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 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.
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.
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.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Omer Levy;Yoav Goldberg;Ido Dagan.
Transactions of the Association for Computational Linguistics (2015)
The PASCAL Recognising Textual Entailment Challenge
Ido Dagan;Oren Glickman;Bernardo Magnini.
Lecture Notes in Computer Science (2006)
Knowledge discovery in Textual Databases (KDT)
Ronen Feldman;Ido Dagan.
knowledge discovery and data mining (1995)
The Third PASCAL Recognizing Textual Entailment Challenge
Danilo Giampiccolo;Bernardo Magnini;Ido Dagan;Bill Dolan.
meeting of the association for computational linguistics (2007)
Committee-based sampling for training probabilistic classifiers
Ido Dagan;Sean P. Engelson.
international conference on machine learning (1995)
Similarity-Based Models of Word Cooccurrence Probabilities
Ido Dagan;Lillian Lee;Fernando C. N. Pereira.
Machine Learning (1999)
The Seventh PASCAL Recognizing Textual Entailment Challenge.
Luisa Bentivogli;Peter Clark;Ido Dagan;Danilo Giampiccolo.
Theory and Applications of Categories (2008)
Word sense disambiguation using a second language monolingual corpus
Ido Dagan;Alon Itai.
Computational Linguistics (1994)
Termight: Identifying and Translating Technical Terminology
Ido Dagan;Ken Church.
conference on applied natural language processing (1994)
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
Oren Melamud;Jacob Goldberger;Ido Dagan.
conference on computational natural language learning (2016)
Profile was last updated on December 6th, 2021.
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