--- title: "John D. Lafferty — author profile and papers" description: "John D. Lafferty, Yale University: 2 papers on Elamur Research (1991–2006), h-index 70 — affiliations, topics, co-authors and who cites them in our…" canonical: "https://research.elamur.ai/free/en/authors/john-d-lafferty/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/john-d-lafferty/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / John D. Lafferty # John D. Lafferty John D. Lafferty works on probabilistic models of language and text. Their papers here cover two settings: speech recognition, where a grammar has to predict the words still to come, and large document collections whose themes shift over the years. The approach is mathematical throughout, from exact derivations for probabilistic grammars to latent-variable models that need approximate posterior inference. The most cited of these papers is [Dynamic Topic Models](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/) (2006), written with [David M. Blei](https://md.research.elamur.ai/free/en/authors/david-m-blei/). It adapts topic modelling to archives such as journals, news or email whose contents change over time. A corpus is cut into time slices, and each topic is allowed to evolve smoothly from one slice to the next. Dirichlet priors link poorly across time, so the model places Gaussian dynamics on the topics' natural parameters instead. The earlier paper, [Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars](https://md.research.elamur.ai/free/en/papers/jelinek-1991-computation-probability-initial-substring-generation/) (1991), written with Jelinek, is purely theoretical. It derives the Left-to-Right Inside algorithm, which gives, for a grammar in Chomsky Normal Form, the chance that a generated sentence starts with a particular string of words. That prefix quantity is what a recognizer requires when a grammar acts as its language model, and the paper falls under [Syntax & grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/). Between the two papers here, the direction runs from grammar-based language modelling for speech toward statistical models of how text collections evolve. Lafferty is currently at [Yale University](https://md.research.elamur.ai/free/en/universities/yale-university/). Earlier affiliations include [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/), where much of the work was done, as well as [University of Chicago](https://md.research.elamur.ai/free/en/universities/university-of-chicago/) and, from 1990 to 1994, [IBM Research - Thomas J. Watson Research Center](https://md.research.elamur.ai/free/en/labs/ibm-research-thomas-j-watson-research-center/). John D. Lafferty has 2 papers in our library, in 1991–2006, most of them on [Syntax & grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/); often with [David M. Blei](https://md.research.elamur.ai/free/en/authors/david-m-blei/). 2 papers in our library · 1991–2006 [OpenAlex](https://openalex.org/A5060219657) · [ORCID](https://orcid.org/0000-0002-5929-220X) · [**h-index 70**](https://openalex.org/authors/A5060219657) · [230 works](https://openalex.org/works?filter=author.id:A5060219657) · [45,139 citations](https://openalex.org/works?filter=author.id:A5060219657&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/john-d-lafferty/#papers) · [🔗 577 citations in our library](https://md.research.elamur.ai/free/en/authors/john-d-lafferty/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Star rank #953](https://md.research.elamur.ai/free/en/paper-stars/page/10/#star-953) 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=John+D.+Lafferty) · [Semantic Scholar](https://www.semanticscholar.org/search?q=John+D.+Lafferty) · [dblp](https://dblp.org/search?q=John+D.+Lafferty) · [ACL Anthology](https://aclanthology.org/search/?q=John+D.+Lafferty) ## Affiliations [🎓**Yale University** New Haven · United States 2018–2026 · 23 papers*current*](https://md.research.elamur.ai/free/en/universities/yale-university/) · [🎓**Carnegie Mellon University** Pittsburgh · United States 1995–2025 · 117 papers](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [🎓**University of Chicago** Chicago · United States 2012–2017 · 25 papers](https://md.research.elamur.ai/free/en/universities/university-of-chicago/) · [🔬**IBM Research - Thomas J. Watson Research Center** Yorktown Heights · United States 1990–1994 · 13 papers](https://md.research.elamur.ai/free/en/labs/ibm-research-thomas-j-watson-research-center/) · [🎓**Princeton University** Princeton · United States 2007–2018 · 4 papers](https://md.research.elamur.ai/free/en/universities/princeton-university/) · [🎓**Johns Hopkins University** Baltimore · United States 2012–2020 · 2 papers](https://md.research.elamur.ai/free/en/universities/johns-hopkins-university/) · [🎓**University of Pennsylvania** Philadelphia · United States 2016–2018 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) · [🏢**IBM** Armonk · United States 1992–1994 · 2 papers](https://md.research.elamur.ai/free/en/companies/ibm/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed. ## Topics [Syntax & grammar formalisms (1)](https://md.research.elamur.ai/free/en/topics/syntax-grammar/) ## Works by year ### 2000s [**2006** 1 paper](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/) ### 1990s [**1991** 1 paper](https://md.research.elamur.ai/free/en/papers/jelinek-1991-computation-probability-initial-substring-generation/) ## Papers The most cited first (OpenAlex and our library). 1. **[Dynamic Topic Models](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/)** *(2006 · 2,508 citations)* Classic topic models such as LDA treat a collection as an unordered bag of documents. That ignores the fact that archives like journals, news and email change their content over time. Blei and Lafferty propose dynamic topic models (DTM), which split a corpus into time slices (e.g., years) and let each of K topics… 2. **[Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars](https://md.research.elamur.ai/free/en/papers/jelinek-1991-computation-probability-initial-substring-generation/)** *(1991 · 150 citations)* Speech recognizers need a language model that predicts the next word from the words already heard. If that model is a stochastic context-free grammar (SCFG), the recognizer must know the probability that the grammar produces a sentence beginning with a given word sequence, not just the probability of a complete… ## Also in our library 2 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 1 co-author with a page, the most papers together first. - [David M. Blei](https://md.research.elamur.ai/free/en/authors/david-m-blei/) ## Cited by Papers of our library cite their works 577 times (of the 140,884 papers whose bibliographies we read); [51 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/john-d-lafferty/cited-by/).