2018 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to Natural Language Processing.
2017 - Fellow of the MacArthur Foundation
Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Task are her primary areas of study. The concepts of her Artificial intelligence study are interwoven with issues in Machine learning, Baseline and Set. She has included themes like Context and Word in her Natural language processing study.
Her research integrates issues of Document retrieval, Data mining and Cluster analysis in her study of Automatic summarization. Her work carried out in the field of Information retrieval brings together such families of science as Transcription and Segmentation. Her research in Task intersects with topics in Speech recognition, Arabic, Semitic languages and Hebrew.
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Machine learning, Task and Theoretical computer science. Her Artificial intelligence research focuses on Set and how it connects with Data mining. The study incorporates disciplines such as Structure, Speech recognition and Information retrieval in addition to Natural language processing.
Her Machine learning study combines topics in areas such as Representation and Inference. The various areas that Regina Barzilay examines in her Task study include Question answering, Contrast, Baseline and Reinforcement learning. Her Theoretical computer science study integrates concerns from other disciplines, such as Graph and Molecular graph.
Regina Barzilay mostly deals with Artificial intelligence, Machine learning, Natural language processing, Theoretical computer science and Generative grammar. Regina Barzilay regularly ties together related areas like Context in her Artificial intelligence studies. Regina Barzilay focuses mostly in the field of Context, narrowing it down to topics relating to Information needs and, in certain cases, Task.
Her Machine learning study incorporates themes from Variation and Representation. Her Natural language processing research is multidisciplinary, relying on both Similarity, Word and Decipherment. She has researched Theoretical computer science in several fields, including Correctness, Graph and Drug discovery.
Regina Barzilay mainly investigates Artificial intelligence, Theoretical computer science, Natural language processing, Graph and Language model. Her Artificial intelligence research integrates issues from Machine learning and Workflow. Her study in Theoretical computer science is interdisciplinary in nature, drawing from both Generative grammar, Transformer and Drug discovery.
Her Natural language processing research incorporates themes from Word, Task and Shot. Her studies deal with areas such as Intervention, Randomized controlled trial, Clinical trial and Documentation as well as Task. The Language model study combines topics in areas such as Stylometry and Fake news.
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Using lexical chains for text summarization
Regina Barzilay;Michael Elhadad.
Intelligent Scalable Text Summarization (1997)
Modeling local coherence: An entity-based approach
Regina Barzilay;Regina Barzilay;Mirella Lapata.
Computational Linguistics (2008)
A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes;Kevin Yang;Kyle Swanson;Wengong Jin.
Extracting Paraphrases from a Parallel Corpus
Regina Barzilay;Kathleen R. McKeown.
meeting of the association for computational linguistics (2001)
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
Regina Barzilay;Lillian Lee.
north american chapter of the association for computational linguistics (2003)
Rationalizing Neural Predictions
Tao Lei;Regina Barzilay;Tommi S. Jaakkola.
empirical methods in natural language processing (2016)
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin;Regina Barzilay;Tommi S. Jaakkola.
international conference on machine learning (2018)
Information Fusion in the Context of Multi-Document Summarization
Regina Barzilay;Kathleen R. McKeown;Michael Elhadad.
meeting of the association for computational linguistics (1999)
Style Transfer from Non-Parallel Text by Cross-Alignment
Tianxiao Shen;Tao Lei;Regina Barzilay;Tommi S. Jaakkola.
neural information processing systems (2017)
Analyzing Learned Molecular Representations for Property Prediction.
Kevin Yang;Kyle Swanson;Wengong Jin;Connor W. Coley.
Journal of Chemical Information and Modeling (2019)
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