Geoffrey Zweig

118 papers receiving 6.7k citations

Hit Papers

Linguistic Regularities in Continuous ...1997202620062016201319972015201250010001.5k

Peers

Geoffrey Zweig
Comparison fields: 5 of 153
  • Artificial Intelligence 5.8k
  • Computer Vision and Pattern Recognition 1.7k
  • Signal Processing 1.6k
  • Information Systems 821
  • Computer Networks and Communications 338
Replace Martin Karafiát with:
Martin Karafiát Czechia
Dan Klein United States
Alex Acero United States
Édouard Grave Israel
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Geoffrey Zweig relative to Martin Karafiát Czechia Martin Karafiát's profile →
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Countries citing papers authored by Geoffrey Zweig

Since Specialization
Citations

This map shows the geographic impact of Geoffrey Zweig'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 Geoffrey Zweig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoffrey Zweig more than expected).

Fields of papers citing papers by Geoffrey Zweig

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Geoffrey Zweig. 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 Geoffrey Zweig. The network helps show where Geoffrey Zweig may publish in the future.

Co-authorship network of co-authors of Geoffrey Zweig

This figure shows the co-authorship network connecting the top 25 collaborators of Geoffrey Zweig. A scholar is included among the top collaborators of Geoffrey Zweig 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 Geoffrey Zweig. Geoffrey Zweig is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation.
5
2 146
3 151
4 59
5 72
6 34
7 131
8
An introduction to computational networks and the computational network toolkit (invited talk).
3
9 195
10
Linguistic Regularities in Continuous Space Word Representationsbreakdown →
1635
11
Combining Heterogeneous Models for Measuring Relational Similarity
29
12 168
13
Polarity Inducing Latent Semantic Analysis
48
14
A Challenge Set for Advancing Language Modeling
18
15
Computational Approaches to Sentence Completion
19
16
Linear Feature Space Transformations for Speaker Adaptation
3
17 2
18
Speech recognition with Dynamic Bayesian Networks
137
19
Probabilistic modeling with Bayesian networks for automatic speech recognition.
2
20 19

About Geoffrey Zweig

Geoffrey Zweig is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 119 papers that have together received 7.4k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (78 papers), Natural Language Processing Techniques (54 papers) and Speech and Audio Processing (35 papers). The work is most often cited by research in Artificial Intelligence (5.8k citations), Signal Processing (1.6k citations) and Computer Vision and Pattern Recognition (1.7k citations). Geoffrey Zweig has collaborated with scholars based in United States, United Kingdom and Czechia. Frequent co-authors include Tomáš Mikolov, Wen-tau Yih, Andrei Broder, Mark S. Manasse, Dong Yu, Kaisheng Yao, George Saon, Xiaodong He, Li Deng and Daniel Povey. Their work appears in journals such as Operations Research, IEEE Journal of Selected Topics in Signal Processing and IEEE Transactions on Audio Speech and Language Processing.

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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