H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 58 Citations 17,889 216 World Ranking 1834 National Ranking 35

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Natural language processing

Artificial intelligence, Natural language processing, Textual entailment, Logical consequence and Word are his primary areas of study. His Artificial intelligence research is multidisciplinary, relying on both Context and Machine learning. In general Natural language processing, his work in Machine translation is often linked to Cache language model linking many areas of study.

He works mostly in the field of Textual entailment, limiting it down to topics relating to Question answering and, in certain cases, Text graph, as a part of the same area of interest. His studies in Logical consequence integrate themes in fields like Similarity measure, Feature, Word sense and Semantic similarity. His Word research incorporates themes from Analogy, Contrast, Similarity and Hyperparameter.

His most cited work include:

  • The PASCAL Recognising Textual Entailment Challenge (1179 citations)
  • Improving Distributional Similarity with Lessons Learned from Word Embeddings (914 citations)
  • The Third PASCAL Recognizing Textual Entailment Challenge (766 citations)

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

His primary scientific interests are in Artificial intelligence, Natural language processing, Inference, Textual entailment and Logical consequence. His work deals with themes such as Machine learning and Information retrieval, which intersect with Artificial intelligence. His studies deal with areas such as Annotation, Scheme and Coreference as well as Natural language processing.

His Textual entailment study incorporates themes from Question answering, Information extraction, Pascal and Text graph, Automatic summarization. Ido Dagan is involved in the study of Logical consequence that focuses on Preferential entailment in particular. His biological study deals with issues like Similarity, which deal with fields such as Bigram.

He most often published in these fields:

  • Artificial intelligence (84.88%)
  • Natural language processing (67.44%)
  • Inference (18.22%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (84.88%)
  • Natural language processing (67.44%)
  • Coreference (6.20%)

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

The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Coreference, Crowdsourcing and Machine learning. His Artificial intelligence study frequently draws connections to adjacent fields such as Space. He studies Parsing, a branch of Natural language processing.

His Crowdsourcing study combines topics from a wide range of disciplines, such as Annotation, Sentence, Correctness and Textual entailment. His Machine learning research includes themes of Training set and Source code. His Resolution study combines topics in areas such as Context, Set and Information retrieval.

Between 2017 and 2021, his most popular works were:

  • Supervised Open Information Extraction (106 citations)
  • Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (74 citations)
  • Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference (62 citations)

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

  • Artificial intelligence
  • Programming language
  • Natural language processing

Ido Dagan focuses on Artificial intelligence, Natural language processing, Crowdsourcing, Automatic summarization and Inference. He has researched Artificial intelligence in several fields, including Machine learning and Correctness. His Machine learning research is multidisciplinary, incorporating perspectives in Training set and Source code.

His Natural language processing research integrates issues from Space, Meaning, Event, Coreference and Verbosity. His research investigates the link between Crowdsourcing and topics such as Annotation that cross with problems in Sentence, Set, Scheme and PropBank. His biological study spans a wide range of topics, including BLEU, Conflation and Text generation.

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.

Top Publications

Improving Distributional Similarity with Lessons Learned from Word Embeddings

Omer Levy;Yoav Goldberg;Ido Dagan.
Transactions of the Association for Computational Linguistics (2015)

1353 Citations

The PASCAL Recognising Textual Entailment Challenge

Ido Dagan;Oren Glickman;Bernardo Magnini.
Lecture Notes in Computer Science (2006)

1179 Citations

Knowledge discovery in Textual Databases (KDT)

Ronen Feldman;Ido Dagan.
knowledge discovery and data mining (1995)

920 Citations

The Third PASCAL Recognizing Textual Entailment Challenge

Danilo Giampiccolo;Bernardo Magnini;Ido Dagan;Bill Dolan.
meeting of the association for computational linguistics (2007)

766 Citations

Committee-based sampling for training probabilistic classifiers

Ido Dagan;Sean P. Engelson.
international conference on machine learning (1995)

572 Citations

Similarity-Based Models of Word Cooccurrence Probabilities

Ido Dagan;Lillian Lee;Fernando C. N. Pereira.
Machine Learning (1999)

565 Citations

The Seventh PASCAL Recognizing Textual Entailment Challenge.

Luisa Bentivogli;Peter Clark;Ido Dagan;Danilo Giampiccolo.
Theory and Applications of Categories (2008)

501 Citations

Word sense disambiguation using a second language monolingual corpus

Ido Dagan;Alon Itai.
Computational Linguistics (1994)

446 Citations

Termight: Identifying and Translating Technical Terminology

Ido Dagan;Ken Church.
conference on applied natural language processing (1994)

397 Citations

context2vec: Learning Generic Context Embedding with Bidirectional LSTM

Oren Melamud;Jacob Goldberger;Ido Dagan.
conference on computational natural language learning (2016)

377 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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