---
title: "Australia — research institutions, scientists and papers"
description: "Australia: 19 institutions with papers in our library in 6 agglomerations, 61 people with a page, 152 works, 186,124 citations."
canonical: "https://research.elamur.ai/free/en/countries/australia/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/countries/australia/)

[home](https://md.research.elamur.ai/free/en/) / [countries](https://md.research.elamur.ai/free/en/countries/) / Australia

# Australia

Country 19 institutions in 6 agglomerations · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**19** institutions with a page](https://md.research.elamur.ai/free/en/countries/australia/#institutions) · [**6** agglomerations](https://md.research.elamur.ai/free/en/countries/australia/#agglomerations) · [**61** people with a page](https://md.research.elamur.ai/free/en/countries/australia/#authors) · [**152** works in our library](https://md.research.elamur.ai/free/en/countries/australia/#works) · [**186,124** citations of their works](https://md.research.elamur.ai/free/en/countries/australia/#works)

## About

### Profile

Australia appears on this site through a group of universities, research institutes and one national science agency whose scientists wrote papers collected here. Most of these organisations are universities. The others are two research institutes, [Data61](https://md.research.elamur.ai/free/en/labs/data61/) and [IBM Research - Australia](https://md.research.elamur.ai/free/en/labs/ibm-research-australia/), and the government body [Commonwealth Scientific and Industrial Research Organisation](https://md.research.elamur.ai/free/en/government/commonwealth-scientific-and-industrial-research-organisation/). The country's institutions sit in a few cities. [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/) and [Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/) together hold most of them. Canberra, [Brisbane](https://md.research.elamur.ai/free/en/agglomerations/brisbane/) and [Perth](https://md.research.elamur.ai/free/en/agglomerations/perth/) add smaller groups, and Darwin is represented by [Charles Darwin University](https://md.research.elamur.ai/free/en/universities/charles-darwin-university/).

Most of the work gathered here is language research. Linguistics and languages is the largest field among the Australian works, followed by natural language processing, machine translation, and AI and machine learning. Computing and mathematics appear more thinly. The most cited items are not always language papers. A scientific software library, a machine learning book, a survey of graph neural networks and an essay on explanation in AI lead the citation table. This describes Australian research as the library holds it, not the country's science as a whole.

### History

Among the works here, the earliest Australian contribution is [C4.5: Programs for Machine Learning](https://md.research.elamur.ai/free/en/papers/quinlan-1993-c45-programs-machine-learning/) from 1993. The book describes an algorithm, with accompanying software, that induces classification trees and rules from training cases whose classes are known, and the University of Sydney is credited with it. No other Australian work on the site comes before the next decade.

From 2004 a steadier line of language research begins. Stephen Clark and James R. Curran published [Parsing the WSJ Using CCG and Log-Linear Models](https://md.research.elamur.ai/free/en/papers/clark-2004-parsing-wsj-ccg-log-linear/) in 2004 and a longer treatment of wide-coverage CCG parsing in 2007, both credited to the University of Sydney. Work on noun compound interpretation followed in 2005, and in 2006 [NLTK: The Natural Language Toolkit](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/) appeared from Melbourne. In 2008 came the ACL Anthology Reference Corpus. Together these early works point to parsing, lexical semantics and shared tools and corpora.

From around 2010 to 2015 the Melbourne group around Timothy Baldwin became the most visible part of the collection. Its papers dealt with topic coherence, keyphrase extraction, dialogue acts in live chat, normalisation of social media text, language identification and word sense induction. In the same years Macquarie University ran the HOO shared tasks on grammatical error correction, and in 2015 Macquarie produced a transition-based dependency parser.

From 2016 the range widens. Melbourne took part in reporting the 2016 Conference on Machine Translation. The Australian National University and Macquarie University worked on image captioning and vision-and-language navigation, and Tim Miller wrote on explanation in AI. Monash University's line on graph-based and neural machine translation dates from 2018. Between 2019 and 2021 the collection adds the SciPy paper, the graph neural network survey, pretrained language models for tweets and for Vietnamese, and biomedical entity representations.

The most recent period, from 2023 on, centres on large language models: surveys, a roadmap for joining them with knowledge graphs, safety studies of jailbreaking and prompt injection, detection of machine-generated text, and new benchmarks for code and for frontier models. The latest items here, from 2026, concern Arabic sentiment analysis and Arabic pronunciation assessment.

### Research profile

Machine translation and evaluation are among the most frequent topics in the Australian works. The translation strand includes the WMT16 findings, the WMT 2017 biomedical translation task, [Iterative Back-Translation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/hoang-2018-iterative-back-translation-neural-machine/), graph-to-sequence learning, and a study of offline bilingual word vectors from UNSW Sydney. Evaluation runs through much of the collection. It covers SPICE, which scores image captions by what they mean rather than by word overlap; topic coherence measures; [Tangled up in BLEU](https://md.research.elamur.ai/free/en/papers/mathur-2020-tangled-up-bleu-reevaluating-evaluation/), which re-examines how automatic translation metrics are themselves judged; and later benchmarks such as BigCodeBench and Humanity's Last Exam.

[Language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) form a large group of their own. BERTweet and PhoBERT adapt pretrained models to English tweets and to Vietnamese. SapBERT trains a BERT encoder further on groups of synonymous biomedical terms so that different names for one concept end up close together. The 2023 works then step back and survey large language models in general, or describe how they might be combined with knowledge graphs. The [bias, fairness and safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) topic follows from this: studies from UNSW Sydney, Monash University and the Australian National University test how far ChatGPT and LLM-integrated applications can be pushed past their safeguards.

Syntax and parsing are an older strength. They include the CCG parsing work from Sydney and the non-monotonic dependency parser from Macquarie. Semitic languages, and Hebrew and Aramaic in particular, are a distinctive topic among the Australian works. They sit alongside Arabic work from UNSW Sydney on aspect-based sentiment and on pronunciation assessment. Sentiment and emotion appear through sarcasm detection and the Arabic sentiment study.

Dialogue and discourse form another line. The dialogue act classifiers for one-on-one and multi-party live chats from RMIT University relate to the speech act theory topic. Coreference resolution is represented by the RecipeRef and ChEMU-Ref corpora, which look at anaphora in recipes and in chemistry texts. Discourse relation recognition appears in later work from UNSW Sydney and the national science agency. Social media language, through normalisation, noisy text and language identification, is a strand tied closely to Melbourne and Data61. Vision and language enters through captioning, navigation and video question answering. [Knowledge graphs](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) enter through ConvKB and the LLM roadmap.

### Leading scientists

[Shirui Pan](https://md.research.elamur.ai/free/en/authors/shirui-pan/) is credited to Monash University and also appears with the University of Technology Sydney and Griffith University. Pan's work here covers the survey of graph neural networks and the roadmap for unifying large language models with knowledge graphs.

[Timothy Baldwin](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/) of The University of Melbourne has the largest set of papers among Australian authors on the site. Baldwin also appears with Data61 and RMIT University. The work ranges over langid.py, lexical normalisation of short messages, the variety of noisy social media sources, dialogue act classification, and the reassessment of translation metrics.

[Mark Johnson](https://md.research.elamur.ai/free/en/authors/mark-johnson/) of Macquarie University co-wrote the non-monotonic transition system for dependency parsing with Matthew Honnibal and is also associated with the national science agency.

[Fangyu Liu](https://md.research.elamur.ai/free/en/authors/fangyu-liu/), credited to Monash University, is first author of the self-alignment pretraining work on biomedical entity representations.

[Tim Miller](https://md.research.elamur.ai/free/en/authors/tim-miller/) of The University of Melbourne wrote the essay arguing that explainable AI should draw on what the social sciences know about how people explain things to one another.

[Trevor Cohn](https://md.research.elamur.ai/free/en/authors/trevor-cohn/) of The University of Melbourne co-wrote the graph-to-sequence model with Daniel Beck and Gholamreza Haffari, and the BLEU evaluation study with Nitika Mathur and Baldwin.

[Shervin Malmasi](https://md.research.elamur.ai/free/en/authors/shervin-malmasi/) of Macquarie University belongs to the Macquarie group, whose work here includes the VarDial evaluation campaign on closely related languages and dialects.

[Tianyu Liu](https://md.research.elamur.ai/free/en/authors/tianyu-liu/) and [Jing Jiang](https://md.research.elamur.ai/free/en/authors/jing-jiang/) are credited to the University of Technology Sydney. Jiang is also associated with the Australian National University, and the institution's works here include the large language model overview, Humanity's Last Exam and AMR-TST.

[Dat Quoc Nguyen](https://md.research.elamur.ai/free/en/authors/dat-quoc-nguyen/), credited to The University of Melbourne and also linked with Macquarie University, is behind BERTweet, PhoBERT and the ConvKB knowledge base completion model.

[Yufei Wang](https://md.research.elamur.ai/free/en/authors/yufei-wang/) of Monash University is a co-author of the LLM and knowledge graph roadmap. [Gholamreza Haffari](https://md.research.elamur.ai/free/en/authors/gholamreza-haffari/) of Monash University worked on iterative back-translation and graph-to-sequence learning for translation.

### Landmark works

The most cited Australian item on the site is [SciPy 1.0](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/), the 2019 description of the open-source Python library for scientific computing, with authors at the University of Sydney and Monash University. C4.5 comes next. After it is [A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/), which organises methods for learning on graph-shaped data such as citation networks, molecules and sensor networks. Its authors are at the University of Technology Sydney and Monash.

[Explanation in Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/miller-2017-explanation-artificial-intelligence-insights-social/) is the most cited single-author work from Melbourne. The 2023 [roadmap on large language models and knowledge graphs](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) proposes three frameworks for bringing the two together. NLTK describes a reworked, openly licensed set of Python modules and sample corpora for teaching and research. [Vision-and-Language Navigation](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/) defines the task of following natural-language route instructions in real environments.

Further down, [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) is a broad review that introduces no new model. It is shared across several Australian institutions. [BERTweet](https://md.research.elamur.ai/free/en/papers/nguyen-2020-bertweet-pre-trained-language-model/) addresses the gap between general pretrained models and the language of tweets. The [ConvKB paper](https://md.research.elamur.ai/free/en/papers/nguyen-2018-novel-embedding-model-knowledge-base/) runs a convolutional network over complete knowledge base triples. The [WMT16 findings](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/) report the shared tasks of that year's translation conference.

Among the older papers, [Automatic Evaluation of Topic Coherence](https://md.research.elamur.ai/free/en/papers/newman-2010-automatic-evaluation-topic-coherence/) proposed rating, as a task of its own, how well the highest-ranked words of a topic model hang together. [langid.py](https://md.research.elamur.ai/free/en/papers/lui-2012-langid-py-off-shelf-language/) offered a ready-to-use language identifier. The [non-monotonic dependency parser](https://md.research.elamur.ai/free/en/papers/honnibal-2015-improved-non-monotonic-transition-system/) allows a greedy parser to revise attachments it made earlier rather than being locked into them. [Cross-lingual Name Tagging and Linking for 282 Languages](https://md.research.elamur.ai/free/en/papers/pan-2017-cross-lingual-name-tagging-linking/) builds silver-standard training data so that names can be tagged without manual labels. More of the most cited works can be found on the [full list of works](https://md.research.elamur.ai/free/en/countries/australia/works/).

### Labs and units

[The University of Melbourne](https://md.research.elamur.ai/free/en/universities/the-university-of-melbourne/) has the broadest body of work among Australian institutions here. Its strands include social media text, language identification, translation evaluation, pretrained models and explainable AI, with Baldwin, Miller, Cohn, Nguyen, [Jey Han Lau](https://md.research.elamur.ai/free/en/authors/jey-han-lau/), [Steven Bird](https://md.research.elamur.ai/free/en/authors/steven-bird/), [Nitika Mathur](https://md.research.elamur.ai/free/en/authors/nitika-mathur/) and [Marco Lui](https://md.research.elamur.ai/free/en/authors/marco-lui/) among its people.

[The University of Sydney](https://md.research.elamur.ai/free/en/universities/the-university-of-sydney/) has the largest cited output, built mostly on SciPy and C4.5. Its language work includes CCG parsing, cross-lingual name tagging, ActivityNet-QA and ChatDev, which uses communicating agents for software development. [Joel Nothman](https://md.research.elamur.ai/free/en/authors/joel-nothman/), [Matthew Honnibal](https://md.research.elamur.ai/free/en/authors/matthew-honnibal/) and [Robert Rezetko](https://md.research.elamur.ai/free/en/authors/robert-rezetko/) are among its people.

[Monash University](https://md.research.elamur.ai/free/en/universities/monash-university/) combines graph learning, neural translation, biomedical entity representation, sarcasm detection and red-teaming of ChatGPT. [Macquarie University](https://md.research.elamur.ai/free/en/universities/macquarie-university/) contributes parsing, the HOO shared tasks, VarDial, the ACL Anthology corpus and captioning work, with [Robert Dale](https://md.research.elamur.ai/free/en/authors/robert-dale/) among its authors. The [University of Technology Sydney](https://md.research.elamur.ai/free/en/universities/university-of-technology-sydney/) is represented mainly by a few heavily cited surveys and benchmarks.

[UNSW Sydney](https://md.research.elamur.ai/free/en/universities/unsw-sydney/) brings bilingual embeddings, SLURP for spoken language understanding, LLM security studies, discourse relations and the Arabic work, with [Mostafa Shahin](https://md.research.elamur.ai/free/en/authors/mostafa-shahin/) and [Aditya Joshi](https://md.research.elamur.ai/free/en/authors/aditya-joshi/) among its people. [RMIT University](https://md.research.elamur.ai/free/en/universities/rmit-university/) holds the dialogue act and anaphora corpora with [Karin Verspoor](https://md.research.elamur.ai/free/en/authors/karin-verspoor/) and [Lawrence Cavedon](https://md.research.elamur.ai/free/en/authors/lawrence-cavedon/). The [Australian National University](https://md.research.elamur.ai/free/en/universities/australian-national-university/) is tied to the vision-and-language work of [Peter Anderson](https://md.research.elamur.ai/free/en/authors/peter-anderson/) and [Stephen Gould](https://md.research.elamur.ai/free/en/authors/stephen-gould/), and to UniMorph 4.0.

Data61 holds the topic coherence, social media, word sense induction, data augmentation and BigCodeBench papers. IBM Research - Australia appears through the WMT16 findings and the biomedical translation task led by [Antonio Jimeno Yepes](https://md.research.elamur.ai/free/en/authors/antonio-jimeno-yepes/). The national science agency shares DetectLLM and BigCodeBench with Data61 and adds a study of discourse relation recognition. [Griffith University](https://md.research.elamur.ai/free/en/universities/griffith-university/) appears through the LLM roadmap and the prompt injection study. [Queensland University of Technology](https://md.research.elamur.ai/free/en/universities/queensland-university-of-technology/), [The University of Western Australia](https://md.research.elamur.ai/free/en/universities/the-university-of-western-australia/) and Charles Darwin University each add a small group of works.

### Collaborations

Many Australian works are shared between institutions and cities. SciPy links the University of Sydney with Monash in Melbourne, and topic coherence and langid.py tie Data61 to The University of Melbourne. Individual researchers also connect the cities. Baldwin appears in Melbourne, at Data61 in Sydney and at RMIT. Shirui Pan connects Monash, the University of Technology Sydney and Griffith. Terry Yue Zhuo links Monash, Data61 and the national science agency. Jey Han Lau joins The University of Melbourne, Data61 and IBM Research - Australia. The LLM overview is shared by Sydney, Melbourne, the University of Technology Sydney, the Australian National University and the agency, and Humanity's Last Exam connects the University of Technology Sydney, RMIT and the Australian National University.

Links between universities and industry run mainly through IBM Research - Australia, which co-reports the WMT16 findings with The University of Melbourne. Data61 and the national science agency form a second bridge outside the universities, sharing the code benchmark and the detection work with authors also credited to Monash. Among the people of Australian institutions on this site are also co-authors such as [Mark Steedman](https://md.research.elamur.ai/free/en/authors/mark-steedman/), [Ion Androutsopoulos](https://md.research.elamur.ai/free/en/authors/ion-androutsopoulos/), [Quoc V. Le](https://md.research.elamur.ai/free/en/authors/quoc-v-le/) and [Julian McAuley](https://md.research.elamur.ai/free/en/authors/julian-mcauley/). Australia can be compared with other entries on the [list of countries](https://md.research.elamur.ai/free/en/countries/).

### Timeline

1993: C4.5, the decision-tree book credited to the University of Sydney, is the first Australian work on the site.

2004 to 2008: Statistical CCG parsing at Sydney, noun compound interpretation, NLTK from Melbourne, and the ACL Anthology Reference Corpus lay down a base in parsing, lexical semantics and shared resources.

2010 to 2015: Topic coherence, SemEval keyphrase extraction, chat dialogue acts, lexical normalisation, the HOO error correction tasks, langid.py, word sense induction, multilingual document identification, sarcasm detection and non-monotonic dependency parsing make up the main body of language work, much of it from Melbourne, Data61, RMIT and Macquarie.

2016 to 2018: The WMT16 findings, SPICE, vision-and-language navigation, guided captioning, explanation in AI, cross-lingual name tagging, VarDial, bilingual word vectors, the biomedical translation task, ConvKB, graph-to-sequence learning and iterative back-translation widen the range to translation, vision and knowledge bases.

2019 to 2022: SciPy, the graph neural network survey, ActivityNet-QA, BERTweet, PhoBERT, data augmentation for named entity recognition, the BLEU evaluation study, SLURP, SapBERT, the anaphora corpora and UniMorph 4.0 add software, pretrained models and new resources.

2023 to 2026: Large language models dominate, through surveys, the knowledge graph roadmap, ChatDev, jailbreaking, prompt injection, red teaming, DetectLLM, discourse relation studies, BigCodeBench, GPT-NER and Humanity's Last Exam. The Arabic sentiment and pronunciation work of 2026 is the most recent Australian research on this site.

## Where

## Agglomerations (6)

1. [**Melbourne**5 institutions*77,857 citations*](https://md.research.elamur.ai/free/en/agglomerations/melbourne/)
2. [**Sydney**6 institutions*88,187 citations*](https://md.research.elamur.ai/free/en/agglomerations/sydney/)
3. [**Brisbane**3 institutions*9,896 citations*](https://md.research.elamur.ai/free/en/agglomerations/brisbane/)
4. [**Canberra**2 institutions*7,849 citations*](https://md.research.elamur.ai/free/en/agglomerations/canberra/)
5. [**Toowoomba**1 institution*413 citations*](https://md.research.elamur.ai/free/en/agglomerations/toowoomba/)
6. [**Perth**1 institution*1,668 citations*](https://md.research.elamur.ai/free/en/agglomerations/perth/)

## Institutions (19)

### 🎓 Universities (15)

1. **[The University of Melbourne](https://md.research.elamur.ai/free/en/universities/the-university-of-melbourne/)**University · [Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/), Australia*60 works · 21,199 citations*
2. **[The University of Sydney](https://md.research.elamur.ai/free/en/universities/the-university-of-sydney/)**University · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*31 works · 67,940 citations*
3. **[Monash University](https://md.research.elamur.ai/free/en/universities/monash-university/)**University · [Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/), Australia*31 works · 54,201 citations*
4. **[Macquarie University](https://md.research.elamur.ai/free/en/universities/macquarie-university/)**University · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*17 works · 4,827 citations*
5. **[Australian National University](https://md.research.elamur.ai/free/en/universities/australian-national-university/)**University · [Canberra](https://md.research.elamur.ai/free/en/agglomerations/canberra/), Australia*10 works · 6,215 citations*
6. **[University of Technology Sydney](https://md.research.elamur.ai/free/en/universities/university-of-technology-sydney/)**University · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*4 works · 11,832 citations*
7. **[The University of Queensland](https://md.research.elamur.ai/free/en/universities/the-university-of-queensland/)**University · [Brisbane](https://md.research.elamur.ai/free/en/agglomerations/brisbane/), Australia*2 works · 4,399 citations*
8. **[Griffith University](https://md.research.elamur.ai/free/en/universities/griffith-university/)**University · [Brisbane](https://md.research.elamur.ai/free/en/agglomerations/brisbane/), Australia*4 works · 4,143 citations*
9. **[UNSW Sydney](https://md.research.elamur.ai/free/en/universities/unsw-sydney/)**University · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*8 works · 1,214 citations*
10. **[Deakin University](https://md.research.elamur.ai/free/en/universities/deakin-university/)**University · Burwood ([Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/)), Australia*2 works · 973 citations*
11. **[Charles Darwin University](https://md.research.elamur.ai/free/en/universities/charles-darwin-university/)**University · Darwin, Australia*2 works · 254 citations*
12. **[RMIT University](https://md.research.elamur.ai/free/en/universities/rmit-university/)**University · [Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/), Australia*6 works · 477 citations*
13. **[Queensland University of Technology](https://md.research.elamur.ai/free/en/universities/queensland-university-of-technology/)**University · [Brisbane](https://md.research.elamur.ai/free/en/agglomerations/brisbane/), Australia*3 works · 1,354 citations*
14. **[University of Southern Queensland](https://md.research.elamur.ai/free/en/universities/university-of-southern-queensland/)**University · [Toowoomba](https://md.research.elamur.ai/free/en/agglomerations/toowoomba/), Australia*2 works · 413 citations*
15. **[The University of Western Australia](https://md.research.elamur.ai/free/en/universities/the-university-of-western-australia/)**University · [Perth](https://md.research.elamur.ai/free/en/agglomerations/perth/), Australia*3 works · 1,668 citations*

### 🔬 Labs and research institutes (3)

1. **[Data61](https://md.research.elamur.ai/free/en/labs/data61/)**Research institute · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*9 works · 2,192 citations*
2. **[ARC Centre of Excellence in Cognition and its Disorders](https://md.research.elamur.ai/free/en/labs/arc-centre-of-excellence-in-cognition-and-its-disorders/)**Research institute · [Sydney](https://md.research.elamur.ai/free/en/agglomerations/sydney/), Australia*2 works · 182 citations*
3. **[IBM Research - Australia](https://md.research.elamur.ai/free/en/labs/ibm-research-australia/)**Research institute · [Melbourne](https://md.research.elamur.ai/free/en/agglomerations/melbourne/), Australia*2 works · 1,007 citations*

### 🏛️ Government research bodies (1)

1. **[Commonwealth Scientific and Industrial Research Organisation](https://md.research.elamur.ai/free/en/government/commonwealth-scientific-and-industrial-research-organisation/)**Government body · [Canberra](https://md.research.elamur.ai/free/en/agglomerations/canberra/), Australia*5 works · 1,634 citations*

## People (61)

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

1. [**Xuezhi Wang**RMIT University*11 papers here · 31,897 citations*](https://md.research.elamur.ai/free/en/authors/xuezhi-wang/)
2. [**Shirui Pan**Monash University*2 papers here · 12,846 citations*](https://md.research.elamur.ai/free/en/authors/shirui-pan/)
3. [**Timothy Baldwin**The University of Melbourne*37 papers here · 10,820 citations*](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/)
4. [**Mark Johnson**Macquarie University*22 papers here · 9,566 citations*](https://md.research.elamur.ai/free/en/authors/mark-johnson/)
5. [**Tim Miller**The University of Melbourne*2 papers here · 5,493 citations*](https://md.research.elamur.ai/free/en/authors/tim-miller/)
6. [**Trevor Cohn**The University of Melbourne*15 papers here · 5,280 citations*](https://md.research.elamur.ai/free/en/authors/trevor-cohn/)
7. [**Shervin Malmasi**Macquarie University*10 papers here · 5,061 citations*](https://md.research.elamur.ai/free/en/authors/shervin-malmasi/)
8. [**Tianyu Liu**University of Technology Sydney*10 papers here · 3,324 citations*](https://md.research.elamur.ai/free/en/authors/tianyu-liu/)
9. [**Dat Quoc Nguyen**The University of Melbourne*5 papers here · 3,313 citations*](https://md.research.elamur.ai/free/en/authors/dat-quoc-nguyen/)
10. [**Jing Jiang**University of Technology Sydney*4 papers here · 3,179 citations*](https://md.research.elamur.ai/free/en/authors/jing-jiang/)
11. [**Yufei Wang**Monash University*5 papers here · 3,121 citations*](https://md.research.elamur.ai/free/en/authors/yufei-wang/)
12. [**Gholamreza Haffari**Monash University*10 papers here · 2,235 citations*](https://md.research.elamur.ai/free/en/authors/gholamreza-haffari/)
13. [**Jey Han Lau**The University of Melbourne*8 papers here · 2,172 citations*](https://md.research.elamur.ai/free/en/authors/jey-han-lau/)
14. [**Steven Bird**The University of Melbourne*3 papers here · 2,152 citations*](https://md.research.elamur.ai/free/en/authors/steven-bird/)
15. [**Nigel Collier**Monash University*11 papers here · 2,083 citations*](https://md.research.elamur.ai/free/en/authors/nigel-collier/)
16. [**Peter Anderson**Australian National University*3 papers here · 1,881 citations*](https://md.research.elamur.ai/free/en/authors/peter-anderson/)
17. [**Stephen Gould**Australian National University*3 papers here · 1,881 citations*](https://md.research.elamur.ai/free/en/authors/stephen-gould/)
18. [**David Newman**Data61*3 papers here · 1,714 citations*](https://md.research.elamur.ai/free/en/authors/david-newman/)
19. [**Karin Verspoor**The University of Melbourne*7 papers here · 1,576 citations*](https://md.research.elamur.ai/free/en/authors/karin-verspoor/)
20. [**Nitika Mathur**The University of Melbourne*7 papers here · 1,544 citations*](https://md.research.elamur.ai/free/en/authors/nitika-mathur/)
21. [**Marco Lui**The University of Melbourne*4 papers here · 1,473 citations*](https://md.research.elamur.ai/free/en/authors/marco-lui/)
22. [**Robert Dale**Macquarie University*5 papers here · 1,388 citations*](https://md.research.elamur.ai/free/en/authors/robert-dale/)
23. [**Fajri Koto**The University of Melbourne*7 papers here · 1,360 citations*](https://md.research.elamur.ai/free/en/authors/fajri-koto/)
24. [**Aditya Joshi**Monash University*15 papers here · 1,208 citations*](https://md.research.elamur.ai/free/en/authors/aditya-joshi/)
25. [**Joel Nothman**The University of Sydney*2 papers here · 1,162 citations*](https://md.research.elamur.ai/free/en/authors/joel-nothman/)

[See all 61 people in Australia →](https://md.research.elamur.ai/free/en/countries/australia/people/)

## Works in our library (152)

Works of its institutions, the most cited first.

1. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python arXiv:1907.10121 (Nature Methods 17, 261 (2020))**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
2. [**C4.5: Programs for Machine Learning**J. Ross Quinlan*1993 · 23,783 citations*](https://md.research.elamur.ai/free/en/papers/quinlan-1993-c45-programs-machine-learning/)
3. [**A Comprehensive Survey on Graph Neural Networks arXiv:1901.00596**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,300 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
4. [**Explanation in Artificial Intelligence: Insights from the Social Sciences arXiv:1706.07269**Tim Miller*2017 · 5,364 citations*](https://md.research.elamur.ai/free/en/papers/miller-2017-explanation-artificial-intelligence-insights-social/)
5. [**The Cambridge Grammar of the English Language**Rodney Huddleston, Geoffrey K. Pullum*2002 · 4,254 citations*](https://md.research.elamur.ai/free/en/papers/huddleston-2002-cambridge-grammar-english-language/)
6. [**Embedding Entities and Relations for Learning and Inference in Knowledge Bases arXiv:1412.6575**Bishan Yang, Wen-tau Yih, Xiaodong He, et al.*2014 · 2,356 citations*](https://md.research.elamur.ai/free/en/papers/yang-2014-embedding-entities-relations-learning-inference/)
7. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,549 citations*
8. **[NLTK: The Natural Language Toolkit](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Steven Bird*2006 · 1,374 citations*
9. **[Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,352 citations*
10. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*
11. **[BERTweet: A pre-trained language model for English Tweets](https://md.research.elamur.ai/free/en/papers/nguyen-2020-bertweet-pre-trained-language-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen*2020 · 1,142 citations*
12. **[A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/nguyen-2018-novel-embedding-model-knowledge-base/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, et al.*2018 · 944 citations*
13. **[Findings of the 2016 Conference on Machine Translation](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/) [Conference on Machine Translation: Shared Task Papers](https://md.research.elamur.ai/free/en/venues/conference-on-machine-translation-shared-task-papers/)**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2016 · 938 citations*
14. **[Automatic Evaluation of Topic Coherence](https://md.research.elamur.ai/free/en/papers/newman-2010-automatic-evaluation-topic-coherence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Newman, Jey Han Lau, Karl Grieser, et al.*2010 · 825 citations*
15. [**An Improved Non-monotonic Transition System for Dependency Parsing**Matthew Honnibal, Mark Johnson*2015 · 751 citations*](https://md.research.elamur.ai/free/en/papers/honnibal-2015-improved-non-monotonic-transition-system/)
16. **[langid.py: An Off-the-shelf Language Identification Tool](https://md.research.elamur.ai/free/en/papers/lui-2012-langid-py-off-shelf-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Marco Lui, Timothy Baldwin*2012 · 696 citations*
17. **[Cross-lingual Name Tagging and Linking for 282 Languages](https://md.research.elamur.ai/free/en/papers/pan-2017-cross-lingual-name-tagging-linking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoman Pan, Boliang Zhang, Jonathan May, et al.*2017 · 668 citations*
18. [**Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models**Stephen Clark, James R. Curran*2007 · 626 citations*](https://md.research.elamur.ai/free/en/papers/clark-2007-wide-coverage-efficient-statistical-parsing/)
19. **[Lexical Normalisation of Short Text Messages: Makn Sens a #twitter](https://md.research.elamur.ai/free/en/papers/han-2011-lexical-normalisation-short-text-messages/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bo Han, Timothy Baldwin*2011 · 587 citations*
20. **[PhoBERT: Pre-trained language models for Vietnamese](https://md.research.elamur.ai/free/en/papers/nguyen-2020-phobert-pre-trained-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dat Quoc Nguyen, Anh Tuan Nguyen*2020 · 571 citations*
21. **[SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles](https://md.research.elamur.ai/free/en/papers/kim-2010-semeval-2010-task-5-automatic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Su Nam Kim, Olena Medelyan, Min-Yen Kan, et al.*2010 · 460 citations*
22. **[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/beck-2018-graph-sequence-learning-gated-graph/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Beck, Gholamreza Haffari, Trevor Cohn*2018 · 458 citations*
23. **[GPT-NER: Named Entity Recognition via Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*
24. **[Self-Alignment Pretraining for Biomedical Entity Representations](https://md.research.elamur.ai/free/en/papers/liu-2021-self-alignment-pretraining-biomedical-entity/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, et al.*2021 · 434 citations*
25. [**Offline bilingual word vectors, orthogonal transformations and the inverted softmax arXiv:1702.03859**Samuel L. Smith, David H. P. Turban, Steven Hamblin, et al.*2017 · 427 citations*](https://md.research.elamur.ai/free/en/papers/smith-2017-offline-bilingual-word-vectors-orthogonal/)

[See all 152 works in Australia →](https://md.research.elamur.ai/free/en/countries/australia/works/)

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 31 The field of science this work belongs to (OpenAlex fields).[Computer Science · 142 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 · 136 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 134 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 16 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 12 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Social Sciences · 12 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Decision Sciences · 2 works](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · [Mathematics · 2 works](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Computational Theory and Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · [Information Systems and Management · 1 work](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · [Safety Research · 8 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [History and Philosophy of Science · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history-and-philosophy-of-science/) · [Psychology · 11 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Biochemistry, Genetics and Molecular Biology · 5 works](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/)+16 more

🧭 Topics 34 Research subjects and languages the work is about.[Language models & pretraining · 20 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [English · 24 works](https://md.research.elamur.ai/free/en/topics/english/) · [Machine translation · 15 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Evaluation & benchmarks · 15 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Knowledge graphs & ontologies · 5 works](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) · [Sentiment & emotion · 13 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Vision & language · 3 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Syntax & grammar formalisms · 8 works](https://md.research.elamur.ai/free/en/topics/syntax-grammar/) · [Multilingual & cross-lingual NLP · 7 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Named entity recognition · 4 works](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Bias, fairness & safety · 5 works](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Question answering · 5 works](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Dependency parsing · 3 works](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) · [Dialogue systems · 8 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Coreference resolution · 5 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)+19 more

💡 Concepts 18 Key ideas and methods explained on our concept pages.[Machine learning · 1 work](https://md.research.elamur.ai/free/en/concepts/machine-learning/) · [Artificial intelligence · 1 work](https://md.research.elamur.ai/free/en/concepts/artificial-intelligence/) · [Large language models · 2 works](https://md.research.elamur.ai/free/en/concepts/large-language-models/) · [Knowledge graph · 3 works](https://md.research.elamur.ai/free/en/concepts/knowledge-graph/) · [Parsing · 2 works](https://md.research.elamur.ai/free/en/concepts/parsing/) · [chrF · 3 works](https://md.research.elamur.ai/free/en/concepts/chrf/) · [Knowledge representation and reasoning · 2 works](https://md.research.elamur.ai/free/en/concepts/knowledge-representation/) · [Topic modeling · 1 work](https://md.research.elamur.ai/free/en/concepts/topic-modeling/) · [Named entity recognition · 1 work](https://md.research.elamur.ai/free/en/concepts/named-entity-recognition/) · [GloVe · 1 work](https://md.research.elamur.ai/free/en/concepts/glove/) · [Retrieval-augmented generation · 1 work](https://md.research.elamur.ai/free/en/concepts/retrieval-augmented-generation/) · [Corpus linguistics · 1 work](https://md.research.elamur.ai/free/en/concepts/corpus-linguistics/) · [Natural language processing · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-processing/) · [Dependency parsing · 1 work](https://md.research.elamur.ai/free/en/concepts/dependency-parsing/) · [Word sense disambiguation · 1 work](https://md.research.elamur.ai/free/en/concepts/word-sense-disambiguation/)+3 more

## Works by year

### 2020s

[**2026** 5 works Works of Australia from 2026](https://md.research.elamur.ai/free/en/countries/australia/year/2026/) · [**2025** 5 works Works of Australia from 2025](https://md.research.elamur.ai/free/en/countries/australia/year/2025/) · [**2024** 13 works Works of Australia from 2024](https://md.research.elamur.ai/free/en/countries/australia/year/2024/) · [**2023** 20 works Works of Australia from 2023](https://md.research.elamur.ai/free/en/countries/australia/year/2023/) · [**2022** 6 works Works of Australia from 2022](https://md.research.elamur.ai/free/en/countries/australia/year/2022/) · [**2021** 8 works Works of Australia from 2021](https://md.research.elamur.ai/free/en/countries/australia/year/2021/) · [**2020** 10 works Works of Australia from 2020](https://md.research.elamur.ai/free/en/countries/australia/year/2020/)

### 2010s

[**2019** 9 works Works of Australia from 2019](https://md.research.elamur.ai/free/en/countries/australia/year/2019/) · [**2018** 9 works Works of Australia from 2018](https://md.research.elamur.ai/free/en/countries/australia/year/2018/) · [**2017** 14 works Works of Australia from 2017](https://md.research.elamur.ai/free/en/countries/australia/year/2017/) · [**2016** 10 works Works of Australia from 2016](https://md.research.elamur.ai/free/en/countries/australia/year/2016/) · [**2015** 5 works Works of Australia from 2015](https://md.research.elamur.ai/free/en/countries/australia/year/2015/) · [**2014** 4 works Works of Australia from 2014](https://md.research.elamur.ai/free/en/countries/australia/year/2014/) · [**2013** 2 works Works of Australia from 2013](https://md.research.elamur.ai/free/en/countries/australia/year/2013/) · [**2012** 5 works Works of Australia from 2012](https://md.research.elamur.ai/free/en/countries/australia/year/2012/) · [**2011** 3 works Works of Australia from 2011](https://md.research.elamur.ai/free/en/countries/australia/year/2011/) · [**2010** 6 works Works of Australia from 2010](https://md.research.elamur.ai/free/en/countries/australia/year/2010/)

### 2000s

[**2009** 1 work Works of Australia from 2009](https://md.research.elamur.ai/free/en/papers/honnibal-2009-evaluating-statistical-ccg-parser-wikipedia/) · [**2008** 3 works Works of Australia from 2008](https://md.research.elamur.ai/free/en/countries/australia/year/2008/) · [**2007** 4 works Works of Australia from 2007](https://md.research.elamur.ai/free/en/countries/australia/year/2007/) · [**2006** 2 works Works of Australia from 2006](https://md.research.elamur.ai/free/en/countries/australia/year/2006/) · [**2005** 3 works Works of Australia from 2005](https://md.research.elamur.ai/free/en/countries/australia/year/2005/) · [**2004** 3 works Works of Australia from 2004](https://md.research.elamur.ai/free/en/countries/australia/year/2004/) · [**2002** 1 work Works of Australia from 2002](https://md.research.elamur.ai/free/en/papers/huddleston-2002-cambridge-grammar-english-language/)

### 1990s

[**1993** 1 work Works of Australia from 1993](https://md.research.elamur.ai/free/en/papers/quinlan-1993-c45-programs-machine-learning/)

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Updated 24 October 2026