Daniel Hardt

1.6k citations
64 papers · 1.1k · h-index 17

Impact in

Papers in

    • Natural Language Processing Techniques 30
    • Logic, Reasoning, and Knowledge 8
    • Topic Modeling 7
    • Sentiment Analysis and Opinion Mining 7
    • Multi-Agent Systems and Negotiation 4
    • Syntax, Semantics, Linguistic Variation 22

Daniel Hardt

59 papers receiving 931 citations

Peers

Daniel Hardt
Comparison fields: 5 of 89
  • Language and Linguistics 620
  • Artificial Intelligence 630
  • Linguistics and Language 89
  • Experimental and Cognitive Psychology 182
  • Philosophy 136
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Hardt

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Hardt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Hardt, 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 Daniel Hardt Line = papers co-authored together Daniel Hardt links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Verb phrase ellipsis: form, meaning, and processing
1993149
2 1999105
3 200183
4 200478
5
The process of post-editing: a pilot study
201163
6 199252
7 199249
8 199248
9 201335
10 199935
11
An empirical approach to VP ellipsis
199732
12
Incremental Re-training for Post-editing SMT
201031
13 202327
14 202125
15 201422
16 199718
17
Universal Joy A Data Set and Results for Classifying Emotions Across Languages
202117
18 201616
19 200215
20 201414

About Daniel Hardt

Daniel Hardt is a scholar working on Artificial Intelligence, Language and Linguistics, Sociology and Political Science, Philosophy and Cultural Studies, having authored 64 papers that have together received 1.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (30 papers), Syntax, Semantics, Linguistic Variation (22 papers), Logic, Reasoning, and Knowledge (8 papers), Topic Modeling (7 papers), Sentiment Analysis and Opinion Mining (7 papers), Linguistics and Discourse Analysis (6 papers), Language and cultural evolution (5 papers) and Multi-Agent Systems and Negotiation (4 papers). The work is most often cited by research in Language and Linguistics (620 citations), Artificial Intelligence (630 citations), Linguistics and Language (89 citations), Experimental and Cognitive Psychology (182 citations) and Philosophy (136 citations). Daniel Hardt has collaborated with scholars based in Denmark, United States and Germany. Frequent co-authors include Nicholas Asher, Joan Busquets, Matthew Stone, Ravi Vatrapu, Michaël Carl, Barbara Dragsted, Arnt Lykke Jakobsen, Julie Hunter, Line Mikkelsen and Dirk Hovy. Their work appears in journals such as Linguistics and Philosophy, Journal of Semantics, Journal of the Association for Information Systems, Big Data and Pflügers Archiv - European Journal of Physiology.

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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