2006 · 277 citations*](https://md.research.elamur.ai/free/en/papers/turney-2006-similarity-semantic-relations/)
91. [**SemEval-2007 Task 14: Affective Text**Carlo Strapparava, Rada Mihalcea*2007 · 273 citations*](https://md.research.elamur.ai/free/en/papers/strapparava-2007-semeval-2007-task-14-affective/)
92. [**CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System**Zachary J. Mason*2004 · 270 citations*](https://md.research.elamur.ai/free/en/papers/mason-2004-cormet-computational-corpus-based-conventional/)
93. [**met*: A Method for Discriminating Metonymy and Metaphor by Computer**Dan Fass*1991 · 269 citations*](https://md.research.elamur.ai/free/en/papers/fass-1991-met-method-discriminating-metonymy-metaphor/)
94. [**SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!**Chhavi Sharma, Deepesh Bhageria, William Scott, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-semeval-2020-task-8-memotion/)
95. [**Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge**Todor Mihaylov, Anette Frank*2018 · 260 citations*](https://md.research.elamur.ai/free/en/papers/mihaylov-2018-knowledgeable-reader-enhancing-cloze-style/)
96. [**The Ups and Downs of Preposition Error Detection in ESL Writing**Joel R. Tetreault, Martin Chodorow*2008 · 260 citations*](https://md.research.elamur.ai/free/en/papers/tetreault-2008-ups-downs-preposition-error-detection/)
97. [**Experiments on the Automatic Induction of German Semantic Verb Classes**Schulte im Walde, Sabine*2006 · 259 citations*](https://md.research.elamur.ai/free/en/papers/walde-2006-experiments-automatic-induction-german-semantic/)
98. [**A Large Self-Annotated Corpus for Sarcasm**Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli*2018 · 258 citations*](https://md.research.elamur.ai/free/en/papers/khodak-2018-large-self-annotated-corpus-sarcasm/)
99. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
100. [**Metaphor Identification Using Verb and Noun Clustering**Ekaterina Shutova, Lin Sun, Anna Korhonen*2010 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2010-metaphor-identification-verb-noun-clustering/)
101. [**CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts**Ayah Zirikly, Philip Resnik, Özlem Uzuner, et al.*2019 · 246 citations*](https://md.research.elamur.ai/free/en/papers/zirikly-2019-clpsych-2019-shared-task-predicting/)
102. [**Learning to Distinguish Hypernyms and Co-Hyponyms**Julie Weeds, Daoud Clarke, Jeremy Reffin, et al.*2014 · 242 citations*](https://md.research.elamur.ai/free/en/papers/weeds-2014-learning-distinguish-hypernyms-co-hyponyms/)
103. [**Simplifying Lexical Simplification: Do We Need Simplified Corpora?**Goran Glavaš, Sanja Štajner*2015 · 241 citations*](https://md.research.elamur.ai/free/en/papers/glavas-2015-simplifying-lexical-simplification-do-we/)
104. [**GDEX: Automatically Finding Good Dictionary Examples in a Corpus**Adam Kilgarriff, Miloš Husák, Katy McAdam, et al.*2008 · 238 citations*](https://md.research.elamur.ai/free/en/papers/kilgarriff-2008-gdex-examples/)
105. [**Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text**Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, et al.*2020 · 234 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-corpus-creation-sentiment-analysis-code/)
106. [**Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies**Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, et al.*2021 · 228 citations*](https://md.research.elamur.ai/free/en/papers/dev-2021-harms-gender-exclusivity-challenges-non/)
107. [**Text Readability Assessment for Second Language Learners**Menglin Xia, Ekaterina Kochmar, Ted Briscoe*2016 · 222 citations*](https://md.research.elamur.ai/free/en/papers/xia-2016-text-readability-assessment-second-language/)
108. [**Putting it Simply: a Context-Aware Approach to Lexical Simplification**Or Biran, Samuel Brody, Noémie Elhadad*2011 · 222 citations*](https://md.research.elamur.ai/free/en/papers/biran-2011-putting-it-simply-context-aware/)
109. [**Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks**Paul Röttger, Bertie Vidgen, Dirk Hovy, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2022-two-contrasting-data-annotation-paradigms/)
110. [**Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding**Hang Zhang, Xin Li, Lidong Bing*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-video-llama-instruction-tuned-audio/)
111. [**Inclusive yet Selective: Supervised Distributional Hypernymy Detection**Stephen Roller, Katrin Erk, Gemma Boleda*2014 · 217 citations*](https://md.research.elamur.ai/free/en/papers/roller-2014-inclusive-yet-selective-supervised-distributional/)
112. [**Automatic Sarcasm Detection: A Survey**Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman*2016 · 214 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/)
113. [**Nonliteral understanding of number words**Justine T. Kao, Jean Y. Wu, Leon Bergen, et al.*2014 · 213 citations*](https://md.research.elamur.ai/free/en/papers/kao-2014-nonliteral-understanding-number-words/)
114. [**MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets**Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.*2021 · 212 citations*](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/)
115. [**HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion**Bharathi Raja Chakravarthi*2020 · 211 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-hopeedi-multilingual-hope-speech-detection/)
116. [**SemEval-2012 Task 1: English Lexical Simplification**Lucia Specia, Sujay Kumar Jauhar, Rada Mihalcea*2012 · 210 citations*](https://md.research.elamur.ai/free/en/papers/specia-2012-semeval-2012-task-1-english/)
117. [**A Classifier-Based Approach to Preposition and Determiner Error Correction in L2 English**Rachele De Felice, Stephen G. Pulman*2008 · 202 citations*](https://md.research.elamur.ai/free/en/papers/felice-2008-classifier-based-approach-preposition-determiner/)
118. [**Neural Metaphor Detection in Context**Ge Gao, Eunsol Choi, Yejin Choi, et al.*2018 · 198 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-neural-metaphor-detection-context/)
119. [**Learning a Lexical Simplifier Using Wikipedia**Colby Horn, Cathryn Manduca, David Kauchak*2014 · 198 citations*](https://md.research.elamur.ai/free/en/papers/horn-2014-learning-lexical-simplifier-wikipedia/)
120. [**Semi-Supervised Recognition of Sarcasm in Twitter and Amazon**Dmitry Davidov, Oren Tsur, Ari Rappoport*2010 · 198 citations*](https://md.research.elamur.ai/free/en/papers/davidov-2010-semi-supervised-recognition-sarcasm-twitter/)
121. [**A General Framework for Distributional Similarity**Julie Weeds, David Weir*2003 · 198 citations*](https://md.research.elamur.ai/free/en/papers/weeds-2003-general-framework-distributional-similarity/)
122. [**Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models**Haim Dubossarsky, Daphna Weinshall, Eitan Grossman*2017 · 196 citations*](https://md.research.elamur.ai/free/en/papers/dubossarsky-2017-outta-control-laws-semantic-change/)
123. [**Statistical Metaphor Processing**Ekaterina Shutova, Simone Teufel, Anna Korhonen*2013 · 196 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2013-statistical-metaphor-processing/)
124. [**OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification**Sowmya Vajjala, Ivana Lučić*2018 · 191 citations*](https://md.research.elamur.ai/free/en/papers/vajjala-2018-onestopenglish-corpus-new-corpus-automatic/)
125. [**Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection**Hongliang Pan, Zheng Lin, Peng Fu, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/pan-2020-modeling-intra-inter-modality-incongruity/)

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