Helmut Schmid

3.0k total citations
60 papers, 1.4k citations indexed

About

Helmut Schmid is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Helmut Schmid has authored 60 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Artificial Intelligence, 5 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Helmut Schmid's work include Natural Language Processing Techniques (50 papers), Topic Modeling (45 papers) and Speech and dialogue systems (11 papers). Helmut Schmid is often cited by papers focused on Natural Language Processing Techniques (50 papers), Topic Modeling (45 papers) and Speech and dialogue systems (11 papers). Helmut Schmid collaborates with scholars based in Germany, United Kingdom and Hungary. Helmut Schmid's co-authors include Alexander Fraser, Hinrich Schütze, Nadir Durrani, Florian Laws, Hassan Sajjad, Ulrich Heid, Timo Schick, Philipp Koehn, Richárd Farkas and Sabine Schulte im Walde and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computational Linguistics and Language Resources and Evaluation.

In The Last Decade

Helmut Schmid

58 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Helmut Schmid Germany 21 1.3k 181 122 73 55 60 1.4k
Katja Markert United Kingdom 21 940 0.7× 149 0.8× 129 1.1× 107 1.5× 41 0.7× 57 1.2k
Maxim Krikun France 7 806 0.6× 372 2.1× 32 0.3× 42 0.6× 47 0.9× 15 1.0k
Adam Meyers United States 16 1.6k 1.2× 56 0.3× 111 0.9× 157 2.2× 178 3.2× 61 1.7k
Ann Bies United States 14 1.3k 1.0× 84 0.5× 122 1.0× 107 1.5× 176 3.2× 44 1.4k
Christian Jacquemin France 16 627 0.5× 140 0.8× 154 1.3× 97 1.3× 145 2.6× 67 880
Johan Hall Sweden 15 1.8k 1.4× 169 0.9× 77 0.6× 161 2.2× 139 2.5× 31 2.0k
Ashish Vaswani United States 14 590 0.5× 196 1.1× 20 0.2× 45 0.6× 84 1.5× 27 863
Jens Nilsson Sweden 16 2.0k 1.6× 130 0.7× 99 0.8× 176 2.4× 181 3.3× 30 2.2k
John B. Lowe United States 8 1.7k 1.4× 147 0.8× 159 1.3× 193 2.6× 142 2.6× 15 2.0k
Daisuke Kawahara Japan 19 1.3k 1.1× 127 0.7× 39 0.3× 193 2.6× 78 1.4× 139 1.5k

Countries citing papers authored by Helmut Schmid

Since Specialization
Citations

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

Fields of papers citing papers by Helmut Schmid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Helmut Schmid

This figure shows the co-authorship network connecting the top 25 collaborators of Helmut Schmid. A scholar is included among the top collaborators of Helmut Schmid 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 Helmut Schmid. Helmut Schmid 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
1.
Schmid, Helmut, et al.. (2024). CUTE: Measuring LLMs’ Understanding of Their Tokens. 3017–3026. 1 indexed citations
2.
Färber, Michael, et al.. (2024). GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network. 3987–4001. 1 indexed citations
3.
Kassner, Nora, et al.. (2023). Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages. Open access LMU (Ludwid Maxmilian's Universitat Munchen). 1082–1117. 6 indexed citations
4.
Schmid, Helmut, et al.. (2023). Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages. Open access LMU (Ludwid Maxmilian's Universitat Munchen). 8320–8340. 11 indexed citations
5.
Durrani, Nadir, Philipp Koehn, Helmut Schmid, & Alexander Fraser. (2014). Investigating the Usefulness of Generalized Word Representations in SMT. International Conference on Computational Linguistics. 421–432. 20 indexed citations
6.
Fraser, Alexander, et al.. (2013). Munich-Edinburgh-Stuttgart Submissions at WMT13: Morphological and Syntactic Processing for SMT. Workshop on Statistical Machine Translation. 232–239. 9 indexed citations
7.
Sajjad, Hassan, et al.. (2013). QCRI-MES Submission at WMT13: Using Transliteration Mining to Improve Statistical Machine Translation. Workshop on Statistical Machine Translation. 219–224. 8 indexed citations
8.
Schmid, Helmut, et al.. (2013). Efficient Higher-Order CRFs for Morphological Tagging. 322–332. 108 indexed citations
9.
Durrani, Nadir, Alexander Fraser, Helmut Schmid, Hassan Sajjad, & Richárd Farkas. (2013). Munich-Edinburgh-Stuttgart Submissions of OSM Systems at WMT13. Workshop on Statistical Machine Translation. 122–127. 7 indexed citations
10.
Farkas, Richárd & Helmut Schmid. (2012). Forest Reranking through Subtree Ranking. Empirical Methods in Natural Language Processing. 1038–1047. 4 indexed citations
11.
Schuetze, Hinrich, et al.. (2012). A Comparative Investigation of Morphological Language Modeling for the Languages of the European Union. North American Chapter of the Association for Computational Linguistics. 386–395. 8 indexed citations
12.
Seeker, Wolfgang, Richárd Farkas, Bernd Bohnet, Helmut Schmid, & Jonas Kuhn. (2012). Data-driven Dependency Parsing With Empty Heads. International Conference on Computational Linguistics. 1081–1090. 8 indexed citations
13.
Farkas, Richárd, Veronika Vincze, & Helmut Schmid. (2012). Dependency Parsing of Hungarian: Baseline Results and Challenges. Conference of the European Chapter of the Association for Computational Linguistics. 55–65. 12 indexed citations
14.
Sajjad, Hassan, Alexander Fraser, & Helmut Schmid. (2012). A Statistical Model for Unsupervised and Semi-supervised Transliteration Mining. Meeting of the Association for Computational Linguistics. 1. 469–477. 26 indexed citations
15.
Sajjad, Hassan, Nadir Durrani, Helmut Schmid, & Alexander Fraser. (2011). Comparing Two Techniques for Learning Transliteration Models Using a Parallel Corpus. International Joint Conference on Natural Language Processing. 129–137. 6 indexed citations
16.
Sajjad, Hassan, Alexander Fraser, & Helmut Schmid. (2011). An Algorithm for Unsupervised Transliteration Mining with an Application to Word Alignment. Meeting of the Association for Computational Linguistics. 430–439. 14 indexed citations
17.
Walde, Sabine Schulte im, et al.. (2008). Combining EM Training and the MDL Principle for an Automatic Verb Classification Incorporating Selectional Preferences. Meeting of the Association for Computational Linguistics. 496–504. 22 indexed citations
18.
Demberg, Vera, et al.. (2007). Phonological Constraints and Morphological Preprocessing for Grapheme-to-Phoneme Conversion. Meeting of the Association for Computational Linguistics. 96–103. 26 indexed citations
19.
Schmid, Helmut. (2005). A Programming Language For Finite State Transducers. 1 indexed citations
20.
Schmid, Helmut, et al.. (2004). SMOR: A German Computational Morphology Covering Derivation, Composition and Inflection. Language Resources and Evaluation. 92 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.

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