Lawrence Phillips

426 citations
20 papers · 91 · h-index 6

Impact in

Papers in

Lawrence Phillips

15 papers receiving 79 citations

Peers

Lawrence Phillips
Comparison fields: 5 of 34
  • Developmental and Educational Psychology 39
  • Experimental and Cognitive Psychology 29
  • Artificial Intelligence 36
  • Cognitive Neuroscience 21
  • Signal Processing 11
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Seunghun J. Lee Japan
Jürg Fleischer Germany
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Marjorie K. M. Chan United States
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Citations per field
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Citations per year

Countries citing papers authored by Lawrence Phillips

Since Specialization
Citations

This map shows the geographic impact of Lawrence Phillips's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lawrence Phillips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lawrence Phillips more than expected).

Fields of papers citing papers by Lawrence Phillips

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lawrence Phillips. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lawrence Phillips. The network helps show where Lawrence Phillips may publish in the future.

Co-authors

The 10 scholars most cited alongside Lawrence Phillips, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lawrence Phillips Line = papers co-authored together Lawrence Phillips links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201520
2 201114
3 20149
4
Less is More in Bayesian Word Segmentation: When cognitively plausible learners outperform the ideal
20128
5 20188
6 20176
7
A mighty mass of brick and smoke : Victorian and Edwardian representations of London
20075
8
Bayesian inference as a viable cross-linguistic word segmentation strategy: It’s all about what’s useful
20145
9 20004
10 20123
11 20152
12 20042
13 20021
14 20071
15 20091
16 20051
17 19981
18 20080
19 20250
20 20070

About Lawrence Phillips

Lawrence Phillips is a scholar working on Artificial Intelligence, Literature and Literary Theory, Developmental and Educational Psychology, Anthropology and Signal Processing, having authored 20 papers that have together received 91 indexed citations. Recurring topics across this work include Speech and dialogue systems (5 papers), Language Development and Disorders (5 papers), Speech Recognition and Synthesis (3 papers), Topic Modeling (3 papers), Speech and Audio Processing (2 papers), Natural Language Processing Techniques (2 papers), Phonetics and Phonology Research (2 papers) and Modernist Literature and Criticism (2 papers). The work is most often cited by research in Developmental and Educational Psychology (39 citations), Experimental and Cognitive Psychology (29 citations), Artificial Intelligence (36 citations), Cognitive Neuroscience (21 citations) and Signal Processing (11 citations). Lawrence Phillips has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include Lisa Pearl, David B. Pisoni, Tessa Bent, Svitlana Volkova, Stephen Ranshous, Sonya Andermahr, Nathan O. Hodas, Dustin Arendt, Kyle Shaffer and Rory Donovan-Maiye. Their work appears in journals such as Cognitive Science, Race & Class, Scrutiny2, Journal of Experimental Psychology Human Perception & Performance and Critical Survey.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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