---
title: "Singapore — research institutions, scientists and papers"
description: "Singapore: 8 institutions with papers in our library in 1 agglomerations, 78 people with a page, 142 works, 91,887 citations."
canonical: "https://research.elamur.ai/free/en/countries/singapore/"
---
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# Singapore

Country 8 institutions in 1 agglomeration · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**8** institutions with a page](https://md.research.elamur.ai/free/en/countries/singapore/#institutions) · [**1** agglomeration](https://md.research.elamur.ai/free/en/countries/singapore/#agglomerations) · [**78** people with a page](https://md.research.elamur.ai/free/en/countries/singapore/#authors) · [**142** works in our library](https://md.research.elamur.ai/free/en/countries/singapore/#works) · [**91,887** citations of their works](https://md.research.elamur.ai/free/en/countries/singapore/#works)

## About

### Profile

Singapore appears on this site as a compact research landscape centred on a single city region, [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/). Every institution connected with the country sits there. The group is small but mixed: four universities, two research institutes, one government research body and one nonprofit laboratory.

The universities are the [National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/), [Nanyang Technological University](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/), the [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) and the [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/). The institutes are the [Institute for Infocomm Research](https://md.research.elamur.ai/free/en/labs/institute-for-infocomm-research/) and [Temasek Laboratories @ NTU](https://md.research.elamur.ai/free/en/labs/temasek-laboratories-ntu/). The government body is the [Agency for Science, Technology and Research](https://md.research.elamur.ai/free/en/government/agency-for-science-technology-and-research/), and the nonprofit is [DSO National Laboratories](https://md.research.elamur.ai/free/en/ngos/dso-national-laboratories/).

The collection here is built around the study of language, so Singapore's presence is seen through computational linguistics and neighbouring fields. Most of its cited output belongs to [natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) and linguistics. Smaller shares fall in machine translation, machine learning, and mathematics and statistics, with single works touching law, education and computing.

Two large universities hold most of the works. The research institutes and the government agency also carry substantial weight. Cited research therefore reaches the library through several kinds of organisation, not only through teaching institutions.

### History

The research shown on this site begins in the 1990s with a study of machine translation. Verb Semantics and Lexical Selection (1994), by Zhibiao Wu and Martha Palmer, is linked to the National University of Singapore. It examines how a translation system settles on a target verb when English verbs are rendered into Mandarin. It remains one of the most cited items connected with the country.

The early 2000s bring a sustained line on coreference resolution, the task of deciding which expressions in a text point to the same entity. This line runs through the National University of Singapore, DSO National Laboratories, the Institute for Infocomm Research and the Agency for Science, Technology and Research. Relation extraction, transliteration and statistical language modelling appear in the same years. This first phase rests on feature-based machine learning and probabilistic models.

From the middle of the 2010s the picture widens sharply. Neural methods arrive in work on essay scoring, natural language inference and parsing. A cluster of research on sentiment, emotion and sarcasm follows, much of it multimodal, combining text with audio and video. This cluster draws on Nanyang Technological University, the National University of Singapore, the Singapore University of Technology and Design, the government agency and Temasek Laboratories @ NTU.

The 2020s bring a turn towards large pretrained models. Much of the newest work consists of surveys of prompting, retrieval augmentation and hallucination. It also includes studies of multitask instruction training, adapter-based tuning, vision-language models, and benchmarks for multimodal understanding and code generation. Across these periods, the country's research on this site moves from feature engineering, through neural architectures, to the study and evaluation of large language models.

### Research profile

The largest topic among the works here is [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). Its most cited item is [Pre-train, Prompt, and Predict](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) (2021). This systematic survey covers prompting methods, in which a pretrained model is steered through crafted inputs rather than fitted with new task-specific layers.

Close to it stands [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/) (2021), linked to Nanyang Technological University and the Institute for Infocomm Research. It studies how training on many prompted tasks lets a model handle tasks it has never seen. Its starting point is that such abilities otherwise emerge only at very large scale and depend strongly on wording. Crosslingual Generalization through Multitask Finetuning (2022) carries the idea across languages. LLM-Adapters (2023), shared by the Singapore University of Technology and Design and the Singapore Management University, offers design advice and a toolkit for low-cost adaptation of large models.

Several surveys map the field around large models. Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023) sets out three frameworks for joining the two. [Siren's Song in the AI Ocean](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) (2023) sorts hallucination into input-conflicting, context-conflicting and fact-conflicting kinds. [A Survey on RAG Meeting LLMs](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) (2024), with Tat-Seng Chua among its authors, reviews retrieval-augmented models without proposing a new system. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023) tests a chat model across standard tasks.

The second largest topic is [sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/), where Singapore's works form a dense, connected body. The [Tensor Fusion Network](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/) (2017) models sentiment by combining separate subnetworks for language, vision and sound. Context-Dependent Sentiment Analysis in User-Generated Videos (2017) holds that nearby utterances in a clip tend to share sentiment, so context should inform each prediction.

Two dataset papers followed. [MELD](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/) (2018) builds a multi-party conversation corpus from the EmotionLines material taken from the sitcom Friends. The CMU-MOSEI paper (2018) gives researchers an openly released, sizeable resource for studying affect across modalities. Conversational Memory Network (2018) and DialogueGCN (2019) model emotion across the turns of a dialogue.

Sarcasm forms a side branch of this line. CASCADE (2018) draws on context from Reddit discussions, and Reasoning with Sarcasm by Reading In-Between (2018) proposes an intra-attention recurrent network. Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020) and work on stance detection extend the line to finer-grained opinion analysis. Much of this work also belongs to the [vision and language](https://md.research.elamur.ai/free/en/topics/multimodal/) topic, alongside InstructBLIP (2023), MMBench (2023) and ChartQA (2022).

The third major strand is [coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/), the oldest continuous line here. A Machine Learning Approach to Coreference Resolution of Noun Phrases (2001) offers a resolver trained end to end that handles noun phrases in general. Later works add a twin-candidate "competition learning" model (2003), an entity-mention model built with inductive logic programming (2008), the modular BART toolkit (2008), and an adaptive ensemble for domain adaptation (2012). Discourse analysis and reasoning sit close by, and both are well represented among the works.

Information extraction is a further steady line. Exploring Various Knowledge in Relation Extraction (2005) builds a feature-rich classifier for relations between entities. Attention Guided Graph Convolutional Networks (2019) and Latent Structure Refinement for document-level extraction (2020) bring graph neural methods to the task. PAIE (2022) adapts prompt tuning to event argument extraction. A 2023 study argues that a large model performs poorly as a few-shot extractor but serves well as a reranker. [Chinese NER Using Lattice LSTM](https://md.research.elamur.ai/free/en/papers/zhang-2018-chinese-ner-lattice-lstm/) (2018) keeps characters at the core of a tagger while drawing on word information.

Syntax and multilingual work round out the profile. Fast and Accurate Shift-Reduce Constituent Parsing (2013) carries transition-based methods over to constituent trees. [Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/wang-2017-universal-dependencies-parsing-colloquial-singaporean/) (2017), linked to DSO National Laboratories, asks why English parsers break down on Singlish, a creole rooted in English that borrows from Hokkien and other languages. In translation, A Joint Source-Channel Model for Machine Transliteration (2004) maps spellings directly between scripts without a phonemic layer. IndicTrans2 (2023) works towards open translation models for the languages of India.

Smaller topics include grammatical error correction and learner language. These are represented by A Neural Approach to Automated Essay Scoring (2016) and a later attention-based essay-scoring model (2017). Safety, code generation and legal AI also appear.

### Leading scientists

[Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) is the first name among Singapore's people on this site, connected to the National University of Singapore and the Singapore University of Technology and Design. Zhang's works here include the lattice LSTM for Chinese named entity recognition, written with Jie Yang, and the 2023 hallucination survey.

[Soujanya Poria](https://md.research.elamur.ai/free/en/authors/soujanya-poria/) is at Nanyang Technological University and is also tied to the Singapore University of Technology and Design, the government agency and Temasek Laboratories @ NTU. Poria is the central figure of the multimodal sentiment line, as an author of the Tensor Fusion Network, the video sentiment study, MELD, CMU-MOSEI and DialogueGCN.

[Hwee Tou Ng](https://md.research.elamur.ai/free/en/authors/hwee-tou-ng/) is at the National University of Singapore and is also connected with DSO National Laboratories. Ng co-wrote the 2001 coreference paper with Wee Meng Soon and Daniel Chung Yong Lim. Ng also co-wrote the 2016 neural essay-scoring paper with Kaveh Taghipour, linking the coreference work with research on learner language.

[Erik Cambria](https://md.research.elamur.ai/free/en/authors/erik-cambria/), connected to the National University of Singapore and Nanyang Technological University, co-wrote the context-dependent study of sentiment in user videos. That work places Cambria within the affective computing cluster.

[Bo Li](https://md.research.elamur.ai/free/en/authors/bo-li/) is connected to Nanyang Technological University. [Lei Wang](https://md.research.elamur.ai/free/en/authors/lei-wang/) is connected to the Singapore Management University, where works such as LLM-Adapters, PAIE and the extraction reranking study appear.

[Yee Whye Teh](https://md.research.elamur.ai/free/en/authors/yee-whye-teh/), at the National University of Singapore, wrote [A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes](https://md.research.elamur.ai/free/en/papers/teh-2006-hierarchical-bayesian-language-model-based/) (2006). This single-author work builds a hierarchical Bayesian language model on Pitman-Yor processes.

[Min Zhang](https://md.research.elamur.ai/free/en/authors/min-zhang/) is connected to the Institute for Infocomm Research and the Agency for Science, Technology and Research. Between them, these two bodies hold much of the country's work on relation extraction, coreference and transliteration.

[Jinlan Fu](https://md.research.elamur.ai/free/en/authors/jinlan-fu/), at the National University of Singapore, co-wrote the prompting survey. It is the most cited language-model work connected with the country.

[Shafiq Joty](https://md.research.elamur.ai/free/en/authors/shafiq-joty/) is connected to Nanyang Technological University, alongside colleagues such as M Saiful Bari. The university's works here range from multimodal emotion to multitask prompted training and large-model surveys.

[Devamanyu Hazarika](https://md.research.elamur.ai/free/en/authors/devamanyu-hazarika/), at the National University of Singapore, is an author of MELD, the video sentiment study, the Conversational Memory Network for dyadic dialogue videos, and CASCADE for sarcasm detection.

[Jian Su](https://md.research.elamur.ai/free/en/authors/jian-su/), at the Institute for Infocomm Research and the government agency, co-wrote Exploring Various Knowledge in Relation Extraction with GuoDong Zhou and others. Su belongs to the institute's long line on extraction and coreference, alongside Xiaofeng Yang.

### Landmark works

The most cited work connected with Singapore is [MIMIC-III](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/) (2016), linked to the Institute for Infocomm Research and the government agency. It is a data descriptor for an openly accessible intensive-care database, the successor to MIMIC-II.

The 2021 prompting survey comes next. It describes a shift away from task-specific layers and fine-tuning towards prompting pretrained models. [Verb Semantics and Lexical Selection](https://md.research.elamur.ai/free/en/papers/wu-1994-verb-semantics-lexical-selection/) follows and is the oldest of the most cited works. The Tensor Fusion Network and MELD lead the multimodal emotion group. Multitask Prompted Training, the knowledge-graph roadmap and CMU-MOSEI come after them.

[Is BERT Really Robust?](https://md.research.elamur.ai/free/en/papers/jin-2019-bert-really-robust-strong-baseline/) (2019), linked to the government agency and with Joey Tianyi Zhou among its authors, introduced TextFooler. This black-box attack alters text while seeing only the scores a model outputs. It targets text classification and entailment.

The 2001 coreference paper by Soon, Ng and Lim is the most cited of the coreference line. [Exploring Various Knowledge in Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhou-2005-exploring-various-knowledge-relation-extraction/) holds the same place for extraction. Among recent works, InstructBLIP starts from BLIP-2 to study instruction tuning for vision-language models. MMBench, with Yuanhan Zhang among its authors, proposes a more careful way of evaluating such models.

### Labs and units

The National University of Singapore holds the most works in our library. Its research spans prompting, coreference, multimodal sentiment, Bayesian language modelling, essay scoring, retrieval-augmented models and the MMBench benchmark.

Nanyang Technological University is close behind and shares much of the multimodal emotion cluster. It also adds multitask prompted training, the knowledge-graph roadmap, InstructBLIP and the hallucination survey.

The Singapore University of Technology and Design has a smaller but frequently cited body of work. It covers the lattice LSTM, DialogueGCN, graph-based relation extraction, Latent Structure Refinement, aspect sentiment triplets and essay scoring. Wei Lu, Zhijiang Guo, Yan Zhang and Deepanway Ghosal are among its people.

The Singapore Management University contributes the match-LSTM model for natural language inference by Shuohang Wang and Jing Jiang. It also adds PAIE, the reranking study and BigCodeBench.

The Institute for Infocomm Research is the main home of the coreference and extraction tradition. GuoDong Zhou, Xiaofeng Yang, Haizhou Li, Nancy Chen and Anh Tuan Luu are among its people. The Agency for Science, Technology and Research shares several of its works and adds parsing, adversarial attack and sarcasm research.

DSO National Laboratories, with Hai Leong Chieu and Yoong Keok Lee, holds the Singlish parsing study, coreference domain adaptation and stance detection work. Temasek Laboratories @ NTU appears through two multimodal sentiment papers.

### Collaborations

Many works here are shared across Singapore's institutions, and these shared works show how closely its research groups are linked. The Tensor Fusion Network and the video sentiment study tie the National University of Singapore, Nanyang Technological University and Temasek Laboratories @ NTU together. MELD links both large universities with the Singapore University of Technology and Design. CMU-MOSEI joins Nanyang Technological University and the government agency.

The 2001 coreference paper connects the National University of Singapore with DSO National Laboratories. MIMIC-III and the 2005 relation extraction study are shared between the Institute for Infocomm Research and the Agency for Science, Technology and Research. Multitask prompted training links Nanyang Technological University with the institute. LLM-Adapters joins the Singapore University of Technology and Design with the Singapore Management University.

Several people appear at more than one institution, among them Soujanya Poria, Erik Cambria, Hwee Tou Ng, Jian Su, Min Zhang and Nancy Chen. Many of the most cited works have long author lists that reach beyond Singapore. The 1994 verb semantics paper is co-written with Martha Palmer, and the multitask training work includes Victor Sanh, Albert Webson and Colin Raffel. On this site, Singaporean research often enters the library as one part of a large collaborative effort.

### Timeline

In the 1990s, the country's presence on this site begins with the 1994 study of verb selection in English-to-Mandarin translation.

From 2001 to 2008, coreference resolution takes shape. The learned noun-phrase resolver appears in 2001, competition learning in 2003, and the entity-mention model and BART toolkit in 2008. Transliteration (2004), relation extraction (2005) and the Pitman-Yor language model (2006) appear alongside them.

From 2012 to 2016, domain adaptation for coreference (2012) and shift-reduce constituent parsing (2013) continue the earlier lines. The year 2016 brings MIMIC-III, the match-LSTM for inference and neural essay scoring.

In 2017 and 2018, multimodal sentiment grows quickly with the Tensor Fusion Network, video context modelling, MELD, CMU-MOSEI and conversational memory networks. Sarcasm detection, Singlish parsing, the lattice LSTM and a study of unintended memorisation in neural networks appear in the same years.

In 2019 and 2020, graph-based models spread through DialogueGCN, attention-guided graph networks and Latent Structure Refinement. TextFooler raises questions of robustness, and aspect sentiment triplet extraction refines opinion analysis.

From 2021 to 2024, large models dominate. The prompting survey and multitask prompted training (2021) are followed by PAIE, ChartQA and crosslingual finetuning (2022). The year 2023 brings InstructBLIP, MMBench, the knowledge-graph roadmap, the hallucination survey, IndicTrans2, LLM-Adapters and stance detection. The retrieval-augmentation survey and BigCodeBench follow in 2024.

## Where

## Agglomerations (1)

1. [**Singapore**8 institutions*91,887 citations*](https://md.research.elamur.ai/free/en/agglomerations/singapore/)

## Institutions (8)

### 🎓 Universities (4)

1. **[National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/)**University · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*61 works · 28,316 citations*
2. **[Nanyang Technological University](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/)**University · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*56 works · 22,783 citations*
3. **[Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/)**University · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*20 works · 8,193 citations*
4. **[Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/)**University · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*5 works · 1,487 citations*

### 🔬 Labs and research institutes (2)

1. **[Institute for Infocomm Research](https://md.research.elamur.ai/free/en/labs/institute-for-infocomm-research/)**Research institute · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*25 works · 13,128 citations*
2. **[Temasek Laboratories @ NTU](https://md.research.elamur.ai/free/en/labs/temasek-laboratories-ntu/)**Research institute · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*2 works · 3,126 citations*

### 🤝 Nonprofits (1)

1. **[DSO National Laboratories](https://md.research.elamur.ai/free/en/ngos/dso-national-laboratories/)**Nonprofit · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*4 works · 1,377 citations*

### 🏛️ Government research bodies (1)

1. **[Agency for Science, Technology and Research](https://md.research.elamur.ai/free/en/government/agency-for-science-technology-and-research/)**Government body · [Singapore](https://md.research.elamur.ai/free/en/agglomerations/singapore/), Singapore*14 works · 13,477 citations*

## People (78)

Authors with a page confirmed at its institutions, the most cited first.

1. [**Yue Zhang**National University of Singapore*32 papers here · 16,287 citations*](https://md.research.elamur.ai/free/en/authors/yue-zhang/)
2. [**Soujanya Poria**Nanyang Technological University*12 papers here · 11,353 citations*](https://md.research.elamur.ai/free/en/authors/soujanya-poria/)
3. [**Hwee Tou Ng**National University of Singapore*17 papers here · 8,981 citations*](https://md.research.elamur.ai/free/en/authors/hwee-tou-ng/)
4. [**Erik Cambria**National University of Singapore*14 papers here · 8,618 citations*](https://md.research.elamur.ai/free/en/authors/erik-cambria/)
5. [**Bo Li**Nanyang Technological University*6 papers here · 6,667 citations*](https://md.research.elamur.ai/free/en/authors/bo-li/)
6. [**Lei Wang**Singapore Management University*2 papers here · 6,598 citations*](https://md.research.elamur.ai/free/en/authors/lei-wang/)
7. [**Yee Whye Teh**National University of Singapore*2 papers here · 6,583 citations*](https://md.research.elamur.ai/free/en/authors/yee-whye-teh/)
8. [**Min Zhang**Institute for Infocomm Research*17 papers here · 6,076 citations*](https://md.research.elamur.ai/free/en/authors/min-zhang/)
9. [**Jinlan Fu**National University of Singapore*2 papers here · 5,807 citations*](https://md.research.elamur.ai/free/en/authors/jinlan-fu/)
10. [**Shafiq Joty**Nanyang Technological University*17 papers here · 5,347 citations*](https://md.research.elamur.ai/free/en/authors/shafiq-joty/)
11. [**Devamanyu Hazarika**National University of Singapore*7 papers here · 4,729 citations*](https://md.research.elamur.ai/free/en/authors/devamanyu-hazarika/)
12. [**Jian Su**Institute for Infocomm Research*18 papers here · 3,988 citations*](https://md.research.elamur.ai/free/en/authors/jian-su/)
13. [**Guodong Zhou**Institute for Infocomm Research*21 papers here · 3,835 citations*](https://md.research.elamur.ai/free/en/authors/guodong-zhou/)
14. [**Shuohang Wang**Singapore Management University*5 papers here · 3,748 citations*](https://md.research.elamur.ai/free/en/authors/shuohang-wang/)
15. [**Min-Yen Kan**National University of Singapore*11 papers here · 3,712 citations*](https://md.research.elamur.ai/free/en/authors/min-yen-kan/)
16. [**M Saiful Bari**Nanyang Technological University*8 papers here · 3,511 citations*](https://md.research.elamur.ai/free/en/authors/m-saiful-bari/)
17. [**Wei Lu**National University of Singapore*7 papers here · 3,255 citations*](https://md.research.elamur.ai/free/en/authors/wei-lu/)
18. [**Jing Jiang**Singapore Management University*3 papers here · 3,169 citations*](https://md.research.elamur.ai/free/en/authors/jing-jiang/)
19. [**Zhijiang Guo**Singapore University of Technology and Design*5 papers here · 2,834 citations*](https://md.research.elamur.ai/free/en/authors/zhijiang-guo/)
20. [**Yulin Chen**National University of Singapore*5 papers here · 2,723 citations*](https://md.research.elamur.ai/free/en/authors/yulin-chen/)
21. [**Jie Yang**Singapore University of Technology and Design*4 papers here · 2,424 citations*](https://md.research.elamur.ai/free/en/authors/jie-yang/)
22. [**Daniel Dahlmeier**National University of Singapore*2 papers here · 2,174 citations*](https://md.research.elamur.ai/free/en/authors/daniel-dahlmeier/)
23. [**Xavier Bresson**National University of Singapore*2 papers here · 2,103 citations*](https://md.research.elamur.ai/free/en/authors/xavier-bresson/)
24. [**Francis Bond**Nanyang Technological University*8 papers here · 2,085 citations*](https://md.research.elamur.ai/free/en/authors/francis-bond/)
25. [**Yuanhan Zhang**Nanyang Technological University*3 papers here · 2,057 citations*](https://md.research.elamur.ai/free/en/authors/yuanhan-zhang/)

[See all 78 people →](https://md.research.elamur.ai/free/en/countries/singapore/people/)

## Works in our library (142)

Works of its institutions, the most cited first.

1. [**MIMIC-III, a freely accessible critical care database**Alistair E. W. Johnson, Tom Pollard, Lu Shen, et al.*2016 · 8,530 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
2. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
3. [**Verb Semantics and Lexical Selection**Zhibiao Wu, Martha Palmer*1994 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/wu-1994-verb-semantics-lexical-selection/)
4. [**Tensor Fusion Network for Multimodal Sentiment Analysis**Amir Zadeh, Minghai Chen, Soujanya Poria, et al.*2017 · 2,107 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/)
5. [**MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations**Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, et al.*2018 · 1,668 citations*](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/)
6. [**Multitask Prompted Training Enables Zero-Shot Task Generalization**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,577 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
7. [**Unifying Large Language Models and Knowledge Graphs: A Roadmap**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,545 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/)
8. [**Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,506 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
9. [**Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment**Di Jin, Zhijing Jin, Joey Tianyi Zhou, et al.*2019 · 1,318 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-bert-really-robust-strong-baseline/)
10. [**A Machine Learning Approach to Coreference Resolution of Noun Phrases**Wee Meng Soon, Hwee Tou Ng, Daniel Chung Yong Lim*2001 · 1,287 citations*](https://md.research.elamur.ai/free/en/papers/soon-2001-machine-learning-approach-coreference-resolution/)
11. [**InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning**Wenliang Dai, Junnan Li, Dongxu Li, et al.*2023 · 1,051 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-instructblip-towards-general-purpose-vision/)
12. [**Context-Dependent Sentiment Analysis in User-Generated Videos**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,019 citations*](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/)
13. [**A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,006 citations*](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/)
14. [**Chinese NER Using Lattice LSTM**Yue Zhang, Jie Yang*2018 · 990 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-chinese-ner-lattice-lstm/)
15. [**Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 962 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
16. [**Exploring Various Knowledge in Relation Extraction**GuoDong Zhou, Jian Su, Jie Zhang, et al.*2005 · 937 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2005-exploring-various-knowledge-relation-extraction/)
17. [**MMBench: Is Your Multi-modal Model an All-around Player?**Yuan Liu, Haodong Duan, Yuanhan Zhang, et al.*2023 · 907 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mmbench-multi-modal-model-all/)
18. [**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/)
19. [**Is ChatGPT a General-Purpose Natural Language Processing Task Solver?**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 818 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/)
20. [**ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 744 citations*](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/)
21. [**Crosslingual Generalization through Multitask Finetuning**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 692 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
22. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 668 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/)
23. [**A Neural Approach to Automated Essay Scoring**Kaveh Taghipour, Hwee Tou Ng*2016 · 652 citations*](https://md.research.elamur.ai/free/en/papers/taghipour-2016-neural-approach-automated-essay-scoring/)
24. [**Attention Guided Graph Convolutional Networks for Relation Extraction**Zhijiang Guo, Yan Zhang, Wei Lu*2019 · 597 citations*](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/)
25. [**A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes**Yee Whye Teh*2006 · 571 citations*](https://md.research.elamur.ai/free/en/papers/teh-2006-hierarchical-bayesian-language-model-based/)

[See all 142 works →](https://md.research.elamur.ai/free/en/countries/singapore/works/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

💡 Concepts 8[Discourse analysis · 5 works](https://md.research.elamur.ai/free/en/concepts/discourse-analysis/) · [Named entity recognition · 2 works](https://md.research.elamur.ai/free/en/concepts/named-entity-recognition/) · [Word sense disambiguation · 3 works](https://md.research.elamur.ai/free/en/concepts/word-sense-disambiguation/) · [Coreference resolution · 1 work](https://md.research.elamur.ai/free/en/concepts/coreference-resolution/) · [Context-free grammar · 2 works](https://md.research.elamur.ai/free/en/concepts/context-free-grammar/) · [Corpus linguistics · 1 work](https://md.research.elamur.ai/free/en/concepts/corpus-linguistics/) · [Part-of-speech tagging · 1 work](https://md.research.elamur.ai/free/en/concepts/part-of-speech-tagging/) · [Psycholinguistics · 1 work](https://md.research.elamur.ai/free/en/concepts/psycholinguistics/)

📚 Shelves 35[Computer Science · 141 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 138 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 129 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 128 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 30 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Psychology · 11 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Experimental and Cognitive Psychology · 9 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Information Systems · 12 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Social Sciences · 6 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Epidemiology · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/epidemiology/) · [Health Information Management · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/health-information-management/) · [Health Professions · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/) · [Medicine · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/) · [Sociology and Political Science · 3 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · [Mathematics · 4 works](https://md.research.elamur.ai/free/en/shelves/mathematics/)+20 more

🧭 Topics 33[Language models & pretraining · 28 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Sentiment & emotion · 20 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Vision & language · 11 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Coreference resolution · 19 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [Reasoning · 14 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Information extraction · 5 works](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Discourse analysis · 14 works](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Evaluation & benchmarks · 12 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Dialogue systems · 9 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Multilingual & cross-lingual NLP · 7 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Machine translation · 9 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Grammatical error correction & learner language · 3 works](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · [English · 8 works](https://md.research.elamur.ai/free/en/topics/english/) · [Named entity recognition · 2 works](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Question answering · 5 works](https://md.research.elamur.ai/free/en/topics/question-answering/)+18 more

## Works by year

### 2020s

[**2026** 6 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2026/) · [**2025** 3 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2025/) · [**2024** 8 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2024/) · [**2023** 30 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2023/) · [**2022** 8 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2022/) · [**2021** 6 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2021/) · [**2020** 9 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2020/)

### 2010s

[**2019** 8 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2019/) · [**2018** 12 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2018/) · [**2017** 7 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2017/) · [**2016** 7 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2016/) · [**2015** 1 work](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/) · [**2014** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2014/) · [**2013** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2013/) · [**2012** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2012/) · [**2011** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2011/) · [**2010** 4 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2010/)

### 2000s

[**2009** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2009/) · [**2008** 5 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2008/) · [**2007** 3 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2007/) · [**2006** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2006/) · [**2005** 3 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2005/) · [**2004** 5 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2004/) · [**2003** 2 works](https://md.research.elamur.ai/free/en/countries/singapore/year/2003/) · [**2002** 1 work](https://md.research.elamur.ai/free/en/papers/lee-2002-empirical-evaluation-knowledge-sources-learning/) · [**2001** 1 work](https://md.research.elamur.ai/free/en/papers/soon-2001-machine-learning-approach-coreference-resolution/)

### 1990s

[**1994** 1 work](https://md.research.elamur.ai/free/en/papers/wu-1994-verb-semantics-lexical-selection/)

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Updated 23 October 2026