Sabine Buchholz

29 papers receiving 1.0k citations

Hit Papers

CoNLL-X shared task on multilingual dependency parsing20062026201220192006200400600

Peers

Sabine Buchholz
Comparison fields: 5 of 66
  • Artificial Intelligence 1.2k
  • Signal Processing 222
  • Molecular Biology 86
  • Information Systems 79
  • Experimental and Cognitive Psychology 73
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Citations per field
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Citations per year

Countries citing papers authored by Sabine Buchholz

Since Specialization
Citations

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

Fields of papers citing papers by Sabine Buchholz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sabine Buchholz

This figure shows the co-authorship network connecting the top 25 collaborators of Sabine Buchholz. A scholar is included among the top collaborators of Sabine Buchholz 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 Sabine Buchholz. Sabine Buchholz 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 45
2 69
3 28
4 3
5 5
6
Annotating the Enron Email Corpus with Number Senses.
2
7
HMM-based polyglot speech synthesis by speaker and language adaptive training.
4
8 1
9 4
10
How (not) to select your voice corpus: random selection vs. phonologically balanced.
7
11
CoNLL-X shared task on multilingual dependency parsingbreakdown →
610
12
Using Grammatical Relations, Answer Frequencies and the World Wide Web for TREC Question Answering.
28
13
SHAPAQA : Shallow Parsing for Question Answering on the World Wide Web
4
14 17
15
Using Grammatical Relations
2
16
Integrating seed names and ngrams for a named entity list and classifier
11
17 54
18
Unsupervised Learning of Subcategorisation Information and Its Application in a Parsing Subtask
1
19
Literaturwissenschaftliche Theorien, Modelle und Methoden : eine Einführung
1
20
Distinguishing complements from adjuncts using memory-based learning
15

About Sabine Buchholz

Sabine Buchholz is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 30 papers that have together received 1.3k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (15 papers), Speech Recognition and Synthesis (12 papers) and Topic Modeling (11 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Signal Processing (222 citations) and Experimental and Cognitive Psychology (73 citations). Sabine Buchholz has collaborated with scholars based in United Kingdom, Netherlands and Japan. Frequent co-authors include Erwin Marsi, Norbert Braunschweiler, Walter Daelemans, Javier Latorre, Jorn Veenstra, Mark Gales, Antal van den Bosch, Florian Eyben, Sacha Krstulović and Heiga Zen. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, Language Resources and Evaluation and Natural Language Engineering.

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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