H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 65 Citations 15,517 371 World Ranking 1153 National Ranking 39

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Natural language processing
  • Programming language

Artificial intelligence, Natural language processing, Information retrieval, Semantic similarity and Argument are her primary areas of study. Her Artificial intelligence research is multidisciplinary, relying on both Context, Machine learning and German. Her study in Natural language processing is interdisciplinary in nature, drawing from both Annotation, Word, Set and Ranking.

Iryna Gurevych combines subjects such as Domain, Similarity, Quality and World Wide Web with her study of Information retrieval. Her Semantic similarity study combines topics from a wide range of disciplines, such as Semantics, Similarity, Explicit semantic analysis and Linguistic distance. Her work deals with themes such as Field and Argumentation theory, which intersect with Argument.

Her most cited work include:

  • Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (381 citations)
  • Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields (366 citations)
  • Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary (294 citations)

What are the main themes of her work throughout her whole career to date?

Her primary areas of study are Artificial intelligence, Natural language processing, Information retrieval, Machine learning and Annotation. Her Argument research extends to the thematically linked field of Artificial intelligence. She works mostly in the field of Argument, limiting it down to concerns involving Argumentation theory and, occasionally, Argumentative.

The concepts of her Natural language processing study are interwoven with issues in Context, Set and German. Many of her studies involve connections with topics such as Domain and Information retrieval. Her Machine learning research is multidisciplinary, incorporating elements of Question answering and Bayesian probability.

She most often published in these fields:

  • Artificial intelligence (58.24%)
  • Natural language processing (44.01%)
  • Information retrieval (18.73%)

What were the highlights of her more recent work (between 2019-2021)?

  • Artificial intelligence (58.24%)
  • Machine learning (12.55%)
  • Natural language processing (44.01%)

In recent papers she was focusing on the following fields of study:

Iryna Gurevych focuses on Artificial intelligence, Machine learning, Natural language processing, Sentence and Transformer. Her research on Artificial intelligence frequently connects to adjacent areas such as Preference learning. Her studies deal with areas such as Debiasing, Pipeline, Robustness and Component as well as Machine learning.

Her research investigates the connection between Natural language processing and topics such as Coreference that intersect with problems in Event. She interconnects Crowdsourcing, Interpretability, Parsing and Code in the investigation of issues within Sentence. Her research in Transformer intersects with topics in Scalability, Inference and Scripting language.

Between 2019 and 2021, her most popular works were:

  • MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer (37 citations)
  • AdapterHub: A Framework for Adapting Transformers (23 citations)
  • Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation (23 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Machine learning

Her main research concerns Artificial intelligence, Natural language processing, Machine learning, Language model and Sentence. Her research integrates issues of Quality, Adaptation and Coherence in her study of Artificial intelligence. Her work in Natural language processing addresses subjects such as Embedding, which are connected to disciplines such as Code.

The various areas that Iryna Gurevych examines in her Machine learning study include Debiasing and Training set. Her Language model study also includes

  • Forgetting which connect with Common sense and Inference,
  • Vocabulary which intersects with area such as Lexical analysis, Set, Factor and Range. The study incorporates disciplines such as Dependency, Interpretability, Parsing and Classifier in addition to Sentence.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Nils Reimers;Iryna Gurevych.
empirical methods in natural language processing (2019)

639 Citations

Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields

Niklas Jakob;Iryna Gurevych.
empirical methods in natural language processing (2010)

595 Citations

Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary

Torsten Zesch;Christof Müller;Iryna Gurevych.
language resources and evaluation (2008)

459 Citations

A Monolingual Tree-based Translation Model for Sentence Simplification

Zhemin Zhu;Delphine Bernhard;Iryna Gurevych.
international conference on computational linguistics (2010)

358 Citations

What helps where – and why? Semantic relatedness for knowledge transfer

Marcus Rohrbach;Michael Stark;Gyorgy Szarvas;Iryna Gurevych.
computer vision and pattern recognition (2010)

339 Citations

Large-scale multi-label text classification — revisiting neural networks

Jinseok Nam;Jungi Kim;Eneldo Loza Mencía;Iryna Gurevych.
european conference on machine learning (2014)

325 Citations

Identifying Argumentative Discourse Structures in Persuasive Essays

Christian Stab;Iryna Gurevych.
empirical methods in natural language processing (2014)

311 Citations

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

Nils Reimers;Iryna Gurevych.
empirical methods in natural language processing (2017)

267 Citations

UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

Daniel Bär;Chris Biemann;Iryna Gurevych;Torsten Zesch.
joint conference on lexical and computational semantics (2012)

243 Citations

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

Nils Reimers;Iryna Gurevych.
arXiv: Computation and Language (2017)

235 Citations

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