World's Best Scientists 2026 revealed!

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Computer Science

D-Index
39
Citations
7460
World Ranking
9669
National Ranking
158

Overview

Roi Reichart is affiliated with the Technion - Israel Institute of Technology in Israel. Their research primarily focuses on the field of Computer Science, with a substantial number of publications in Artificial Intelligence, Computer Vision and Pattern Recognition, Social Psychology, Cognitive Neuroscience, and Information Systems.

The main topics that Roi Reichart has contributed to include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Sentiment Analysis and Opinion Mining
  • Domain Adaptation and Few-Shot Learning
  • Neurobiology of Language and Bilingualism
  • Explainable Artificial Intelligence (XAI)

Among the recent papers, the following are highlighted for their publication year and venues:

  • Modeling the Detection of Textual Cyberbullying, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • Shared computational principles for language processing in humans and deep language models, 2022, Nature Neuroscience
  • Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond, 2022, Transactions of the Association for Computational Linguistics
  • Deep neural networks detect suicide risk from textual facebook posts, 2020, Scientific Reports
  • Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity, 2020, Computational Linguistics

Frequent co-authors in Roi Reichart's work include Amir Feder, Anna Korhonen, Eyal Ben-David, Rotem Dror, and Lotem Peled-Cohen.

Roi Reichart's publications have appeared often in venues such as:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Nature Communications
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Artificial Intelligence Research

Their contributions also extend to book publications, with one known book titled Statistical Significance Testing for Natural Language Processing, published in 2020 by Morgan & Claypool Publishers.

Best Publications

  • Simlex-999: Evaluating semantic models with genuine similarity estimation

    Felix Hill;Roi Reichart;Anna Korhonen

  • Modeling the Detection of Textual Cyberbullying

    Karthik Dinakar;Roi Reichart;Henry Lieberman

  • Shared computational principles for language processing in humans and deep language models

    Unknown

  • The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

    Rotem Dror;Gili Baumer;Segev Shlomov;Roi Reichart

  • Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

    Unknown

  • SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

    Daniela Gerz;Ivan Vulic;Felix Hill;Roi Reichart

  • Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

    Nikola Mrksic;Nikola Mrksic;Ivan Vulic;Diarmuid Ó Séaghdha;Ira Leviant

  • Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

    Edoardo Maria Ponti;Helen O’Horan;Yevgeni Berzak;Ivan Vulić

  • Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

    Roy Schwartz;Roi Reichart;Ari Rappoport

  • Pivot Based Language Modeling for Improved Neural Domain Adaptation

    Yftah Ziser;Roi Reichart

  • CausaLM: Causal Model Explanation Through Counterfactual Language Models

    Amir Feder;Nadav Oved;Uri Shalit;Roi Reichart

  • Multi-Task Active Learning for Linguistic Annotations

    Roi Reichart;Katrin Tomanek;Udo Hahn;Ari Rappoport

  • Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?

    Ivan Vulić;Goran Glavaš;Roi Reichart;Anna Korhonen

  • Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets

    Roi Reichart;Ari Rappoport

  • PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

    Unknown

  • Neural Structural Correspondence Learning for Domain Adaptation

    Yftah Ziser;Roi Reichart

  • Deep neural networks detect suicide risk from textual facebook posts.

    Yaakov Ophir;Yaakov Ophir;Refael Tikochinski;Refael Tikochinski;Christa S C Asterhan;Itay Sisso

  • Separated by an Un-common Language: Towards Judgment Language Informed Vector Space Modeling

    Ira Leviant;Roi Reichart

  • Deep Dominance - How to Properly Compare Deep Neural Models

    Rotem Dror;Segev Shlomov;Roi Reichart

  • An Unsupervised Model for Instance Level Subcategorization Acquisition

    Simon Baker;Roi Reichart;Anna Korhonen

  • Confidence Driven Unsupervised Semantic Parsing

    Dan Goldwasser;Roi Reichart;James Clarke;Dan Roth

  • Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity

    Ivan Vulic;Simon Baker;Edoardo Maria Ponti;Ulla Petti

  • Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

    Nikola Mrkšić;Ivan Vulić;Diarmuid Ó Séaghdha;Ira Leviant

Frequent Co-Authors

Anna Korhonen
Anna Korhonen University of Cambridge
Ari Rappoport
Ari Rappoport Hebrew University of Jerusalem
Ivan Vulić
Ivan Vulić University of Cambridge
Felix Hill
Felix Hill Google (United States)
Moshe Tennenholtz
Moshe Tennenholtz Technion – Israel Institute of Technology
Ryan Cotterell
Ryan Cotterell ETH Zurich
Hinrich Schütze
Hinrich Schütze Ludwig-Maximilians-Universität München
Steve Young
Steve Young University of Cambridge
Nikola Mrksic
Nikola Mrksic PolyAI Limited

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