Jan Hajič

8.2k citations
110 papers · 3.8k indexed · 3 hit papers · h-index 29

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Text Readability and Simplification
    • Speech and dialogue systems
    • Semantic Web and Ontologies
    • Speech Recognition and Synthesis
    • Advanced Text Analysis Techniques

Papers in

    • Natural Language Processing Techniques 93
    • Topic Modeling 55
    • Semantic Web and Ontologies 18
    • Speech and dialogue systems 10
    • Text Readability and Simplification 10
    • Speech Recognition and Synthesis 8
    • Lexicography and Language Studies 10

Jan Hajič

95 papers receiving 3.3k citations

Hit Papers

Universal Dependencies v1: A Multilingual Treebank Collection 2016 · 575 citations
5752005202620122019100200300400500

Peers

Jan Hajič
Comparison fields: 5 of 102
  • Artificial Intelligence 3.6k
  • Language and Linguistics 239
  • Computer Vision and Pattern Recognition 273
  • Information Systems 247
  • Linguistics and Language 42
Replace Marie-Catherine de Marneffe with:
Marie-Catherine de Marneffe United States
Daniel Gildea United States
Judith L. Klavans United States
Slav Petrov United States
Steven Abney United States
Gregory Grefenstette France
Piek Vossen Netherlands
Thorsten Brants Germany
Jörg Tiedemann Sweden
Anders Søgaard Denmark
Jan Hajič relative to Marie-Catherine de Marneffe United States Marie-Catherine de Marneffe's profile →
Citations per field
00.5×1.5×
Marie-Catherine de Marneffe · 1×
Citations per year

Countries citing papers authored by Jan Hajič

Since Specialization
Citations

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

Fields of papers citing papers by Jan Hajič

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 2019175
2 20190
3
Creating a Verb Synonym Lexicon Based on a Parallel Corpus
20181
4
CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies
201896
5
Diacritics Restoration Using Neural Networks.
201816
6
Universal Dependencies v1: A Multilingual Treebank Collection
Hit paper breakdown →
2016575
7
QTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages
20167
8
UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing
2016201
9 201469
10
Multilingual Test Sets for Machine Translation of Search Queries for Cross-Lingual Information Retrieval in the Medical Domain
20142
11
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers
Hit paper breakdown →
2014303
12
An Analysis of Annotation of Verb-Noun Idiomatic Combinations in a Parallel Dependency Corpus
20134
13
HamleDT: To Parse or Not to Parse?
201232
14
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task
20094
15
Validating the Quality of Full Morphological Annotation.
20081
16
Issues in annotation of the Czech spontaneous speech corpus in the MALACH project
20047
17
Annotation Lexicons: Using the Valency Lexicon for Tectogrammatical Annotation.
20032
18 19982
19
Czech language processing, POS tagging.
19989
20 198710

About Jan Hajič

Jan Hajič is a scholar working on Artificial Intelligence, Language and Linguistics, General Social Sciences, Linguistics and Language and Information Systems, having authored 110 papers that have together received 3.8k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (93 papers), Topic Modeling (55 papers), Semantic Web and Ontologies (18 papers), Lexicography and Language Studies (10 papers), Speech and dialogue systems (10 papers), Text Readability and Simplification (10 papers), Speech Recognition and Synthesis (8 papers) and Biomedical Text Mining and Ontologies (7 papers). The work is most often cited by research in Artificial Intelligence (3.6k citations), Language and Linguistics (239 citations), Computer Vision and Pattern Recognition (273 citations), Information Systems (247 citations) and Linguistics and Language (42 citations). Jan Hajič has collaborated with scholars based in Czechia, United States and Sweden. Frequent co-authors include Ryan McDonald, Milan Straka, Jun’ichi Tsujii, Jana Straková, Kiril Ribarov, Fernando Pereira, Daniel Zeman, Filip Ginter, Joakim Nivre and Slav Petrov. Their work appears in journals such as Language Resources and Evaluation, Artificial Intelligence in Medicine, Transactions of the Association for Computational Linguistics, International Journal of Lexicography and Meta Journal des traducteurs.

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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