Research Interests

My academic career is interleaved with industrial work and spans from Speech and Natural Language Processing (NLP) to general and applied data science.

I am interested in real-world problems that involve a novel or non-trivial application of deep-learning over large amounts of data. Data types of interest could be structured or unstructured (e.g. text, images or time series of Brain MEG signals). I am particularly interested in problems that involve challenges that can be generalized as interesting research topics in the core data science disciplines.

Research Projects:

Deep learning models to assess quality traits in tomato fruits based on hyperspectral imaging. (Grant from ministry of Agriculture, joint work with colleagues from the Hebrew U. and Volcani Institute)

Teaching

272000 - Introduction to Programming with python. Brain Science. Spring 2022.

Selected Publications

Google Scholar: https://scholar.google.com/citations?user=WWPHtjIAAAAJ

DBLP: https://dblp.org/pid/97/3859.html

Speech and Machine Translation

Lori Levin, Oren Glickman, Yan Qu, Donna Gates, Alon Lavie, Carolyn P. Rose, Carol Van Ess-Dykema and Alex Waibel. 1995. Using Context in Machine Translation of Spoken Language, Theoretical and Methodological Issues in Machine Translation (TMI-95).
Alon Lavie, Alex Waibel, Lori Levin, Donna Gates, Marsal Gavalda, Torsten Zeppenfeld, Puming Zhan and Oren Glickman. 1996. Translation of Conversational Speech with Janus-II, ICSLP-96
Pedro J. Moreno, Christopher F. Joerg, Jean-Manuel Van Thong, Oren Glickman: A recursive algorithm for the forced alignment of very long audio segments. ICSLP 1998
William Goldenthal, Keith Waters, Jean-Manuel Van Thong, Oren Glickman: Driving synthetic mouth gestures: phonetic recognition for faceme! EUROSPEECH 1997

Textual Entailment

Oren Glickman, Eyal Shnarch and Ido Dagan. 2006. Lexical Reference: a Semantic Matching Subtask, Empirical Methods in Natural Language Processing (EMNLP).
Ido Dagan, Oren Glickman, Alfio Gliozzo, Efrat Marmorshtein and Carlo Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution, COLING-ACL.
Oren Glickman, Ido Dagan, Mikaela Keller, Samy Bengio and Walter Daelemans. 2006. Investigating Lexical Substitution Scoring for Subtitle Generation Tenth Conference on Computational Natural Language Learning (CONLL-X).
Oren Glickman, Ido Dagan and Moshe Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05). (earlier version appeared at IJCAI-05)
Ido Dagan and Oren Glickman, 2004. Probabilistic textual entailment: Generic applied modeling of language variability. Learning Methods for Text Understanding and Mining 2004, 26-29.
Oren Glickman. 2009. Applied textual entailment: A generic framework to capture shallow semantic inference. VDM Verlag.
Ido Dagan, Oren Glickman and Bernardo Magnini. 2006. The PASCAL Recognising Textual Entailment Challenge, Lecture Notes in Computer Science, Volume 3944, Jan 2006.
Glickman, Oren and Ido Dagan. 2004. Acquiring lexical paraphrases from a single corpus, In Recent Advances in Natural Language Processing III, Nicolov, Nicolas, Kalina Bontcheva, Galia Angelova and Ruslan Mitkov (eds.). (note: this is a book chapter version of the RANLP-03 paper)
Oren Glickman and Ido Dagan. 2005. A Probabilistic Setting and Lexical Cooccurrence Model for Textual Entailment, ACL-05 Workshop on Empirical Modeling of Semantic Equivalence and Entailment.
Roy Bar-Haim, Idan Szpektor and Oren Glickman. 2005. Definition and Analysis of Intermediate Entailment Levels, ACL-05 Workshop on Empirical Modeling of Semantic Equivalence and Entailment.
Oren Glickman, Ido Dagan, Moshe Koppel. 2005. Web based probabilistic textual entailment. Proceedings of the 1st Pascal Challenge Workshop.
Oren Glickman and Rosie Jones. 1999. Examining Machine Learning for Adaptable End-to-End Information Extraction Systems, AAAI 1999 Workshop on Machine Learning for Information Extraction (MLIE-99)

Applied Data Science

Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Science of The Total Environment - 2020.
Examining the boundaries between ICD-11 PTSD/CPTSD and depression and anxiety symptoms: A network analysis perspective. Journal of affective disorders - 2020.
Yaron Michael, Itamar Lensky, Steve Brenner and David Helman. 2019. Using decomposed MODIS NDVI time series to improve wildfire risk mapping in Mediterranean forests. 7th International Conference on Fire Effects on Soil Properties.

Contact Information

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