Benjamin Börschinger

584 citations
13 papers · 297 · h-index 7

Impact in

Papers in

Benjamin Börschinger

12 papers receiving 280 citations

Peers

Benjamin Börschinger
Comparison fields: 5 of 51
  • Artificial Intelligence 257
  • Developmental and Educational Psychology 26
  • Computer Vision and Pattern Recognition 42
  • Experimental and Cognitive Psychology 17
  • Management Science and Operations Research 15
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Clara Meister Switzerland
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Citations per year

Countries citing papers authored by Benjamin Börschinger

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Börschinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Benjamin Börschinger, 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 Benjamin Börschinger Line = papers co-authored together Benjamin Börschinger links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing
2015193
2
Reducing Grounded Learning Tasks To Grammatical Inference
201126
3 202223
4 201417
5
Using Rejuvenation to Improve Particle Filtering for Bayesian Word Segmentation
201211
6
A joint model of word segmentation and phonological variation for English word-final /t/-deletion
20136
7 20126
8
Unsupervised Word Segmentation in Context
20144
9
Collocations in Multilingual Natural Language Generation: Lexical Functions meet Lexical Functional Grammar
20114
10
Modeling online word segmentation performance in structured artificial languages
20123
11
Implementing lexical functions in XLE
20122
12
Why is English so easy to segment
20131
13 20151

About Benjamin Börschinger

Benjamin Börschinger is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics, Developmental and Educational Psychology and Information Systems, having authored 13 papers that have together received 297 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (12 papers), Topic Modeling (6 papers), Speech and dialogue systems (5 papers), Language Development and Disorders (2 papers), Syntax, Semantics, Linguistic Variation (2 papers), Phonetics and Phonology Research (2 papers), Speech Recognition and Synthesis (2 papers) and Expert finding and Q&A systems (1 paper). The work is most often cited by research in Artificial Intelligence (257 citations), Developmental and Educational Psychology (26 citations), Computer Vision and Pattern Recognition (42 citations), Experimental and Cognitive Psychology (17 citations) and Management Science and Operations Research (15 citations). Benjamin Börschinger has collaborated with scholars based in Australia, Germany and United States. Frequent co-authors include Mark Johnson, John K. Pate, Lan Du, Mark Steedman, Massimiliano Ciaramita, Zhendong Zhao, Bevan Jones, Tal Schuster, Mark Dras and Katherine Demuth. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Cognitive Science, Edinburgh Research Explorer, Empirical Methods in Natural Language Processing and Columbia Academic Commons (Columbia University).

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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