---
title: "Canada — research institutions, scientists and papers"
description: "Canada: 32 institutions with papers in our library in 13 agglomerations, 160 people with a page, 333 works, 271,725 citations."
canonical: "https://research.elamur.ai/free/en/countries/canada/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/countries/canada/)

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

# Canada

Country 32 institutions in 13 agglomerations · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**32** institutions with a page](https://md.research.elamur.ai/free/en/countries/canada/#institutions) · [**13** agglomerations](https://md.research.elamur.ai/free/en/countries/canada/#agglomerations) · [**160** people with a page](https://md.research.elamur.ai/free/en/countries/canada/#authors) · [**333** works in our library](https://md.research.elamur.ai/free/en/countries/canada/#works) · [**271,725** citations of their works](https://md.research.elamur.ai/free/en/countries/canada/#works)

## About

### Profile

Canada appears on this site as a network of universities, research institutes, a government laboratory, a hospital, a bank's research arm and a technology company. Together their scientists account for a substantial body of work in the library. Nearly all of it falls within artificial intelligence and the study of language by machine. That includes neural machine translation, language models, sentiment and emotion analysis, dialogue systems, discourse analysis and the evaluation of models. A few works reach into neighbouring areas: scientific software, graph learning, clinical research resources and the environmental cost of computing.

The institutions are spread across the country, but on this site the weight sits in two metropolitan areas. [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/) holds the largest share of cited work and [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/) is close behind. [Vancouver](https://md.research.elamur.ai/free/en/agglomerations/vancouver/), [Ottawa](https://md.research.elamur.ai/free/en/agglomerations/ottawa/) and the Waterloo area, grouped under [Kitchener](https://md.research.elamur.ai/free/en/agglomerations/kitchener/), form a second tier. Edmonton, Winnipeg, Kingston, Halifax, London and Sherbrooke each contribute a smaller presence. Universities make up most of the institutions. The independent institutes and the government body carry a share of the most cited work that is large for their size.

### History

Among the works here, Canadian research on language begins with lexical and textual semantics. In 1991 Jane Morris and [Graeme Hirst](https://md.research.elamur.ai/free/en/authors/graeme-hirst/) proposed lexical chains in [Lexical Cohesion Computed by Thesaural Relations](https://md.research.elamur.ai/free/en/papers/morris-1991-lexical-cohesion-computed-thesaural-relations/). These chains are sequences of related words, built on the notion of lexical cohesion, that point to how a text is structured. Later work extended this line. Dekang Lin's [Automatic Retrieval and Clustering of Similar Words](https://md.research.elamur.ai/free/en/papers/lin-1998-automatic-retrieval-clustering-similar-words/) (1998) defined an information-theoretic measure of word similarity. Peter Turney's [Thumbs Up or Thumbs Down?](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/) (2002) set out a three-stage unsupervised method for judging the semantic orientation of reviews. Alexander Budanitsky and Hirst compared WordNet-based relatedness measures in 2006.

Around 2010 two currents met. The lexical and affective tradition continued with an emotion lexicon gathered through crowdsourcing and with the lexicon-based sentiment calculator of 2011. At the same time a neural line was taking shape in Montreal and Toronto. [Word Representations: A Simple and General Method for Semi-Supervised Learning](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/) (2010) learned word representations from unlabelled text and then used them as extra features. In 2012 came a review of representation learning and the RNN Transducer. The years 2013 to 2015 show the most concentrated burst of highly cited work. It covers adversarial properties of image classifiers, attention-based translation, encoder-decoder analysis, visual attention for captioning, attention for speech and the alignment of books with films.

From 2016 onward the work spreads into dialogue corpora and dialogue evaluation, multi-hop question answering, graph neural networks and text-based game environments. From 2020 the emphasis moves toward pretrained transformers and their efficiency, adapters, Arabic language models, bias measurement and, by 2023, surveys and evaluations of large language models. The most recent works here, from 2024 and 2025, deal with long-context learning, hard benchmarks, stance detection with large models and very small multimodal models for edge devices.

### Research profile

The broadest topic among Canada's works in our library is [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). It runs from Montreal's early neural language research to the 2023 [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/), a systematic and regularly updated overview of the field. The same years brought a comprehensive overview of large language models, a taxonomy of hallucination types in [Siren's Song in the AI Ocean](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/), and [Llemma](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/), an open model built for mathematics. [The Curse of Recursion](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/) asked what happens when models are trained, generation after generation, on text produced by earlier models.

[Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) is the second large strand. Its centre is the 2014 pair from the Université de Montréal. The first is the RNNsearch architecture, which learns to align and translate jointly. The second is an analysis of when encoder-decoder translation succeeds and when it fails. Work on very large target vocabularies continued the line. The attention mechanism developed for translation was then carried over to image captioning and speech recognition.

[Evaluation and benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) form a third strand. It includes automatic scoring of dialogue responses, the SemEval shared tasks on stance and affect in tweets, a systematic test of ChatGPT on academic NLP benchmarks, the long-context benchmark LongICLBench, and [Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/). That last work responds to benchmarks such as MMLU nearing saturation.

[Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) are unusually prominent. The main contribution is Vancouver's Arabic work, above all [ARBERT & MARBERT](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/), transformers that cover both Modern Standard Arabic and regional dialects. A 2021 system from Sherbrooke classified Arabic text by country and province for the NADI shared task.

[Sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) is one of the oldest Canadian lines here. It runs from Turney's unsupervised review classifier through the emotion lexicon and SO-CAL to EmoNet, which used gated recurrent networks for fine-grained emotion detection. [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) is also a steady presence. Examples include the CODRA framework for rhetorical parsing and summarization of product reviews guided by discourse structure. Concordia contributes work on discourse connectives and on mapping relations between the RST-DT and PDTB 3.0 annotation schemes.

[Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) concentrate in Montreal. Work there includes hierarchical neural dialogue models, the Ubuntu Dialogue Corpus, the persona-based chit-chat of 2018 and the ConvAI2 competition. Smaller clusters cover coreference resolution, multilingual NLP, [question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) and summarization. There is also work on efficient models such as DeeBERT, which speeds up BERT inference by letting it exit early.

### Leading scientists

[Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/) of the Université de Montréal comes first. Bengio's work here spans semi-supervised word representations, the 2012 review [Representation Learning: A Review and New Perspectives](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/), and attention-based translation.

[Kyunghyun Cho](https://md.research.elamur.ai/free/en/authors/kyunghyun-cho/), also at the Université de Montréal, has the broadest output among the first names. It is centred on encoder-decoder translation, including the 2014 study of its properties.

[Dzmitry Bahdanau](https://md.research.elamur.ai/free/en/authors/dzmitry-bahdanau/) is first author of the 2014 joint align-and-translate paper. Bahdanau also contributed to attention-based speech recognition and to an actor-critic method for training sequence generators beyond teacher forcing.

[Bart van Merriënboer](https://md.research.elamur.ai/free/en/authors/bart-van-merrienboer/) co-wrote the encoder-decoder analysis. [Ian Goodfellow](https://md.research.elamur.ai/free/en/authors/ian-goodfellow/) and [Caglar Gulcehre](https://md.research.elamur.ai/free/en/authors/caglar-gulcehre/) belong to the same Montreal group of deep learning researchers.

[Ruslan Salakhutdinov](https://md.research.elamur.ai/free/en/authors/ruslan-salakhutdinov/) of the University of Toronto worked on multimodal models. These include [Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/), which treats captioning as a kind of translation. [Geoffrey Hinton](https://md.research.elamur.ai/free/en/authors/geoffrey-hinton/) is also at the University of Toronto.

[Kelvin Xu](https://md.research.elamur.ai/free/en/authors/kelvin-xu/) is first author of the visual attention captioning paper. [Aaron Courville](https://md.research.elamur.ai/free/en/authors/aaron-courville/) appears at the Université de Montréal and in work associated with Polytechnique Montréal and the Canadian Institute for Advanced Research.

[Ryan Lowe](https://md.research.elamur.ai/free/en/authors/ryan-lowe/) of McGill University built the Ubuntu Dialogue Corpus from technical-support chat channels. Lowe also proposed a learned evaluator of dialogue responses, as an alternative to word-overlap metrics such as BLEU.

[Dale Schuurmans](https://md.research.elamur.ai/free/en/authors/dale-schuurmans/) represents the University of Alberta among the first names.

### Landmark works

The most cited Canadian-linked work in our library is [Array Programming with NumPy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) (2020), a description of the core Python library for array computing. It is tied to the University of British Columbia and the University of Toronto.

Close behind is [Neural Machine Translation by Jointly Learning to Align and Translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/) (2014). It introduced RNNsearch, in which the decoder attends to different parts of the source sentence instead of relying on a single fixed vector. No other Canadian work among the works here is cited by more papers in the library.

[Graph Attention Networks](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/) (2017) brought attention to graph-structured inputs and links the Université de Montréal with HEC Montréal. [On the Properties of Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/) (2014) examined where the then-new encoder-decoder approach breaks down. [Show, Attend and Tell](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/) (2015) applied visual attention to generating captions for photographs. [Intriguing Properties of Neural Networks](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/) (2013) reported two unexpected behaviours of image classifiers.

[Lexicon-Based Methods for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/) (2011) presented SO-CAL as an alternative to purely statistical classifiers. [HotpotQA](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/) (2018) built multi-hop questions over a graph of hyperlinks taken from Wikipedia opening paragraphs. It is one of the works most cited by other papers in the library.

[MIMIC-IV](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/) (2023) is a freely available clinical record resource, released by researchers at MIT and the Boston medical centre known as BIDMC. [Aligning Books and Movies](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/) (2015) linked the descriptive text of novels to scenes from their film versions. [Named Entity Recognition with Bidirectional LSTM-CNNs](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/) (2015) proposed a tagger that learns most of its features without hand engineering. [Personalizing Dialogue Agents](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/) (2018) gave chit-chat agents personas to make them less bland.

### Labs and units

The [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) is the largest single presence. Its works run from word representations and translation to HotpotQA and the 2023 large-language-model survey. In the same city, [Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/) contributes work on dialogue, self-attentive sentence embeddings, FiLM conditioning for visual reasoning and data augmentation surveys. Its works also include a study of the distributional hypothesis behind masked language modelling and a hybrid method for document summarization. People there include [Siva Reddy](https://md.research.elamur.ai/free/en/authors/siva-reddy/), [Joelle Pineau](https://md.research.elamur.ai/free/en/authors/joelle-pineau/) and [Jackie Chi Kit Cheung](https://md.research.elamur.ai/free/en/authors/jackie-chi-kit-cheung/).

[McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/) shares many people with Mila. Its works include [StereoSet](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/), which measures stereotypical bias in pretrained models. McGill also contributes the Ubuntu corpus and [TextWorld](https://md.research.elamur.ai/free/en/papers/cote-2018-textworld-learning-environment-text-based/), a Python sandbox for text-based games.

[Polytechnique Montréal](https://md.research.elamur.ai/free/en/universities/polytechnique-montreal/) adds [Quantifying the Carbon Emissions of Machine Learning](https://md.research.elamur.ai/free/en/papers/lacoste-2019-quantifying-carbon-emissions-machine-learning/), early coreference work and the BitMar edge model. [Concordia University](https://md.research.elamur.ai/free/en/universities/concordia-university/), through [Leila Kosseim](https://md.research.elamur.ai/free/en/authors/leila-kosseim/) and Nelson Filipe Costa, concentrates on discourse relations. HEC Montréal appears through Laurent Charlin's dialogue and graph work.

In Toronto, the [University of Toronto](https://md.research.elamur.ai/free/en/universities/university-of-toronto/) combines Hirst's older lexical semantics line with the multimodal and sequence work of Salakhutdinov, [Richard Zemel](https://md.research.elamur.ai/free/en/authors/richard-zemel/) and [Alex Graves](https://md.research.elamur.ai/free/en/authors/alex-graves/). Graves's [Sequence Transduction with Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/graves-2012-sequence-transduction-recurrent-neural-networks/) introduced the RNN Transducer.

The [Canadian Institute for Advanced Research](https://md.research.elamur.ai/free/en/labs/canadian-institute-for-advanced-research/) is attached to captioning, speech attention and the representation learning review. It is also linked to the AdapterFusion and AdapterHub frameworks for adapting transformers. The [Vector Institute](https://md.research.elamur.ai/free/en/labs/vector-institute/) carries much of the recent large-model work: automatic prompt engineering, table reasoning, DeCLUTR's contrastive sentence representations, DeeBERT and LongICLBench. The [Royal Bank of Canada](https://md.research.elamur.ai/free/en/labs/royal-bank-of-canada/) research arm and the Hospital for Sick Children complete the Toronto picture.

The [University of British Columbia](https://md.research.elamur.ai/free/en/universities/university-of-british-columbia/) is the western hub. Its lines include the discourse and summarization work of [Giuseppe Carenini](https://md.research.elamur.ai/free/en/authors/giuseppe-carenini/) and the Arabic NLP of [AbdelRahim Elmadany](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/) and El Moatez Billah Nagoudi. Ganesh Jawahar's survey on detecting machine-generated text also comes from there.

The [National Research Council Canada](https://md.research.elamur.ai/free/en/government/national-research-council-canada/) in Ottawa, the [University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/), Simon Fraser University, the University of Alberta and the [Université de Sherbrooke](https://md.research.elamur.ai/free/en/universities/universite-de-sherbrooke/) round out the main units.

### Collaborations

Works shared between Canadian institutions are common on this site. Show, Attend and Tell connects the Université de Montréal, the University of Toronto and the Canadian Institute for Advanced Research, tying the Montreal and Toronto deep learning groups together. NumPy and the SO-CAL paper both appear under Vancouver and Toronto.

Within Montreal, HotpotQA joins the Université de Montréal and Mila, and StereoSet joins Mila and McGill. The hierarchical latent-variable dialogue model links HEC Montréal and McGill. The 2020 summarization hybrid is shared by Mila, Polytechnique Montréal and the Canadian Institute for Advanced Research.

Links with industry appear in a few places. Humanity's Last Exam connects Concordia University with the Royal Bank of Canada. The bank's research arm also shares in the systematic evaluation of ChatGPT. Google (Canada) in Waterloo sits beside the University of Waterloo.

Beyond the country, MIMIC-IV brings in American partners at MIT and BIDMC. ConvAI2 was organised as a NeurIPS 2018 competition. The persona dialogue work of 2018 involved co-authors who worked on that shared challenge.

### Timeline

From 1991 to 2006 the Canadian works here deal with words and texts. They cover lexical chains as signals of text structure, distributional word similarity, unsupervised review sentiment and the comparison of WordNet relatedness measures.

The years 2010 to 2012 bridge two traditions. The emotion lexicon and SO-CAL carry the lexical line forward. Semi-supervised word representations, the representation learning review and the RNN Transducer open the neural one. Poly-co, a multilayer perceptron for coreference, dates from 2011.

From 2013 to 2015 comes the densest group of heavily cited works. The year 2013 brought adversarial properties of image networks. In 2014 came attention-based translation, encoder-decoder analysis and multimodal embeddings, along with discourse connective induction and discourse-aware review summarization. In 2015 the works include captioning with attention, book-film alignment, attention for speech, bidirectional LSTM-CNN tagging, CODRA, the Ubuntu corpus and hierarchical dialogue models.

From 2016 to 2019 the range widens. The year 2016 covers latent-variable dialogue generation, actor-critic sequence training and stance detection in tweets. In 2017 came graph attention, self-attentive embeddings, FiLM, EmoNet and learned dialogue evaluation. The year 2018 brought HotpotQA, persona chat, TextWorld and the affect task, and 2019 brought ConvAI2 and the carbon cost of machine learning.

From 2020 to 2022 pretrained transformers take over. In 2020 the works include NumPy, adapters, DeeBERT, detection of generated text and hybrid summarization. In 2021 came ARBERT and MARBERT, StereoSet, the augmentation survey, the distributional hypothesis study and DeCLUTR. The 2022 study of large models as prompt engineers leads into the next phase.

From 2023 to 2025 large language models dominate. In 2023 the works include surveys, hallucination, mathematics, recursive training, table reasoning, ChatGPT evaluation and MIMIC-IV. In 2024 came long-context learning and soft-label discourse classification. The most recent works, from 2025, are Humanity's Last Exam, open-target stance detection with large models and BitMar.

## Where

## Agglomerations (13)

1. [**Montreal**7 institutions*108,739 citations*](https://md.research.elamur.ai/free/en/agglomerations/montreal/)
2. [**Toronto**8 institutions*79,823 citations*](https://md.research.elamur.ai/free/en/agglomerations/toronto/)
3. [**Ottawa**3 institutions*23,483 citations*](https://md.research.elamur.ai/free/en/agglomerations/ottawa/)
4. [**Vancouver**2 institutions*37,934 citations*](https://md.research.elamur.ai/free/en/agglomerations/vancouver/)
5. [**Kitchener–Waterloo**2 institutions*13,659 citations*](https://md.research.elamur.ai/free/en/agglomerations/kitchener/)
6. [**Edmonton**1 institution*2,655 citations*](https://md.research.elamur.ai/free/en/agglomerations/edmonton/)
7. [**Winnipeg**1 institution*2,350 citations*](https://md.research.elamur.ai/free/en/agglomerations/winnipeg/)
8. [**Kingston**1 institution*1,610 citations*](https://md.research.elamur.ai/free/en/agglomerations/kingston/)
9. [**London**1 institution*204 citations*](https://md.research.elamur.ai/free/en/agglomerations/london-ca/)
10. [**Halifax**1 institution*440 citations*](https://md.research.elamur.ai/free/en/agglomerations/halifax/)
11. [**Calgary**1 institution*265 citations*](https://md.research.elamur.ai/free/en/agglomerations/calgary/)
12. [**Sherbrooke**1 institution*12 citations*](https://md.research.elamur.ai/free/en/agglomerations/sherbrooke/)
13. [**Guelph**1 institution*245 citations*](https://md.research.elamur.ai/free/en/agglomerations/guelph/)

## Institutions (32)

### 🎓 Universities (23)

1. **[Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/)**University · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*47 works · 73,845 citations*
2. **[University of Toronto](https://md.research.elamur.ai/free/en/universities/university-of-toronto/)**University · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*47 works · 50,254 citations*
3. **[University of Waterloo](https://md.research.elamur.ai/free/en/universities/university-of-waterloo/)**University · Waterloo ([Kitchener–Waterloo](https://md.research.elamur.ai/free/en/agglomerations/kitchener/)), Canada*44 works · 12,783 citations*
4. **[University of British Columbia](https://md.research.elamur.ai/free/en/universities/university-of-british-columbia/)**University · [Vancouver](https://md.research.elamur.ai/free/en/agglomerations/vancouver/), Canada*46 works · 33,673 citations*
5. **[McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/)**University · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*51 works · 10,476 citations*
6. **[University of Alberta](https://md.research.elamur.ai/free/en/universities/university-of-alberta/)**University · [Edmonton](https://md.research.elamur.ai/free/en/agglomerations/edmonton/), Canada*23 works · 2,655 citations*
7. **[University of Manitoba](https://md.research.elamur.ai/free/en/universities/university-of-manitoba/)**University · [Winnipeg](https://md.research.elamur.ai/free/en/agglomerations/winnipeg/), Canada*2 works · 2,350 citations*
8. **[Simon Fraser University](https://md.research.elamur.ai/free/en/universities/simon-fraser-university/)**University · Burnaby ([Vancouver](https://md.research.elamur.ai/free/en/agglomerations/vancouver/)), Canada*10 works · 4,261 citations*
9. **[York University](https://md.research.elamur.ai/free/en/universities/york-university/)**University · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*8 works · 2,822 citations*
10. **[University of Ottawa](https://md.research.elamur.ai/free/en/universities/university-of-ottawa/)**University · [Ottawa](https://md.research.elamur.ai/free/en/agglomerations/ottawa/), Canada*9 works · 2,393 citations*
11. **[HEC Montréal](https://md.research.elamur.ai/free/en/universities/hec-montreal/)**University · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*3 works · 9,680 citations*
12. **[Polytechnique Montréal](https://md.research.elamur.ai/free/en/universities/polytechnique-montreal/)**University · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*5 works · 670 citations*
13. **[Queen's University](https://md.research.elamur.ai/free/en/universities/queen-s-university/)**University · [Kingston](https://md.research.elamur.ai/free/en/agglomerations/kingston/), Canada*8 works · 1,610 citations*
14. **[Concordia University](https://md.research.elamur.ai/free/en/universities/concordia-university/)**University · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*5 works · 303 citations*
15. **[McMaster University](https://md.research.elamur.ai/free/en/universities/mcmaster-university/)**University · Hamilton ([Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/)), Canada*2 works · 646 citations*
16. **[Western University](https://md.research.elamur.ai/free/en/universities/western-university/)**University · [London](https://md.research.elamur.ai/free/en/agglomerations/london-ca/), Canada*4 works · 204 citations*
17. **[Ontario Tech University](https://md.research.elamur.ai/free/en/universities/ontario-tech-university/)**University · Oshawa ([Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/)), Canada*4 works · 1,490 citations*
18. **[Dalhousie University](https://md.research.elamur.ai/free/en/universities/dalhousie-university/)**University · [Halifax](https://md.research.elamur.ai/free/en/agglomerations/halifax/), Canada*6 works · 440 citations*
19. **[University of New Brunswick](https://md.research.elamur.ai/free/en/universities/university-of-new-brunswick/)**University · Fredericton, Canada*3 works · 77 citations*
20. **[University of Calgary](https://md.research.elamur.ai/free/en/universities/university-of-calgary/)**University · [Calgary](https://md.research.elamur.ai/free/en/agglomerations/calgary/), Canada*2 works · 265 citations*
21. **[Carleton University](https://md.research.elamur.ai/free/en/universities/carleton-university/)**University · [Ottawa](https://md.research.elamur.ai/free/en/agglomerations/ottawa/), Canada*2 works · 147 citations*
22. **[Université de Sherbrooke](https://md.research.elamur.ai/free/en/universities/universite-de-sherbrooke/)**University · [Sherbrooke](https://md.research.elamur.ai/free/en/agglomerations/sherbrooke/), Canada*2 works · 12 citations*
23. **[University of Guelph](https://md.research.elamur.ai/free/en/universities/university-of-guelph/)**University · [Guelph](https://md.research.elamur.ai/free/en/agglomerations/guelph/), Canada*2 works · 245 citations*

### 🔬 Labs and research institutes (5)

1. **[Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/)**Research institute · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*31 works · 13,061 citations*
2. **[Canadian Institute for Advanced Research](https://md.research.elamur.ai/free/en/labs/canadian-institute-for-advanced-research/)**Research institute · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*17 works · 16,274 citations*
3. **[Vector Institute](https://md.research.elamur.ai/free/en/labs/vector-institute/)**Research institute · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*19 works · 4,281 citations*
4. **[Alberta Machine Intelligence Institute](https://md.research.elamur.ai/free/en/labs/alberta-machine-intelligence-institute/)**Research institute · Alberta, Canada*2 works · 229 citations*
5. **[Royal Bank of Canada](https://md.research.elamur.ai/free/en/labs/royal-bank-of-canada/)**Research institute · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*2 works · 435 citations*

### 🏢 Companies (2)

1. **[Microsoft Research Montréal](https://md.research.elamur.ai/free/en/companies/microsoft-research-montreal/)**Company · [Montreal](https://md.research.elamur.ai/free/en/agglomerations/montreal/), Canada*5 works · 704 citations*
2. **[Google (Canada)](https://md.research.elamur.ai/free/en/companies/google-canada/)**Company · Waterloo ([Kitchener–Waterloo](https://md.research.elamur.ai/free/en/agglomerations/kitchener/)), Canada*2 works · 876 citations*

### 🏛️ Government research bodies (1)

1. **[National Research Council Canada](https://md.research.elamur.ai/free/en/government/national-research-council-canada/)**Government body · [Ottawa](https://md.research.elamur.ai/free/en/agglomerations/ottawa/), Canada*40 works · 20,943 citations*

### 🏥 Hospitals and medical centres (1)

1. **[Hospital for Sick Children](https://md.research.elamur.ai/free/en/hospitals/hospital-for-sick-children/)**Hospital · [Toronto](https://md.research.elamur.ai/free/en/agglomerations/toronto/), Canada*2 works · 3,621 citations*

## People (160)

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

1. [**Yoshua Bengio**Université de Montréal*23 papers here · 101,315 citations*](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/)
2. [**Kyunghyun Cho**Université de Montréal*36 papers here · 78,126 citations*](https://md.research.elamur.ai/free/en/authors/kyunghyun-cho/)
3. [**Dzmitry Bahdanau**Université de Montréal*8 papers here · 56,388 citations*](https://md.research.elamur.ai/free/en/authors/dzmitry-bahdanau/)
4. [**Bart van Merriënboer**Université de Montréal*2 papers here · 35,014 citations*](https://md.research.elamur.ai/free/en/authors/bart-van-merrienboer/)
5. [**Ian Goodfellow**Université de Montréal*5 papers here · 32,232 citations*](https://md.research.elamur.ai/free/en/authors/ian-goodfellow/)
6. [**Caglar Gulcehre**Université de Montréal*4 papers here · 30,981 citations*](https://md.research.elamur.ai/free/en/authors/caglar-gulcehre/)
7. [**Ruslan Salakhutdinov**University of Toronto*13 papers here · 29,064 citations*](https://md.research.elamur.ai/free/en/authors/ruslan-salakhutdinov/)
8. [**Ryan Lowe**McGill University*7 papers here · 27,049 citations*](https://md.research.elamur.ai/free/en/authors/ryan-lowe/)
9. [**Geoffrey Hinton**University of Toronto*5 papers here · 25,589 citations*](https://md.research.elamur.ai/free/en/authors/geoffrey-hinton/)
10. [**Aaron Courville**Université de Montréal*9 papers here · 15,667 citations*](https://md.research.elamur.ai/free/en/authors/aaron-courville/)
11. [**Kelvin Xu**Université de Montréal*5 papers here · 15,655 citations*](https://md.research.elamur.ai/free/en/authors/kelvin-xu/)
12. [**Dale Schuurmans**University of Alberta*4 papers here · 14,998 citations*](https://md.research.elamur.ai/free/en/authors/dale-schuurmans/)
13. [**Ryan Kiros**University of Toronto*3 papers here · 12,497 citations*](https://md.research.elamur.ai/free/en/authors/ryan-kiros/)
14. [**Richard Zemel**University of Toronto*3 papers here · 12,405 citations*](https://md.research.elamur.ai/free/en/authors/richard-zemel/)
15. [**Alistair E. W. Johnson**Hospital for Sick Children*2 papers here · 11,930 citations*](https://md.research.elamur.ai/free/en/authors/alistair-e-w-johnson/)
16. [**Saif Mohammad**National Research Council Canada*21 papers here · 11,334 citations*](https://md.research.elamur.ai/free/en/authors/saif-mohammad/)
17. [**Jimmy Ba**University of Toronto*5 papers here · 10,635 citations*](https://md.research.elamur.ai/free/en/authors/jimmy-ba/)
18. [**Peter Turney**National Research Council Canada*9 papers here · 9,111 citations*](https://md.research.elamur.ai/free/en/authors/peter-turney/)
19. [**Colin Cherry**National Research Council Canada*17 papers here · 8,812 citations*](https://md.research.elamur.ai/free/en/authors/colin-cherry/)
20. [**Siva Reddy**McGill University*14 papers here · 7,905 citations*](https://md.research.elamur.ai/free/en/authors/siva-reddy/)
21. [**Svetlana Kiritchenko**National Research Council Canada*8 papers here · 7,616 citations*](https://md.research.elamur.ai/free/en/authors/svetlana-kiritchenko/)
22. [**Alex Graves**University of Toronto*2 papers here · 7,489 citations*](https://md.research.elamur.ai/free/en/authors/alex-graves/)
23. [**William L. Hamilton**McGill University*5 papers here · 6,930 citations*](https://md.research.elamur.ai/free/en/authors/william-l-hamilton/)
24. [**Wenhu Chen**University of Waterloo*15 papers here · 6,887 citations*](https://md.research.elamur.ai/free/en/authors/wenhu-chen/)
25. [**Yang Xu**University of Toronto*5 papers here · 6,314 citations*](https://md.research.elamur.ai/free/en/authors/yang-xu/)

[See all 160 people in Canada →](https://md.research.elamur.ai/free/en/countries/canada/people/)

## Works in our library (333)

Works of its institutions, the most cited first.

1. **[Array Programming with NumPy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,591 citations*
2. [**Neural Machine Translation by Jointly Learning to Align and Translate arXiv:1409.0473**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 19,018 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
3. [**Graph Attention Networks arXiv:1710.10903**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 9,000 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
4. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches arXiv:1409.1259**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
5. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention arXiv:1502.03044**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,523 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
6. [**Intriguing properties of neural networks arXiv:1312.6199**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,878 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
7. **[Lexicon-Based Methods for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Maite Taboada, Julian Brooke, Milan Tofiloski, et al.*2011 · 3,563 citations*
8. **[HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,495 citations*
9. **[From Frequency to Meaning: Vector Space Models of Semantics](https://md.research.elamur.ai/free/en/papers/turney-2010-frequency-meaning-vector-space-models/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Peter D. Turney, Patrick Pantel*2010 · 3,430 citations*
10. **[MIMIC-IV, a freely accessible electronic health record dataset](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al.*2023 · 3,386 citations*
11. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books arXiv:1506.06724**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,051 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
12. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,863 citations*
13. **[Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peter Turney*2002 · 2,831 citations*
14. **[Word Representations: A Simple and General Method for Semi-Supervised Learning](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio*2010 · 2,442 citations*
15. **[Named Entity Recognition with Bidirectional LSTM-CNNs](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jason P. C. Chiu, Eric Nichols*2015 · 2,283 citations*
16. **[Automatic Retrieval and Clustering of Similar Words](https://md.research.elamur.ai/free/en/papers/lin-1998-automatic-retrieval-clustering-similar-words/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dekang Lin*1998 · 2,062 citations*
17. **[Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,849 citations*
18. [**Attention-Based Models for Speech Recognition arXiv:1506.07503**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,787 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
19. [**A Structured Self-attentive Sentence Embedding arXiv:1703.03130**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,782 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
20. **[How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://md.research.elamur.ai/free/en/papers/liu-2016-how-not-evaluate-your-dialogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chia-Wei Liu, Ryan Lowe, Iulian Serban, et al.*2016 · 1,744 citations*
21. **[Evaluating WordNet-based Measures of Lexical Semantic Relatedness](https://md.research.elamur.ai/free/en/papers/budanitsky-2006-evaluating-wordnet-based-measures-lexical/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Alexander Budanitsky, Graeme Hirst*2006 · 1,614 citations*
22. [**Sequence Transduction with Recurrent Neural Networks arXiv:1211.3711**Alex Graves*2012 · 1,406 citations*](https://md.research.elamur.ai/free/en/papers/graves-2012-sequence-transduction-recurrent-neural-networks/)
23. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models arXiv:1411.2539**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,325 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
24. **[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*
25. **[SemEval-2016 Task 6: Detecting Stance in Tweets](https://md.research.elamur.ai/free/en/papers/mohammad-2016-semeval-2016-task-6-detecting/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, et al.*2016 · 1,199 citations*

[See all 333 works in Canada →](https://md.research.elamur.ai/free/en/countries/canada/works/)

## Fields, topics and concepts

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

📚 Shelves 44 The field of science this work belongs to (OpenAlex fields).[Computer Science · 313 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 308 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 304 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 304 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 50 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Psychology · 33 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Experimental and Cognitive Psychology · 27 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Social Sciences · 22 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Information Systems · 19 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Mathematics · 9 works](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Decision Sciences · 4 works](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · [Safety Research · 11 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Information Systems and Management · 2 works](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · [Hardware and Architecture · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) · [Physics and Astronomy · 1 work](https://md.research.elamur.ai/free/en/shelves/physics-and-astronomy/)+29 more

🧭 Topics 39 Research subjects and languages the work is about.[Machine translation · 34 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Language models & pretraining · 50 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Sentiment & emotion · 26 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Dialogue systems · 22 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Evaluation & benchmarks · 37 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Question answering · 10 works](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Vision & language · 6 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Multilingual & cross-lingual NLP · 18 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [English · 34 works](https://md.research.elamur.ai/free/en/topics/english/) · [Information retrieval & search · 9 works](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Reasoning · 14 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Discourse analysis · 21 works](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Semitic languages · 33 works](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Idioms & multiword expressions · 9 works](https://md.research.elamur.ai/free/en/topics/multiword-expressions/) · [Summarization · 10 works](https://md.research.elamur.ai/free/en/topics/summarization/)+24 more

💡 Concepts 13 Key ideas and methods explained on our concept pages.[Transformer and attention · 2 works](https://md.research.elamur.ai/free/en/concepts/transformer/) · [Distributional semantics · 2 works](https://md.research.elamur.ai/free/en/concepts/distributional-semantics/) · [Named entity recognition · 2 works](https://md.research.elamur.ai/free/en/concepts/named-entity-recognition/) · [Semantic similarity · 3 works](https://md.research.elamur.ai/free/en/concepts/semantic-similarity/) · [Language model · 1 work](https://md.research.elamur.ai/free/en/concepts/language-model/) · [Cognitive linguistics · 3 works](https://md.research.elamur.ai/free/en/concepts/cognitive-linguistics/) · [Word embeddings · 1 work](https://md.research.elamur.ai/free/en/concepts/word-embeddings/) · [Discourse analysis · 3 works](https://md.research.elamur.ai/free/en/concepts/discourse-analysis/) · [BLEU score · 1 work](https://md.research.elamur.ai/free/en/concepts/bleu/) · [Lexical semantics · 2 works](https://md.research.elamur.ai/free/en/concepts/lexical-semantics/) · [Semantic role labeling · 1 work](https://md.research.elamur.ai/free/en/concepts/semantic-role-labeling/) · [Lexicography · 1 work](https://md.research.elamur.ai/free/en/concepts/lexicography/) · [Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/)

## Works by year

### 2020s

[**2026** 10 works Works of Canada from 2026](https://md.research.elamur.ai/free/en/countries/canada/year/2026/) · [**2025** 13 works Works of Canada from 2025](https://md.research.elamur.ai/free/en/countries/canada/year/2025/) · [**2024** 26 works Works of Canada from 2024](https://md.research.elamur.ai/free/en/countries/canada/year/2024/) · [**2023** 47 works Works of Canada from 2023](https://md.research.elamur.ai/free/en/countries/canada/year/2023/) · [**2022** 29 works Works of Canada from 2022](https://md.research.elamur.ai/free/en/countries/canada/year/2022/) · [**2021** 27 works Works of Canada from 2021](https://md.research.elamur.ai/free/en/countries/canada/year/2021/) · [**2020** 28 works Works of Canada from 2020](https://md.research.elamur.ai/free/en/countries/canada/year/2020/)

### 2010s

[**2019** 21 works Works of Canada from 2019](https://md.research.elamur.ai/free/en/countries/canada/year/2019/) · [**2018** 17 works Works of Canada from 2018](https://md.research.elamur.ai/free/en/countries/canada/year/2018/) · [**2017** 20 works Works of Canada from 2017](https://md.research.elamur.ai/free/en/countries/canada/year/2017/) · [**2016** 15 works Works of Canada from 2016](https://md.research.elamur.ai/free/en/countries/canada/year/2016/) · [**2015** 13 works Works of Canada from 2015](https://md.research.elamur.ai/free/en/countries/canada/year/2015/) · [**2014** 13 works Works of Canada from 2014](https://md.research.elamur.ai/free/en/countries/canada/year/2014/) · [**2013** 8 works Works of Canada from 2013](https://md.research.elamur.ai/free/en/countries/canada/year/2013/) · [**2012** 7 works Works of Canada from 2012](https://md.research.elamur.ai/free/en/countries/canada/year/2012/) · [**2011** 5 works Works of Canada from 2011](https://md.research.elamur.ai/free/en/countries/canada/year/2011/) · [**2010** 7 works Works of Canada from 2010](https://md.research.elamur.ai/free/en/countries/canada/year/2010/)

### 2000s

[**2009** 3 works Works of Canada from 2009](https://md.research.elamur.ai/free/en/countries/canada/year/2009/) · [**2007** 2 works Works of Canada from 2007](https://md.research.elamur.ai/free/en/countries/canada/year/2007/) · [**2006** 6 works Works of Canada from 2006](https://md.research.elamur.ai/free/en/countries/canada/year/2006/) · [**2005** 2 works Works of Canada from 2005](https://md.research.elamur.ai/free/en/countries/canada/year/2005/) · [**2003** 1 work Works of Canada from 2003](https://md.research.elamur.ai/free/en/papers/modjeska-2003-web-machine-learning-other-anaphora/) · [**2002** 2 works Works of Canada from 2002](https://md.research.elamur.ai/free/en/countries/canada/year/2002/) · [**2001** 2 works Works of Canada from 2001](https://md.research.elamur.ai/free/en/countries/canada/year/2001/) · [**2000** 1 work Works of Canada from 2000](https://md.research.elamur.ai/free/en/papers/pantel-2000-unsupervised-approach-prepositional-phrase-attachment/)

### 1990s

[**1999** 1 work Works of Canada from 1999](https://md.research.elamur.ai/free/en/papers/lin-1999-automatic-identification-non-compositional-phrases/) · [**1998** 2 works Works of Canada from 1998](https://md.research.elamur.ai/free/en/countries/canada/year/1998/) · [**1995** 1 work Works of Canada from 1995](https://md.research.elamur.ai/free/en/papers/mcroy-1995-repair-speech-act-misunderstandings-abductive/) · [**1991** 2 works Works of Canada from 1991](https://md.research.elamur.ai/free/en/countries/canada/year/1991/)

### 1980s

[**1985** 1 work Works of Canada from 1985](https://md.research.elamur.ai/free/en/papers/cohen-1985-speech-acts-rationality/)

### 1970s

[**1979** 1 work Works of Canada from 1979](https://md.research.elamur.ai/free/en/papers/allen-1979-plans-inference-indirect-speech-acts/)

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Updated 24 October 2026