---
title: "Denver — research institutions and their papers in our library"
description: "Denver (United States): 5 institutions with papers on Elamur Research — 4 universities, 1 company — and 52,468 citations of their works."
canonical: "https://research.elamur.ai/free/en/agglomerations/denver/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/agglomerations/denver/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [agglomerations](https://md.research.elamur.ai/free/en/agglomerations/) / Denver

# Denver

Agglomeration [United States](https://md.research.elamur.ai/free/en/countries/united-states/) · metro population 2,928,613 (functional urban area, GHSL 2015)

**5** institutions with a page**58** works in our library**52,468** citations of their works

## About

### Profile

Denver is a city region in the [United States](https://md.research.elamur.ai/free/en/countries/united-states/). On this site, its research comes from a small group of institutions. Three are universities: the [University of Colorado Boulder](https://md.research.elamur.ai/free/en/universities/university-of-colorado-boulder/), the [University of Colorado System](https://md.research.elamur.ai/free/en/universities/university-of-colorado-system/), both placed in Boulder, and the [University of Colorado Denver](https://md.research.elamur.ai/free/en/universities/university-of-colorado-denver/) in Denver itself. The fourth is a company, [Boulder Language Technologies](https://md.research.elamur.ai/free/en/companies/boulder-language-technologies/), also based in Boulder. A full overview of the institutions of the region gathers them in one place.

The region's profile here is narrow and coherent. Almost all of its works deal with computational linguistics and natural language processing. They build ways to represent meaning, label who did what to whom in a sentence, track entities and events across texts, model discourse and dialogue, and handle the morphology of many languages, including low-resource Indigenous languages of the Americas. A smaller set of works in scientific computing and clinical text processing sits beside this core. The University of Colorado Boulder accounts for nearly all of the region's output and for most of the citations its works receive. The other three institutions appear mainly as partners on shared papers.

### History

Among the works here, the region's research begins at the turn of the millennium with speech and morphology. The earliest item is [Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/) from 2000. It tags each utterance of spontaneous telephone conversation with a dialogue act, such as a statement, a question or a backchannel. A year later, [Knowledge-Free Induction of Inflectional Morphologies](https://md.research.elamur.ai/free/en/papers/schone-2001-knowledge-free-induction-inflectional-morphologies/) learned the inflectional morphology of a language from raw text alone, without any human-supplied knowledge. In 2002 [Automatic Labeling of Semantic Roles](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/) began the line on semantic roles that would run through the next two decades.

During the mid-2000s the region's work moved toward annotation frameworks and evaluation campaigns. A 2005 framework for information structure in conversational English came first. In 2007 a SemEval task jointly evaluated word sense and semantic role labeling. A 2008 study looked at how robust semantic role labelers are, and work from 2008 also extended role labeling to Chinese predicates. In 2011 came a CoNLL shared task on unrestricted coreference.

The 2010s were the busiest decade on this site. Abstract Meaning Representation appeared in 2013. Clinical temporal annotation and the SemLink+ resource followed in 2014. The year 2015 brought neural text classification, AMR parsing, quiz-bowl coreference and shallow discourse parsing. In 2016 Clinical TempEval and a multilingual follow-up on discourse parsing appeared. Work on morphological reinflection marks 2017, and 2019 gave the region both the SciPy 1.0 paper and a study of cross-document coreference.

From 2020 onward, the works here turn toward transfer learning with pretrained models and toward Indigenous languages of the Americas. Shared tasks and evaluation sets for those languages appear in 2021 and 2022. Discourse analysis through questions and answers marks 2023. Two works continue the meaning-representation and low-resource lines into 2025 and 2026.

### Research profile

The largest strand concerns semantic roles and predicate-argument structure. Automatic Labeling of Semantic Roles introduced the first statistical system for assigning FrameNet frame elements to the parts of a sentence. Later work tested such systems at scale and outside their training conditions. [Towards Robust Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/pradhan-2008-robust-semantic-role-labeling/) describes ASSERT, a labeler built on support vector machines, and uses it to study how performance holds up when the text differs from the training material. The 2008 Chinese work carries the same task into another language. [SemEval-2007 Task-17: English Lexical Sample, SRL and All Words](https://md.research.elamur.ai/free/en/papers/pradhan-2007-semeval-2007-task-17-english/) set up a common evaluation for sense disambiguation and role labeling. [SemLink+: FrameNet, VerbNet and Event Ontologies](https://md.research.elamur.ai/free/en/papers/palmer-2014-semlink-framenet-verbnet-event-ontologies/) links lexical resources and proposes an event ontology drawn from relations between frames.

A second strand concerns whole-sentence meaning representation. [Abstract Meaning Representation for Sembanking](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/) defines AMR, a rooted and labelled graph that captures the meaning of an entire sentence. The aim is to build a large bank of sentences annotated this way. [Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers](https://md.research.elamur.ai/free/en/papers/wang-2015-boosting-transition-based-amr-parsing/) is a short paper that improves an existing two-stage transition-based parser for producing such graphs. The line continues with Uniform Meaning Representation. [Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation](https://md.research.elamur.ai/free/en/papers/gamba-2025-bootstrapping-umrs-universal-dependencies-scalable/) addresses slow manual annotation by turning dependency trees into partial UMR graphs, which human annotators then complete.

Coreference and events form a third strand. [CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes](https://md.research.elamur.ai/free/en/papers/pradhan-2011-conll-2011-shared-task-modeling/) organized a community evaluation of general coreference on the OntoNotes corpus. [Removing the Training Wheels](https://md.research.elamur.ai/free/en/papers/guha-2015-removing-training-wheels-coreference-dataset/) argues that quiz bowl questions are a harder test domain for coreference systems. [Cross-document coreference](https://md.research.elamur.ai/free/en/papers/bettner-2019-cross-document-coreference-approach-capturing/) tracks events across documents. One of its central design choices is a rule-driven relation for subevents, applied only to certain event categories.

Discourse and dialogue make up a fourth strand. Beyond the early dialogue-act work, [A Framework for Annotating Information Structure in Discourse](https://md.research.elamur.ai/free/en/papers/calhoun-2005-framework-annotating-information-structure-discourse/) sets out a scheme for analysing information structure in spoken English. [The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/) made the identification of discourse relations a shared benchmark. [CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/xue-2016-conll-2016-shared-task-multilingual/) repeated the exercise across languages on news text. The 2023 work on discourse analysis through questions and answers carries this interest into the 2020s.

Clinical language processing appears through time expressions and events in medical records. [Temporal Annotation in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/iv-2014-temporal-annotation-clinical-domain/) presents THYME-TimeML, which adapts and extends the ISO-TimeML standard for clinical narratives. The 2016 SemEval task on Clinical TempEval turns that kind of annotation into an evaluation.

Morphology and multilingual resources form a further strand that reaches from 2001 to the present. Alongside the unsupervised induction of inflection, there is the 2017 work on data augmentation for morphological reinflection. [Marrying Universal Dependencies and Universal Morphology](https://md.research.elamur.ai/free/en/papers/mccarthy-2018-marrying-universal-dependencies-universal-morphology/) reconciles two cross-lingual schemes for morphosyntactic annotation. Recent work centres on the Indigenous languages of the Americas. [Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous](https://md.research.elamur.ai/free/en/papers/mager-2021-findings-americasnlp-2021-shared-task/) reports a shared task on translating into these languages. The 2022 AmericasNLI work evaluates zero-shot natural language inference for them. [Linguistic Feature Tagging for Automatic Classification of 27 Closely-Related Quechua](https://md.research.elamur.ai/free/en/papers/post-2026-linguistic-feature-tagging-automatic-classification/) classifies closely related Quechua varieties and gives practical advice for other low-resource languages, finding lexical tagging the most useful kind of annotation.

Neural representation learning and general scientific software complete the picture. [Deep Unordered Composition Rivals Syntactic Methods for Text Classification](https://md.research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/) proposes the deep averaging network. This model averages word vectors before passing them through several layers, and it competes with models that rely on syntactic structure. The 2020 work on intermediate-task transfer learning with pretrained models belongs to the same area. [SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/) describes the open-source SciPy library, which has served this role since its earliest version in 2001. The University of Colorado Boulder is among the institutions behind it.

### Leading scientists

The people the University of Colorado Boulder names first set the shape of the region's research here. [Martha Palmer](https://md.research.elamur.ai/free/en/authors/martha-palmer/) comes first. Palmer also appears with the University of Colorado System and the University of Colorado Denver. The SemLink+ paper, which links FrameNet, VerbNet and event ontologies, carries Palmer's name, and it fits the region's long line on predicate-argument structure and lexical semantic resources.

[Steven Bethard](https://md.research.elamur.ai/free/en/authors/steven-bethard/) follows, with ties to both Boulder and the university system. The region's clinical temporal work and its event-oriented annotation form the setting for Bethard's presence here. [Claire Bonial](https://md.research.elamur.ai/free/en/authors/claire-bonial/) and [Jena D. Hwang](https://md.research.elamur.ai/free/en/authors/jena-d-hwang/) come next. Both are connected with Boulder's work on lexical resources and semantic annotation.

[James H. Martin](https://md.research.elamur.ai/free/en/authors/james-h-martin/) is named by both Boulder and the university system, where Martin heads the list. [Dmitriy Dligach](https://md.research.elamur.ai/free/en/authors/dmitriy-dligach/) appears with Boulder, close to the clinical and lexical-semantic strands. [Wayne Ward](https://md.research.elamur.ai/free/en/authors/wayne-ward/) connects three institutions: the university, the university system and Boulder Language Technologies. This makes Ward one of the clearest links between academic and company research in the region. [Mans Hulden](https://md.research.elamur.ai/free/en/authors/mans-hulden/) is the only person here tied to all three universities. Hulden is associated with the region's growing work on morphology and low-resource languages.

Several other people appear through the university system and the partner institutions. [Sameer Pradhan](https://md.research.elamur.ai/free/en/authors/sameer-pradhan/) is named by both the university system and Boulder Language Technologies. Pradhan's name heads the 2007 SemEval task, the 2008 work on robust role labeling with ASSERT, and the 2011 CoNLL coreference task. [Nianwen Xue](https://md.research.elamur.ai/free/en/authors/nianwen-xue/) appears with the university system, and Xue's name heads the 2015 and 2016 CoNLL shared tasks on shallow discourse parsing. [Ananya Ganesh](https://md.research.elamur.ai/free/en/authors/ananya-ganesh/) is also connected with the system.

Through the University of Colorado Denver, the region's works connect with [Zhiyong Lu](https://md.research.elamur.ai/free/en/authors/zhiyong-lu/), [Karin Verspoor](https://md.research.elamur.ai/free/en/authors/karin-verspoor/), [Aurelie Neveol](https://md.research.elamur.ai/free/en/authors/aurelie-neveol/) and [Paul Cook](https://md.research.elamur.ai/free/en/authors/paul-cook/). These names point toward biomedical text processing and lexical semantics. At Boulder Language Technologies, [Rodney Nielsen](https://md.research.elamur.ai/free/en/authors/rodney-nielsen/) appears alongside Pradhan and Ward.

### Landmark works

The most cited work connected with the region on this site is Abstract Meaning Representation for Sembanking (2013). It proposed a single graph formalism for the meaning of a whole sentence, so that large annotated corpora could be built and parsers trained on them. Much later work here, from transition-based AMR parsing to the UMR bootstrapping of 2025, builds on it.

Next comes Automatic Labeling of Semantic Roles (2002). It was the first statistical system to assign FrameNet frame elements automatically, and it opened the region's long line on role labeling. The SciPy 1.0 paper of 2019 follows. It documents a library for scientific computing in Python and shows the region's reach beyond linguistics. The 2000 dialogue-act study is also among the most cited. It combined models of word sequences, prosody and dialogue structure to recognise the function of each utterance in telephone speech.

Below these, the deep averaging network paper of 2015 showed that a simple unordered model can rival syntax-aware methods for text classification. The CoNLL-2011 coreference task and the SemEval-2007 task gave whole research communities common data and evaluation measures. THYME-TimeML, from 2014, set a scheme for annotating time in clinical records. Each of these is cited widely among the works in our library.

### Labs and units

The University of Colorado Boulder is the region's main research centre here. It is behind the large majority of the works and its people head the lists of the other institutions. Its output covers every strand described above, from semantic roles and AMR to clinical time, discourse, morphology and scientific software. The full set of universities in the region also includes two smaller partners.

The University of Colorado System appears on a handful of works: the deep averaging network, robust semantic role labeling, quiz-bowl coreference, SemLink+ and cross-document coreference. Its people largely overlap with Boulder's. The University of Colorado Denver appears on two works, the deep averaging network and the quiz-bowl coreference paper, and it connects the region with researchers in biomedical language processing.

Boulder Language Technologies is the only company in the region with works here. Its contributions are the two CoNLL shared tasks on shallow discourse parsing and the 2015 paper on boosting transition-based AMR parsing.

### Collaborations

The clearest pattern among the works here is the close tie between the university and the company in Boulder. Wayne Ward and Sameer Pradhan both appear with Boulder Language Technologies and with the university system. The company's papers continue the same research lines found at the university: discourse parsing benchmarks and AMR parsing. Within the University of Colorado family, the same people move between the Boulder campus, the system and the Denver campus. Mans Hulden and Martha Palmer appear with all three.

Many of the region's most cited works are shared tasks and community resources, such as SemEval-2007, CoNLL-2011, CoNLL-2015 and 2016, Clinical TempEval and AmericasNLP 2021. By their nature these bring together many groups from outside the region. The SciPy 1.0 paper is a broad open-source collaboration. The recent work on Quechua and other Indigenous languages points to partnerships beyond the United States, although the works here do not name specific foreign institutions.

### Timeline

From 2000 to 2002, the region's works in our library cover dialogue-act modeling of conversational speech, unsupervised induction of inflectional morphology and the first statistical labeling of semantic roles.

From 2005 to 2011, annotation and evaluation take centre stage. Work in this period includes the information-structure framework (2005), the SemEval-2007 task, robust and Chinese semantic role labeling (2008) and the CoNLL-2011 coreference task.

The years 2013 to 2016 are the most productive. They bring AMR (2013), THYME-TimeML and SemLink+ (2014), the deep averaging network, AMR parsing, quiz-bowl coreference and the first shallow discourse parsing task (2015), and Clinical TempEval with the multilingual discourse task (2016).

From 2017 to 2019, morphology returns through reinflection (2017) and the joining of Universal Dependencies with UniMorph (2018). The SciPy 1.0 paper and cross-document event coreference both appear in 2019.

From 2020 to 2026, the region's works turn to transfer learning with pretrained models (2020) and to Indigenous languages of the Americas (2021, 2022). Discourse analysis through questions and answers follows in 2023, then the bootstrapping of UMR graphs from dependency trees (2025) and the classification of Quechua varieties (2026).

## Institutions (5)

4 universities, 1 company.

### 🎓 Universities (4)

1. **[University of Colorado Boulder](https://md.research.elamur.ai/free/en/universities/university-of-colorado-boulder/)**University · Boulder, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*52 works · 49,647 citations*
2. **[University of Colorado System](https://md.research.elamur.ai/free/en/universities/university-of-colorado-system/)**University · Boulder, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*7 works · 1,348 citations*
3. **[University of Colorado Denver](https://md.research.elamur.ai/free/en/universities/university-of-colorado-denver/)**University · Denver, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*2 works · 959 citations*
4. **[Colorado School of Mines](https://md.research.elamur.ai/free/en/universities/colorado-school-of-mines/)**University · Golden, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*2 works · 2 citations*

### 🏢 Companies (1)

1. **[Boulder Language Technologies](https://md.research.elamur.ai/free/en/companies/boulder-language-technologies/)**Company · Boulder, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*3 works · 512 citations*

## Works in our library (58)

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. [**Automatic Labeling of Semantic Roles**Daniel Gildea, Daniel Jurafsky*2002 · 2,218 citations*](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/)
3. [**Abstract Meaning Representation for Sembanking**Laura Banarescu, Claire Bonial, Shu Cai, et al.*2013 · 1,919 citations*](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/)
4. [**Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech**Andreas Stolcke, Klaus Ries, Noah Coccaro, et al.*2000 · 1,450 citations*](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/)
5. **[Deep Unordered Composition Rivals Syntactic Methods for Text Classification](https://md.research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, et al.*2015 · 920 citations*
6. **[SemEval-2007 Task-17: English Lexical Sample, SRL and All Words](https://md.research.elamur.ai/free/en/papers/pradhan-2007-semeval-2007-task-17-english/) [International Workshop on Semantic Evaluations](https://md.research.elamur.ai/free/en/venues/international-workshop-on-semantic-evaluations/)**Sameer Pradhan, Edward Loper, Dmitriy Dligach, et al.*2007 · 443 citations*
7. [**CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes**Sameer Pradhan, Lance Ramshaw, Mitchell Marcus, et al.*2011 · 434 citations*](https://md.research.elamur.ai/free/en/papers/pradhan-2011-conll-2011-shared-task-modeling/)
8. **[Temporal Annotation in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/iv-2014-temporal-annotation-clinical-domain/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**William F. Styler IV, Steven Bethard, Sean Finan, et al.*2014 · 349 citations*
9. **[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 281 citations*
10. **[SemEval-2016 Task 12: Clinical TempEval](https://md.research.elamur.ai/free/en/papers/bethard-2016-semeval-2016-task-12-clinical/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Steven Bethard, Guergana Savova, Wei-Te Chen, et al.*2016 · 264 citations*
11. **[SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge](https://md.research.elamur.ai/free/en/papers/dzikovska-2013-semeval-2013-task-7-joint/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Myroslava Dzikovska, Rodney Nielsen, Chris Brew, et al.*2013 · 235 citations*
12. [**Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)**Tim O'Gorman, Kristin Wright-Bettner, Martha Palmer*2016 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gorman-2016-richer-event-description-integrating-event/)
13. **[The CoNLL-2015 Shared Task on Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/xue-2015-conll-2015-shared-task-shallow/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, et al.*2015 · 210 citations*
14. [**CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing**Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, et al.*2016 · 187 citations*](https://md.research.elamur.ai/free/en/papers/xue-2016-conll-2016-shared-task-multilingual/)
15. **[Labeling Chinese Predicates with Semantic Roles](https://md.research.elamur.ai/free/en/papers/xue-2008-labeling-chinese-predicates-semantic-roles/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Nianwen Xue*2008 · 181 citations*
16. **[SemEval-2017 Task 12: Clinical TempEval](https://md.research.elamur.ai/free/en/papers/bethard-2017-semeval-2017-task-12-clinical/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Steven Bethard, Guergana Savova, Martha Palmer, et al.*2017 · 176 citations*
17. **[Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence](https://md.research.elamur.ai/free/en/papers/sultan-2014-back-basics-monolingual-alignment-exploiting/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Md Arafat Sultan, Steven Bethard, Tamara Sumner*2014 · 175 citations*
18. [**Knowledge-Free Induction of Inflectional Morphologies**Patrick Schone, Daniel Jurafsky*2001 · 170 citations*](https://md.research.elamur.ai/free/en/papers/schone-2001-knowledge-free-induction-inflectional-morphologies/)
19. **[ClearTK-TimeML: A minimalist approach to TempEval 2013](https://md.research.elamur.ai/free/en/papers/bethard-2013-cleartk-timeml-minimalist-approach-tempeval/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Steven Bethard*2013 · 168 citations*
20. **[AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, et al.*2022 · 163 citations*
21. **[Towards Robust Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/pradhan-2008-robust-semantic-role-labeling/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Sameer S. Pradhan, Wayne Ward, James H. Martin*2008 · 160 citations*
22. **[UniMorph 3.0: Universal Morphology](https://md.research.elamur.ai/free/en/papers/mccarthy-2020-unimorph-3-0-universal-morphology/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Arya D. McCarthy, Christo Kirov, Matteo Grella, et al.*2020 · 122 citations*
23. **[Data Augmentation for Morphological Reinflection](https://md.research.elamur.ai/free/en/papers/silfverberg-2017-data-augmentation-morphological-reinflection/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Miikka Silfverberg, Adam Wiemerslage, Ling Liu, et al.*2017 · 122 citations*
24. **[Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas](https://md.research.elamur.ai/free/en/papers/mager-2021-findings-americasnlp-2021-shared-task/) [Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)](https://md.research.elamur.ai/free/en/venues/americasnlp/)**Manuel Mager, Arturo Oncevay, Abteen Ebrahimi, et al.*2021 · 118 citations*
25. **[Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers](https://md.research.elamur.ai/free/en/papers/wang-2015-boosting-transition-based-amr-parsing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chuan Wang, Nianwen Xue, Sameer Pradhan*2015 · 115 citations*

[See all 58 works in Denver →](https://md.research.elamur.ai/free/en/agglomerations/denver/works/)

## Works by year

### 2020s

[**2026** 1 work Works of Denver from 2026](https://md.research.elamur.ai/free/en/papers/post-2026-linguistic-feature-tagging-automatic-classification/) · [**2025** 1 work Works of Denver from 2025](https://md.research.elamur.ai/free/en/papers/gamba-2025-bootstrapping-umrs-universal-dependencies-scalable/) · [**2023** 3 works Works of Denver from 2023](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2023/) · [**2022** 4 works Works of Denver from 2022](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2022/) · [**2021** 4 works Works of Denver from 2021](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2021/) · [**2020** 5 works Works of Denver from 2020](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2020/)

### 2010s

[**2019** 4 works Works of Denver from 2019](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2019/) · [**2018** 1 work Works of Denver from 2018](https://md.research.elamur.ai/free/en/papers/mccarthy-2018-marrying-universal-dependencies-universal-morphology/) · [**2017** 4 works Works of Denver from 2017](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2017/) · [**2016** 7 works Works of Denver from 2016](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2016/) · [**2015** 5 works Works of Denver from 2015](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2015/) · [**2014** 5 works Works of Denver from 2014](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2014/) · [**2013** 4 works Works of Denver from 2013](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2013/) · [**2012** 1 work Works of Denver from 2012](https://md.research.elamur.ai/free/en/papers/ii-2012-annotating-factive-verbs/) · [**2011** 1 work Works of Denver from 2011](https://md.research.elamur.ai/free/en/papers/pradhan-2011-conll-2011-shared-task-modeling/) · [**2010** 1 work Works of Denver from 2010](https://md.research.elamur.ai/free/en/papers/hwang-2010-domain-independent-semantics-enhancing-semantic/)

### 2000s

[**2008** 2 works Works of Denver from 2008](https://md.research.elamur.ai/free/en/agglomerations/denver/year/2008/) · [**2007** 1 work Works of Denver from 2007](https://md.research.elamur.ai/free/en/papers/pradhan-2007-semeval-2007-task-17-english/) · [**2005** 1 work Works of Denver from 2005](https://md.research.elamur.ai/free/en/papers/calhoun-2005-framework-annotating-information-structure-discourse/) · [**2002** 1 work Works of Denver from 2002](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/) · [**2001** 1 work Works of Denver from 2001](https://md.research.elamur.ai/free/en/papers/schone-2001-knowledge-free-induction-inflectional-morphologies/) · [**2000** 1 work Works of Denver from 2000](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/)

## Fields, topics and concepts

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

📚 Shelves 21 The field of science this work belongs to (OpenAlex fields).[Computer Science · 57 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 57 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 56 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 56 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computational Theory and Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · [Decision Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · [Information Systems and Management · 1 work](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Biochemistry, Genetics and Molecular Biology · 5 works](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · [Molecular Biology · 5 works](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · [Psychology · 4 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Experimental and Cognitive Psychology · 3 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Literature and Literary Theory · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/)+6 more

🧭 Topics 20 Research subjects and languages the work is about.[English · 26 works](https://md.research.elamur.ai/free/en/topics/english/) · [Semantic role labeling · 10 works](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) · [Coreference resolution · 5 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [Morphology · 5 works](https://md.research.elamur.ai/free/en/topics/morphology/) · [Language models & pretraining · 4 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Semantic parsing & meaning representation · 4 works](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) · [Machine translation · 5 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Multilingual & cross-lingual NLP · 5 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Discourse analysis · 4 works](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Dialogue systems · 3 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Speech act theory · 3 works](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) · [Evaluation & benchmarks · 2 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Low-resource languages · 2 works](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · [Universal Dependencies · 2 works](https://md.research.elamur.ai/free/en/topics/universal-dependencies/)+5 more

💡 Concepts 6 Key ideas and methods explained on our concept pages.[Semantic role labeling · 2 works](https://md.research.elamur.ai/free/en/concepts/semantic-role-labeling/) · [Semantic parsing · 1 work](https://md.research.elamur.ai/free/en/concepts/semantic-parsing/) · [Discourse analysis · 2 works](https://md.research.elamur.ai/free/en/concepts/discourse-analysis/) · [Coreference resolution · 1 work](https://md.research.elamur.ai/free/en/concepts/coreference-resolution/) · [Ontology engineering · 1 work](https://md.research.elamur.ai/free/en/concepts/ontology-engineering/) · [Construction grammar · 1 work](https://md.research.elamur.ai/free/en/concepts/construction-grammar/)

Agglomerations: the GHSL Urban Centre Database 2025; populations: the GHSL functional urban areas 2015 (urban centre and commuting zone), else the urban centre, as each page says (European Commission, Joint Research Centre, CC BY 4.0). An institution belongs to the urban centre its coordinates (ROR, OpenAlex) fall in, or one within 5 km. Land: Natural Earth.

- [← back to](https://md.research.elamur.ai/free/en/countries/united-states/) — United States

Updated 24 October 2026