Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Evaluating deep learning architectures for Speech Emotion Recognition
2017387 citationsHaytham M. Fayek, Margaret Lech et al.Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Lawrence Cavedon
Since
Specialization
Citations
This map shows the geographic impact of Lawrence Cavedon'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 Cavedon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lawrence Cavedon more than expected).
Fields of papers citing papers by Lawrence Cavedon
This network shows the impact of papers produced by Lawrence Cavedon. 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 Cavedon. The network helps show where Lawrence Cavedon may publish in the future.
Co-authorship network of co-authors of Lawrence Cavedon
This figure shows the co-authorship network connecting the top 25 collaborators of Lawrence Cavedon.
A scholar is included among the top collaborators of Lawrence Cavedon based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Lawrence Cavedon. Lawrence Cavedon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Fayek, Haytham M., Margaret Lech, & Lawrence Cavedon. (2017). Evaluating deep learning architectures for Speech Emotion Recognition. Neural Networks. 92. 60–68.387 indexed citations breakdown →
Spina, Damiano, Maria Maistro, Yongli Ren, et al.. (2017). Understanding user behavior in job and talent search: an initial investigation. RMIT Research Repository (RMIT University Library). 2311. 1–5.10 indexed citations
Martínez, David, Andrew MacKinlay, Diego Mollá, Lawrence Cavedon, & Karin Verspoor. (2012). Simple similarity-based question answering strategies for biomedical text. 1178. 1–13.4 indexed citations
8.
Wong, Wilson, Lawrence Cavedon, John Thangarajah, & Lin Padgham. (2012). Strategies for Mixed-Initiative Conversation Management using Question-Answer Pairs. RMIT Research Repository (RMIT University Library). 2821–2834.8 indexed citations
9.
Kim, Su Nam, Lawrence Cavedon, & Timothy Baldwin. (2012). Classifying Dialogue Acts in Multi-party Live Chats. Waseda University Repository (Waseda University). 463–472.13 indexed citations
10.
Winter, Stephan, Kai‐Florian Richter, Tim Baldwin, et al.. (2011). Location-based mobile games for spatial knowledge acquisition. Minerva Access (University of Melbourne). 780.20 indexed citations
11.
Cavedon, Lawrence, et al.. (2010). Generating Shifting Sentiment for a Conversational Agent. RMIT Research Repository (RMIT University Library). 89–97.13 indexed citations
12.
Stokes, Nicola, et al.. (2007). Entity-Based Relevance Feedback for Genomic List Answer Retrieval.. Text REtrieval Conference.13 indexed citations
13.
Moffat, Alistair, et al.. (2006). Exploring Probabilistic Toponym Resolution for Geographical Information Retrieval.16 indexed citations
14.
Cavedon, Lawrence, Fuliang Weng, Harry Bratt, et al.. (2005). Developing a Conversational In-Car Dialog System.5 indexed citations
15.
Cavedon, Lawrence, Zakaria Maamar, David Martín, & Boualem Benatallah. (2005). Extending Web Services Technologies: The Use of Multi-Agent Approaches (Multiagent Systems, Artificial Societies, and Simulated Organizations). Springer eBooks.6 indexed citations
16.
Cheng, Hua, et al.. (2005). A Wizard of Oz Framework for Collecting Spoken Human Computer Dialogs: An Experiment Procedure for the Design and Testing of Natural Language In-Vehicle Technology Systems.14 indexed citations
17.
Cheng, Hua, Lawrence Cavedon, & Robert Dale. (2004). Generating Navigation Information Based on the Driver's Route Knowledge. International Conference on Computational Linguistics. 31–38.9 indexed citations
18.
Lemon, Oliver, Lawrence Cavedon, & Barbara F. Kelly. (2003). Managing Dialogue Interaction: A Multi-Layered Approach. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 168–177.18 indexed citations
19.
Dignum, Frank, David Morley, Liz Sonenberg, & Lawrence Cavedon. (2000). Towards Socially Sophisticated BDI Agents. International Journal of Electronic Commerce.49 indexed citations
20.
Cavedon, Lawrence. (1989). Continuity, Consistency, and Completeness Properties for Logic Programs.. International Conference on Lightning Protection. 571–584.35 indexed citations
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.