Daniela Gerz

624 total citations
8 papers, 195 citations indexed

About

Daniela Gerz is a scholar working on Artificial Intelligence, Infectious Diseases and Organic Chemistry. According to data from OpenAlex, Daniela Gerz has authored 8 papers receiving a total of 195 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 0 papers in Infectious Diseases and 0 papers in Organic Chemistry. Recurrent topics in Daniela Gerz's work include Natural Language Processing Techniques (8 papers), Topic Modeling (8 papers) and Speech and dialogue systems (5 papers). Daniela Gerz is often cited by papers focused on Natural Language Processing Techniques (8 papers), Topic Modeling (8 papers) and Speech and dialogue systems (5 papers). Daniela Gerz collaborates with scholars based in United Kingdom, Israel and South Korea. Daniela Gerz's co-authors include Ivan Vulić, Anna Korhonen, Edoardo Maria Ponti, Roi Reichart, Matthew Henderson, Pei-Hao Su, Nikola Mrkšić, Iñigo Casanueva, Tsung-Hsien Wen and Paweł Budzianowski and has published in prestigious journals such as Computational Linguistics, Transactions of the Association for Computational Linguistics and Monash University Research Portal (Monash University).

In The Last Decade

Daniela Gerz

8 papers receiving 176 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniela Gerz United Kingdom	7	183	32	11	6	5	8	195
Sina Zarrieß Germany	9	169 0.9×	73 2.3×	7 0.6×	4 0.7×	5 1.0×	56	198
Ákos Kádár Netherlands	7	152 0.8×	93 2.9×	5 0.5×	6 1.0×	5 1.0×	17	173
Liunian Harold Li United States	8	181 1.0×	112 3.5×	8 0.7×	3 0.5×	6 1.2×	11	229
Michele Bevilacqua Italy	8	301 1.6×	54 1.7×	12 1.1×	6 1.0×	18 3.6×	12	314
Miryam de Lhoneux Sweden	7	147 0.8×	23 0.7×	5 0.5×	3 0.5×	5 1.0×	17	161
Mohammad Sadegh Rasooli United States	10	249 1.4×	31 1.0×	15 1.4×	2 0.3×	2 0.4×	22	261
Lajanugen Logeswaran United States	8	235 1.3×	68 2.1×	25 2.3×	2 0.3×	6 1.2×	17	264
Djamé Seddah France	11	313 1.7×	33 1.0×	11 1.0×	8 1.3×	16 3.2×	40	327
Vikas Bhardwaj United States	8	177 1.0×	46 1.4×	7 0.6×	2 0.3×	1 0.2×	10	190
Yova Kementchedjhieva Denmark	9	162 0.9×	59 1.8×	6 0.5×	4 0.7×	13 2.6×	21	225

Countries citing papers authored by Daniela Gerz

Since Specialization

Citations

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

Fields of papers citing papers by Daniela Gerz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniela Gerz

This figure shows the co-authorship network connecting the top 25 collaborators of Daniela Gerz. A scholar is included among the top collaborators of Daniela Gerz 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 Daniela Gerz. Daniela Gerz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Vulić, Ivan, Pei-Hao Su, Daniela Gerz, et al.. (2021). ConvFiT: Conversational Fine-Tuning of Pretrained Language Models. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 1151–1168. 19 indexed citations

2.

Gerz, Daniela, et al.. (2020). Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations. 107–121. 27 indexed citations

3.

Henderson, Matthew, Ivan Vulić, Iñigo Casanueva, et al.. (2019). PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking. 181–186. 8 indexed citations

4.

Henderson, Matthew, Paweł Budzianowski, Iñigo Casanueva, et al.. (2019). A Repository of Conversational Datasets. 1–10. 43 indexed citations

5.

Shareghi, Ehsan, Daniela Gerz, Ivan Vulić, & Anna Korhonen. (2019). Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines. Monash University Research Portal (Monash University). 4113–4118. 6 indexed citations

6.

Gerz, Daniela, Ivan Vulić, Edoardo Maria Ponti, et al.. (2018). Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction. Transactions of the Association for Computational Linguistics. 6. 451–465. 27 indexed citations

7.

Gerz, Daniela, Ivan Vulić, Edoardo Maria Ponti, Roi Reichart, & Anna Korhonen. (2018). On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling. Edinburgh Research Explorer. 28 indexed citations

8.

Vulić, Ivan, Daniela Gerz, Douwe Kiela, Felix Hill, & Anna Korhonen. (2017). HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment. Computational Linguistics. 43(4). 781–835. 37 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.

Explore authors with similar magnitude of impact