--- title: "Michael Collins — papers and cards" description: "Michael Collins: 5 paper cards in the Research library (1995–2004), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/michael-collins/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/michael-collins/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Michael Collins # Michael Collins 5 paper cards in the Research library · 1995–2004 Michael Collins wrote 5 of the papers Mova reads, in 1995–2004, most of them on [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/); often with Brian Roark, James Brooks, Terry Koo. [OpenAlex](https://openalex.org/A5079061237) · [ORCID](https://orcid.org/0000-0003-0997-1527) · [**h-index 48**](https://openalex.org/authors/A5079061237) · [16537 citations](https://openalex.org/authors/A5079061237) · [214 works](https://openalex.org/works?filter=author.id:A5079061237) — openalex-snapshot, 2026-10-14 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+Collins) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Michael+Collins) · [dblp](https://dblp.org/search?q=Michael+Collins) · [ACL Anthology](https://aclanthology.org/search/?q=Michael+Collins) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Michael+Collins) Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) 1 ## Ideas Mova took (2) From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave. - **kept** — en/src/parse.rs: add a distance feature for each candidate arc (direction, binned distance, and a count of verbs or punctuation between the two words); measure cv LAS on the train folds, then EWT test once per release. *([Head-Driven Statistical Models for Natural Language Parsing](https://md.research.elamur.ai/free/en/papers/cl-2003-collins-head-driven-statistical-models-natural/) · verbs/punctuation between the pair, on top of conj+subcat: 79.80 -> 79.90 (+0.10, noise level), gold tags 84.41 -> 84.77 (3 shuffles), construction set 62.7…)* - **kept** — en/src/parse.rs: add a subcategorization feature for a verb head, namely which of its obligatory dependents (nsubj, obj, iobj, ccomp, xcomp) are already attached; measure cv LAS and the LAS of verb-headed arcs with en ud-eval. *([Head-Driven Statistical Models for Natural Language Parsing](https://md.research.elamur.ai/free/en/papers/cl-2003-collins-head-driven-statistical-models-natural/) · subcat bit set of attached core dependents, on top of conj: 79.37 -> 79.80 (+0.43, 3 shuffles), gold tags 84.55; commit 1c729b451)* ## Papers 1. **[Incremental Parsing with the Perceptron Algorithm](https://md.research.elamur.ai/free/en/papers/collins-2004-incremental-parsing-perceptron-algorithm/)** *(2004)* `measured` The paper asks whether an unnormalised discriminative model can work well together with heuristic search in parsing. 2. **[Head-Driven Statistical Models for Natural Language Parsing](https://md.research.elamur.ai/free/en/papers/cl-2003-collins-head-driven-statistical-models-natural/)** *(2003)* `2 ideas kept` Collins gives three generative models for lexicalized parsing of the Penn Treebank. A tree is generated top-down from its head: at each step the model chooses the head's modifiers on each side, with distance, subcategorization frames, and for Model 3 traces for wh-movement. Model 1 reaches 87.7% labelled precision… 3. **[Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms](https://md.research.elamur.ai/free/en/papers/collins-2002-discriminative-training-methods-hidden-markov/)** *(2002)* `measured` The paper proposes training sequence-labelling models, such as part-of-speech taggers and base-NP chunkers, with a perceptron instead of maximum-entropy models or CRFs. 4. **[Discriminative Reranking for Natural Language Parsing](https://md.research.elamur.ai/free/en/papers/collins-2000-discriminative-reranking-natural-language-parsing/)** *(2000)* `measured` The work splits parsing into two stages. A base probabilistic parser proposes a short list of candidate trees for each sentence together with their probabilities, and a second, discriminatively trained model reorders the list. Because the second model only scores complete candidates, it can use arbitrary, overlapping… 5. **[Prepositional Phrase Attachment through a Backed-off Model](https://md.research.elamur.ai/free/en/papers/aclbulk-1995-collins-prepositional-phrase-attachment-through-backed/)** *(1995)* The paper treats the choice between noun and verb attachment of a prepositional phrase as a language-modeling problem. ## Co-authors (3) - Brian Roark - James Brooks - Terry Koo ## More papers in the library (14) Pages of the Research library: our summary, the ideas, references and who cites each. - [Coreference Resolution through a seq2seq Transition-Based System](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/) — Bernd Bohnet, Chris Alberti, Michael Collins · 2023 - [Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/) — Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al. · 2023 - [Sparse, Dense, and Attentional Representations for Text Retrieval](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/) — Yi Luan, Jacob Eisenstein, Kristina Toutanova, et al. · 2021 - [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://md.research.elamur.ai/free/en/papers/clark-2020-tydi-qa-benchmark-information-seeking/) — Jonathan H. Clark, Eunsol Choi, Michael Collins, et al. · 2020 - [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/) — Christopher Clark, Kenton Lee, Ming-Wei Chang, et al. · 2019 - [Natural Questions: A Benchmark for Question Answering Research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/) — Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al. · 2019 - [Synthetic QA Corpora Generation with Roundtrip Consistency](https://md.research.elamur.ai/free/en/papers/alberti-2019-synthetic-qa-corpora-generation-roundtrip/) — Chris Alberti, Daniel Andor, Emily Pitler, et al. · 2019 - [Globally Normalized Transition-Based Neural Networks](https://md.research.elamur.ai/free/en/papers/andor-2016-globally-normalized-transition-based-neural/) — Daniel Andor, Chris Alberti, David Weiss, et al. · 2016 - [Simple Semi-supervised Dependency Parsing](https://md.research.elamur.ai/free/en/papers/koo-2008-simple-semi-supervised-dependency-parsing/) — Terry Koo, Xavier Carreras, Michael Collins · 2008 - [Chinese Syntactic Reordering for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2007-chinese-syntactic-reordering-statistical-machine/) — Chao Wang, Michael Collins, Philipp Koehn · 2007 - [Clause Restructuring for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/collins-2005-clause-restructuring-statistical-machine-translation/) — Michael Collins, Philipp Koehn, Ivona Kučerová · 2005 - [A Statistical Parser for Czech](https://md.research.elamur.ai/free/en/papers/collins-1999-statistical-parser-czech/) — Michael Collins, Jan Hajic, Lance Ramshaw, et al. · 1999 - [Unsupervised Models for Named Entity Classification](https://md.research.elamur.ai/free/en/papers/collins-1999-unsupervised-models-named-entity-classification/) — Michael Collins, Yoram Singer · 1999 - [Three Generative, Lexicalised Models for Statistical Parsing](https://md.research.elamur.ai/free/en/papers/collins-1997-three-generative-lexicalised-models-statistical/) — Michael Collins · 1997