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# Michael White

6 paper cards in Mova's library · 2002–2016

Michael White wrote 6 of the papers Mova reads, in 2002–2016, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/), [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/), [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/); often with [Rajakrishnan Rajkumar](https://md.mova.elamur.ai/free/en/docs/papers/authors/rajakrishnan-rajkumar/), [Amanda Stent](https://md.mova.elamur.ai/free/en/docs/papers/authors/amanda-stent/), [Anja Belz](https://md.mova.elamur.ai/free/en/docs/papers/authors/anja-belz/).

[OpenAlex](https://openalex.org/A5025147385) · [ORCID](https://orcid.org/0000-0002-3062-1719) · [**h-index 35**](https://openalex.org/authors/A5025147385) · [12011 citations](https://openalex.org/authors/A5025147385) · [223 works](https://openalex.org/works?filter=author.id:A5025147385) — 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=Michael+White) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Michael+White) · [dblp](https://dblp.org/search?q=Michael+White) · [ACL Anthology](https://aclanthology.org/search/?q=Michael+White) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Michael+White)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 6 · [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/) 1 · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/) 1

## Papers

1. **[Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization](https://md.mova.elamur.ai/free/en/docs/papers/king-2016-enhancing-ptb-universal-dependencies-grammar/)** *(2016)* Grammar-based realisers work well when their input carries long-distance dependencies and coordination, but standard UD conversions from the Penn Treebank lose these, because the conversion ignores the traces.
2. **[The First Surface Realisation Shared Task: Overview and Evaluation Results](https://md.mova.elamur.ai/free/en/docs/papers/belz-2011-first-surface-realisation-shared-task/)** *(2011)* The paper reports the first surface realisation shared task. Teams received a common input (a shallow track and a deep track, both derived from Penn Treebank parses) and produced English sentences. Five teams submitted six systems, and the corpus sentences were scored as a human topline. Systems were judged…
3. **[Linguistically Motivated Complementizer Choice in Surface Realization](https://md.mova.elamur.ai/free/en/docs/papers/rajkumar-2011-linguistically-motivated-complementizer-choice-surface/)** *(2011)* The optional complementizer "that" (as in "he said that ...") is left out in about two thirds of Penn Treebank cases, and its presence changes fluency and sometimes meaning.
4. **[A More Precise Analysis of Punctuation for Broad-Coverage Surface Realization with CCG](https://md.mova.elamur.ai/free/en/docs/papers/white-2008-more-precise-analysis-punctuation-broad/)** *(2008)* The paper gives punctuation a lexical analysis in a broad-coverage CCG realisation grammar. In CCGbank, punctuation marks carry categories taken from their tags and sit inside binary rules, which is enough for parsing but overgenerates in realisation: a comma category can wrap a sentence in commas or repeat itself.…
5. **[Towards broad coverage surface realization with CCG](https://md.mova.elamur.ai/free/en/docs/papers/white-2007-broad-coverage-surface-realization-ccg/)** *(2007)* A progress report on the first broad-coverage English surface realiser for Combinatory Categorial Grammar, built on OpenCCG.
6. **[Learning Domain-Specific Transfer Rules: An Experiment with Korean to English Translation](https://md.mova.elamur.ai/free/en/docs/papers/lavoie-2002-learning-domain-specific-transfer-rules/)** *(2002)* An MT system that learns lexico-structural transfer rules from parsed bitexts, for domain-specific Korean-to-English translation.

## Co-authors (10)

- [Rajakrishnan Rajkumar](https://md.mova.elamur.ai/free/en/docs/papers/authors/rajakrishnan-rajkumar/) ×3
- [Amanda Stent](https://md.mova.elamur.ai/free/en/docs/papers/authors/amanda-stent/)
- [Anja Belz](https://md.mova.elamur.ai/free/en/docs/papers/authors/anja-belz/)
- [Benoit Lavoie](https://md.mova.elamur.ai/free/en/docs/papers/authors/benoit-lavoie/)
- David L. King
- Deirdre Hogan
- Dominic Espinosa
- Eric Kow
- Scott Martin
- Tanya Korelsky
- [← previous Michael Strube](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-strube/)
- [next → Michal Novák](https://md.mova.elamur.ai/free/en/docs/papers/authors/michal-novak/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)
