[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/marie-catherine-de-marneffe/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Marie-Catherine de Marneffe # Marie-Catherine de Marneffe 16 paper cards in Mova's library · 2006–2021 Marie-Catherine de Marneffe wrote 16 of the papers Mova reads, in 2006–2021, most of them on [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/), [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/), [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/); often with [Christopher D. Manning](https://md.mova.elamur.ai/free/en/docs/papers/authors/christopher-d-manning/), [Filip Ginter](https://md.mova.elamur.ai/free/en/docs/papers/authors/filip-ginter/), [Joakim Nivre](https://md.mova.elamur.ai/free/en/docs/papers/authors/joakim-nivre/). [OpenAlex](https://openalex.org/A5112862689) · [**h-index 31**](https://openalex.org/authors/A5112862689) · [8698 citations](https://openalex.org/authors/A5112862689) · [167 works](https://openalex.org/works?filter=author.id:A5112862689) — openalex, 2026-10-18 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Marie-Catherine+de+Marneffe) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Marie-Catherine+de+Marneffe) · [dblp](https://dblp.org/search?q=Marie-Catherine+de+Marneffe) · [ACL Anthology](https://aclanthology.org/search/?q=Marie-Catherine+de+Marneffe) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Marie-Catherine+de+Marneffe) Topics: [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/) 5 · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 4 · [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) 2 · [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/) 1 · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 1 ## Papers 1. **[Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/demarneffe-2021-universal-dependencies/)** *(2021)* This article sets out the linguistic theory behind Universal Dependencies (UD), which is at once a cross-linguistically consistent morphosyntactic annotation scheme, an open research community and a growing collection of treebanks. 2. **[He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://md.mova.elamur.ai/free/en/docs/papers/jiang-2021-he-thinks-he-knows-better/)** *(2021)* The task is to predict, on a scale from −3 to +3, how factual an embedded event is. The authors fine-tune BERT on seven English datasets, among them FactBank, MEANTIME, UW, UDS-IH2, CommitmentBank (CB), Ross and Pavlick (RP) and MegaVeridicality. BERT does well on most of them, and sets new results on FactBank and… 3. **[Contextualized Embeddings for Enriching Linguistic Analyses on Politeness](https://md.mova.elamur.ai/free/en/docs/papers/aljanaideh-2020-contextualized-embeddings-enriching-linguistic-analyses/)** *(2020)* Politeness studies count words such as please, you or hedges regardless of how they are used. Here every occurrence of a word in the Stanford Politeness Corpus (Wikipedia and Stack Exchange requests, polite/impolite quartiles) gets a contextual BERT vector, and the occurrences are split by a decision-tree-like… 4. **[Evaluative adjective sentences: A question-based analysis of projection](https://md.mova.elamur.ai/free/en/docs/papers/tonhauser-2020-evaluative-adjective-sentences-question-based/)** *(2020)* In "Kim was smart to watch the movie", the prejacent (Kim watched the movie) is usually analysed as a lexical presupposition. 5. **[Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-nivre-universal-dependencies-v2-evergrowing-multilingual/)** *(2020)* The paper describes the UD v2 guidelines and the state of the collection at release 2.5: 157 treebanks in 90 languages, built by more than 300 contributors. 6. **[The CommitmentBank: Investigating projection in naturally occurring discourse](https://md.mova.elamur.ai/free/en/docs/papers/enprag-2019-marneffe-commitmentbank-investigating-projection-naturally/)** *(2019)* The paper builds a corpus of 1,200 naturally occurring discourses, from the Wall Street Journal, the fiction part of the BNC and Switchboard dialogue. 7. **[CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-zeman-conll-2017-shared-task-multilingual/)** *(2017)* The shared task asks systems to parse raw text, with no gold tokenization, sentence splitting or tags, into UD trees for 49 languages. 8. **[Assessing the Annotation Consistency of the Universal Dependencies Corpora](https://md.mova.elamur.ai/free/en/docs/papers/marneffe-2017-assessing-annotation-consistency-universal-dependencies/)** *(2017)* UD treebanks are produced quickly and for many languages, so manual quality control is hard; the paper looks for a way to find inconsistent annotation automatically. 9. **[Universal Dependencies v1: A Multilingual Treebank Collection](https://md.mova.elamur.ai/free/en/docs/papers/ud-2016-nivre-universal-dependencies-v1-multilingual-treebank/)** *(2016)* The paper is the reference for the first UD release. It sets out the design principles: dependency-based and lexicalist annotation, the recoverability principle (every syntactic word must map back to the raw text), content words as the backbone of the tree with function words as dependents, and a closed inventory of… 10. **[Universal Stanford dependencies: A cross-linguistic typology](https://md.mova.elamur.ai/free/en/docs/papers/ud-2014-marneffe-universal-stanford-dependencies-cross-linguistic/)** *(2014)* The paper reconstructs the typology behind Stanford Dependencies and proposes a two-layer taxonomy: 42 universal grammatical relations that are broadly attested across languages, with language-specific subtypes added under them. 11. **[More Constructions, More Genres: Extending Stanford Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/bank-2013-marneffe-more-constructions-more-genres-extending/)** *(2013)* The Stanford scheme was built on newswire and gives little analysis to constructions that are rare there but common in web text: tough adjectives, free relatives, comparatives, small clauses, questions, imperatives, fragments and lists. 12. **[Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy](https://md.mova.elamur.ai/free/en/docs/papers/bank-2010-cer-parsing-stanford-dependencies-trade-offs/)** *(2010)* The authors compare ways of producing Stanford Dependencies quickly. One group is constituency parsers (Stanford, Charniak, Charniak-Johnson reranker, Bikel, Berkeley) followed by a conversion to dependencies; the other is dependency parsers trained directly on the basic dependencies (MaltParser's Nivre variants… 13. **[Was It Good? It Was Provocative.'' Learning the Meaning of Scalar Adjectives](https://md.mova.elamur.ai/free/en/docs/papers/marneffe-2010-was-it-good-it-was/)** *(2010)* Indirect answers to yes/no questions often do not say "yes" or "no" outright. "Was it good? It was provocative" leaves the answer open, while "It was great" does not. The authors mine two kinds of evidence. First, scale order between gradable adjectives comes from online IMDB reviews: a word's expected star rating… 14. **[Multi-word expressions in textual inference: Much ado about nothing?](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2009-marneffe-multi-word-expressions-textual-inference/)** *(2009)* The paper asks whether multi-word expressions (MWEs) matter for recognising textual entailment (RTE). It uses the manual Microsoft Research word alignments for the RTE2 data and counts the MWEs that appear as multi-word alignment links. Only 81 (dev) and 60 (test) true MWEs occur, in 8.2% of the 1,600 sentence pairs… 15. **[The Stanford Typed Dependencies Representation](https://md.mova.elamur.ai/free/en/docs/papers/marneffe-2008-stanford-typed-dependencies-representation/)** *(2008)* The paper describes Stanford typed dependencies (SD), a dependency scheme designed to make parser output usable by people outside linguistics who need to extract relations from text. 16. **[Generating Typed Dependency Parses from Phrase Structure Parses](https://md.mova.elamur.ai/free/en/docs/papers/map-2006-marneffe-generating-typed-dependency-parses-phrase/)** *(2006)* The paper describes a rule system that turns Penn Treebank phrase-structure trees, as produced by the Stanford parser, into typed dependencies. ## Co-authors (82) - [Christopher D. Manning](https://md.mova.elamur.ai/free/en/docs/papers/authors/christopher-d-manning/) ×10 - [Filip Ginter](https://md.mova.elamur.ai/free/en/docs/papers/authors/filip-ginter/) ×5 - [Joakim Nivre](https://md.mova.elamur.ai/free/en/docs/papers/authors/joakim-nivre/) ×5 - [Daniel Zeman](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-zeman/) ×4 - [Jan Hajič](https://md.mova.elamur.ai/free/en/docs/papers/authors/jan-hajic/) ×3 - [Natalia Silveira](https://md.mova.elamur.ai/free/en/docs/papers/authors/natalia-silveira/) ×3 - [Sampo Pyysalo](https://md.mova.elamur.ai/free/en/docs/papers/authors/sampo-pyysalo/) ×3 - [Francis Tyers](https://md.mova.elamur.ai/free/en/docs/papers/authors/francis-tyers/) ×2 - [Jenna Kanerva](https://md.mova.elamur.ai/free/en/docs/papers/authors/jenna-kanerva/) ×2 - [Judith Tonhauser](https://md.mova.elamur.ai/free/en/docs/papers/authors/judith-tonhauser/) ×2 - [Sebastian Schuster](https://md.mova.elamur.ai/free/en/docs/papers/authors/sebastian-schuster/) ×2 - [Slav Petrov](https://md.mova.elamur.ai/free/en/docs/papers/authors/slav-petrov/) ×2 - [Timothy Dozat](https://md.mova.elamur.ai/free/en/docs/papers/authors/timothy-dozat/) ×2 - [Ahmad Aljanaideh](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmad-aljanaideh/) - Ali Elkahky - [Aljoscha Burchardt](https://md.mova.elamur.ai/free/en/docs/papers/authors/aljoscha-burchardt/) - Anna Missilä - [Anna Nedoluzhko](https://md.mova.elamur.ai/free/en/docs/papers/authors/anna-nedoluzhko/) - Antonio Stella - Atsuko Shimada - Bill MacCartney - Çağrı Çöltekin - Chris Manning - [Christopher Potts](https://md.mova.elamur.ai/free/en/docs/papers/authors/christopher-potts/) - [Dan Jurafsky](https://md.mova.elamur.ai/free/en/docs/papers/authors/dan-jurafsky/) - Daniel Cer - [Dima Taji](https://md.mova.elamur.ai/free/en/docs/papers/authors/dima-taji/) - Elena Badmaeva - [Emily Pitler](https://md.mova.elamur.ai/free/en/docs/papers/authors/emily-pitler/) - Eric Fosler-Lussier - Esha Banerjee - Georg Rehm - Gustavo Mendonça - [Hans Uszkoreit](https://md.mova.elamur.ai/free/en/docs/papers/authors/hans-uszkoreit/) - Hector Fernandez Alcalde - Héctor Martínez Alonso - Herman Leung - Hiroshi Kanayama - Jan Hajič jr. - Jana Strnadová - Jaroslava Hlaváčová - Jesse Kirchner - Josie Li - [Judith Degen](https://md.mova.elamur.ai/free/en/docs/papers/authors/judith-degen/) - Juhani Luotolahti - [Katri Haverinen](https://md.mova.elamur.ai/free/en/docs/papers/authors/katri-haverinen/) - Katrin Marheinecke - Kim Harris - [Kira Droganova](https://md.mova.elamur.ai/free/en/docs/papers/authors/kira-droganova/) - Mandy Simons - Manuela Sanguinetti - [Maria Simi](https://md.mova.elamur.ai/free/en/docs/papers/authors/maria-simi/) - [Martin Popel](https://md.mova.elamur.ai/free/en/docs/papers/authors/martin-popel/) - [Martin Potthast](https://md.mova.elamur.ai/free/en/docs/papers/authors/martin-potthast/) - Matias Grioni - Memduh Gokirmak - Michael Mandl - [Milan Straka](https://md.mova.elamur.ai/free/en/docs/papers/authors/milan-straka/) - Miriam Connor - [Mohammed Attia](https://md.mova.elamur.ai/free/en/docs/papers/authors/mohammed-attia/) - Nanjiang Jiang - [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/) - Rattima Nitisaroj - [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) - Ruli Manurung - [Ryan McDonald](https://md.mova.elamur.ai/free/en/docs/papers/authors/ryan-mcdonald/) - [Samuel R. Bowman](https://md.mova.elamur.ai/free/en/docs/papers/authors/samuel-r-bowman/) - Saran Lertpradit - [Sebastian Padó](https://md.mova.elamur.ai/free/en/docs/papers/authors/sebastian-pado/) - Silvie Cinková - Siva Reddy - Sookyoung Kwak - Stina Ojala - Tatiana Lando - Tolga Kayadelen - Umut Sulubacak - Václava Kettnerová - Valeria de Paiva - [Vivien Macketanz](https://md.mova.elamur.ai/free/en/docs/papers/authors/vivien-macketanz/) - [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/) - [Zdeňka Urešová](https://md.mova.elamur.ai/free/en/docs/papers/authors/zdenka-uresova/) - Zhuoran Yu - [← previous Marie Candito](https://md.mova.elamur.ai/free/en/docs/papers/authors/marie-candito/) - [next → Marie Pauline Krielke](https://md.mova.elamur.ai/free/en/docs/papers/authors/marie-pauline-krielke/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)