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Noam Slonim
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- affiliation: IBM Research
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2020 – today
- 2024
- [c76]Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen:
Efficient Benchmarking (of Language Models). NAACL-HLT 2024: 2519-2536 - [i50]Alon Halfon, Shai Gretz, Ofir Arviv, Artem Spector, Orith Toledo-Ronen, Yoav Katz, Liat Ein-Dor, Michal Shmueli-Scheuer, Noam Slonim:
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications. CoRR abs/2407.18990 (2024) - [i49]Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim:
Conversational Prompt Engineering. CoRR abs/2408.04560 (2024) - 2023
- [j5]Marcos V. Treviso, Ji-Ung Lee
, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro Henrique Martins, André F. T. Martins, Jessica Zosa Forde, Peter A. Milder, Edwin Simpson
Efficient Methods for Natural Language Processing: A Survey. Trans. Assoc. Comput. Linguistics 11: 826-860 (2023) - [c75]Yotam Perlitz, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer:
nBIIG: A Neural BI Insights Generation System for Table Reporting. AAAI 2023: 16470-16472 - [c74]Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Leshem Choshen:
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning. ACL (1) 2023: 788-806 - [c73]Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch:
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers. ACL (1) 2023: 10406-10420 - [c72]Shai Gretz, Assaf Toledo, Roni Friedman, Dan Lahav, Rose Weeks, Naor Bar-Zeev, João Sedoc, Pooja Sangha, Yoav Katz, Noam Slonim:
Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy. EACL (Findings) 2023: 1328-1340 - [c71]Lilach Eden, Yoav Kantor, Matan Orbach, Yoav Katz, Noam Slonim, Roy Bar-Haim:
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis. EMNLP (Industry Track) 2023: 483-491 - [c70]Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen:
Knowledge is a Region in Weight Space for Fine-tuned Language Models. EMNLP (Findings) 2023: 1350-1370 - [c69]Leshem Choshen, Elad Venezian, Shachar Don-Yehiya, Noam Slonim, Yoav Katz:
Where to start? Analyzing the potential value of intermediate models. EMNLP 2023: 1446-1470 - [c68]Shai Gretz, Alon Halfon, Ilya Shnayderman, Orith Toledo-Ronen, Artem Spector, Lena Dankin, Yannis Katsis, Ofir Arviv, Yoav Katz, Noam Slonim, Liat Ein-Dor:
Zero-shot Topical Text Classification with LLMs - an Experimental Study. EMNLP (Findings) 2023: 9647-9676 - [c67]Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor:
Active Learning for Natural Language Generation. EMNLP 2023: 9862-9877 - [i48]Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen:
Knowledge is a Region in Weight Space for Fine-tuned Language Models. CoRR abs/2302.04863 (2023) - [i47]Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch:
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers. CoRR abs/2305.01628 (2023) - [i46]Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor:
Active Learning for Natural Language Generation. CoRR abs/2305.15040 (2023) - [i45]Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen:
Efficient Benchmarking (of Language Models). CoRR abs/2308.11696 (2023) - 2022
- [j4]Assaf Toledo
Revisiting Sequential Information Bottleneck: New Implementation and Evaluation. Entropy 24(8): 1132 (2022) - [c66]Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim:
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis. AAAI 2022: 10608-10617 - [c65]Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor:
Quality Controlled Paraphrase Generation. ACL (1) 2022: 596-609 - [c64]Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Cluster & Tune: Boost Cold Start Performance in Text Classification. ACL (1) 2022: 7639-7653 - [c63]Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim:
Zero-Shot Text Classification with Self-Training. EMNLP 2022: 1107-1119 - [c62]Noam Slonim:
Project Debater - an autonomous debating system. ISAIM 2022 - [c61]Orith Toledo-Ronen, Matan Orbach, Yoav Katz, Noam Slonim:
Multi-Domain Targeted Sentiment Analysis. NAACL-HLT 2022: 2751-2762 - [i44]Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim:
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis. CoRR abs/2201.02026 (2022) - [i43]Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Cluster & Tune: Boost Cold Start Performance in Text Classification. CoRR abs/2203.10581 (2022) - [i42]Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor:
Quality Controlled Paraphrase Generation. CoRR abs/2203.10940 (2022) - [i41]Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim:
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization. CoRR abs/2203.15590 (2022) - [i40]Leshem Choshen, Elad Venezian, Noam Slonim, Yoav Katz:
Fusing finetuned models for better pretraining. CoRR abs/2204.03044 (2022) - [i39]Orith Toledo-Ronen, Matan Orbach, Yoav Katz, Noam Slonim:
Multi-Domain Targeted Sentiment Analysis. CoRR abs/2205.03804 (2022) - [i38]Yotam Perlitz, Liat Ein-Dor, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer:
Diversity Enhanced Table-to-Text Generation via Type Control. CoRR abs/2205.10938 (2022) - [i37]Shai Gretz, Assaf Toledo, Roni Friedman, Dan Lahav, Rose Weeks, Naor Bar-Zeev, João Sedoc, Pooja Sangha, Yoav Katz, Noam Slonim:
Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy. CoRR abs/2205.11966 (2022) - [i36]Roni Friedman, João Sedoc, Shai Gretz, Assaf Toledo, Rose Weeks, Naor Bar-Zeev, Yoav Katz, Noam Slonim:
VIRATrustData: A Trust-Annotated Corpus of Human-Chatbot Conversations About COVID-19 Vaccines. CoRR abs/2205.12240 (2022) - [i35]Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martín Santillán Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz:
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours. CoRR abs/2208.01483 (2022) - [i34]Marcos V. Treviso, Tianchu Ji, Ji-Ung Lee, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Pedro Henrique Martins, André F. T. Martins, Peter A. Milder, Colin Raffel, Edwin Simpson, Noam Slonim, Niranjan Balasubramanian, Leon Derczynski, Roy Schwartz:
Efficient Methods for Natural Language Processing: A Survey. CoRR abs/2209.00099 (2022) - [i33]Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim:
Zero-Shot Text Classification with Self-Training. CoRR abs/2210.17541 (2022) - [i32]Leshem Choshen, Elad Venezian, Shachar Don-Yehiya, Noam Slonim, Yoav Katz:
Where to start? Analyzing the potential value of intermediate models. CoRR abs/2211.00107 (2022) - [i31]Yotam Perlitz, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer:
nBIIG: A Neural BI Insights Generation System for Table Reporting. CoRR abs/2211.04417 (2022) - [i30]Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen:
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning. CoRR abs/2212.01378 (2022) - [i29]Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor:
SimpleStyle: An Adaptable Style Transfer Approach. CoRR abs/2212.10498 (2022) - 2021
- [j3]Noam Slonim
An autonomous debating system. Nat. 591(7850): 379-384 (2021) - [c60]Roy Bar-Haim, Lilach Eden, Yoav Kantor, Roni Friedman, Noam Slonim:
Every Bite Is an Experience: Key Point Analysis of Business Reviews. ACL/IJCNLP (1) 2021: 3376-3386 - [c59]Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim:
Overview of the 2021 Key Point Analysis Shared Task. ArgMining@EMNLP 2021: 154-164 - [c58]Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, Noam Slonim:
Project Debater APIs: Decomposing the AI Grand Challenge. EMNLP (Demos) 2021: 267-274 - [c57]Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim:
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews. EMNLP (1) 2021: 9154-9173 - [i28]Roy Bar-Haim, Lilach Eden, Yoav Kantor, Roni Friedman, Noam Slonim:
Every Bite Is an Experience: Key Point Analysis of Business Reviews. CoRR abs/2106.06758 (2021) - [i27]Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, Noam Slonim:
Project Debater APIs: Decomposing the AI Grand Challenge. CoRR abs/2110.01029 (2021) - [i26]Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim:
Overview of the 2021 Key Point Analysis Shared Task. CoRR abs/2110.10577 (2021) - 2020
- [c56]Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Corpus Wide Argument Mining - A Working Solution. AAAI 2020: 7683-7691 - [c55]Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim:
A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis. AAAI 2020: 7805-7813 - [c54]Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim:
From Arguments to Key Points: Towards Automatic Argument Summarization. ACL 2020: 4029-4039 - [c53]Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Out of the Echo Chamber: Detecting Countering Debate Speeches. ACL 2020: 7073-7086 - [c52]Roy Bar-Haim, Yoav Kantor, Lilach Eden, Roni Friedman, Dan Lahav, Noam Slonim:
Quantitative argument summarization and beyond: Cross-domain key point analysis. EMNLP (1) 2020: 39-49 - [c51]Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim:
Multilingual Argument Mining: Datasets and Analysis. EMNLP (Findings) 2020: 303-317 - [c50]Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik, Noam Slonim:
The workweek is the best time to start a family - A Study of GPT-2 Based Claim Generation. EMNLP (Findings) 2020: 528-544 - [c49]Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains. EMNLP (Findings) 2020: 2678-2697 - [c48]Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim:
Active Learning for BERT: An Empirical Study. EMNLP (1) 2020: 7949-7962 - [i25]Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Out of the Echo Chamber: Detecting Countering Debate Speeches. CoRR abs/2005.01157 (2020) - [i24]Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim:
From Arguments to Key Points: Towards Automatic Argument Summarization. CoRR abs/2005.01619 (2020) - [i23]Yonatan Bilu, Shai Gretz, Edo Cohen, Noam Slonim:
What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus. CoRR abs/2009.08240 (2020) - [i22]Roy Bar-Haim, Yoav Kantor, Lilach Eden, Roni Friedman, Dan Lahav, Noam Slonim:
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis. CoRR abs/2010.05369 (2020) - [i21]Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik, Noam Slonim:
The workweek is the best time to start a family - A Study of GPT-2 Based Claim Generation. CoRR abs/2010.06185 (2020) - [i20]Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim:
Multilingual Argument Mining: Datasets and Analysis. CoRR abs/2010.06432 (2020) - [i19]Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Noam Slonim, Ranit Aharonov:
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains. CoRR abs/2010.09459 (2020) - [i18]Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim:
YASO: A New Benchmark for Targeted Sentiment Analysis. CoRR abs/2012.14541 (2020)
2010 – 2019
- 2019
- [c47]Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network. ACL (1) 2019: 967-976 - [c46]Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim:
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion. ACL (1) 2019: 977-990 - [c45]Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim:
Argument Invention from First Principles. ACL (1) 2019: 1013-1026 - [c44]Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining. ArgMining@ACL 2019: 58-66 - [c43]Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, Noam Slonim:
Learning to combine Grammatical Error Corrections. BEA@ACL 2019: 139-148 - [c42]Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim:
A Dataset of General-Purpose Rebuttal. EMNLP/IJCNLP (1) 2019: 5590-5600 - [c41]Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Automatic Argument Quality Assessment - New Datasets and Methods. EMNLP/IJCNLP (1) 2019: 5624-5634 - [i17]Daniel Hershcovich, Assaf Toledo, Alon Halfon, Noam Slonim:
Syntactic Interchangeability in Word Embedding Models. CoRR abs/1904.00669 (2019) - [i16]Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, Noam Slonim:
Learning to combine Grammatical Error Corrections. CoRR abs/1906.03897 (2019) - [i15]Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network. CoRR abs/1907.08971 (2019) - [i14]Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Shachar Mirkin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining. CoRR abs/1907.11889 (2019) - [i13]Ilya Shnayderman, Liat Ein-Dor, Yosi Mass, Alon Halfon, Benjamin Sznajder, Artem Spector, Yoav Katz, Dafna Sheinwald, Ranit Aharonov, Noam Slonim:
Fast End-to-End Wikification. CoRR abs/1908.06785 (2019) - [i12]Benjamin Sznajder, Ariel Gera, Yonatan Bilu, Dafna Sheinwald, Ella Rabinovich, Ranit Aharonov, David Konopnicki, Noam Slonim:
Controversy in Context. CoRR abs/1908.07491 (2019) - [i11]Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim:
Argument Invention from First Principles. CoRR abs/1908.08336 (2019) - [i10]Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim:
A Dataset of General-Purpose Rebuttal. CoRR abs/1909.00393 (2019) - [i9]Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Automatic Argument Quality Assessment - New Datasets and Methods. CoRR abs/1909.01007 (2019) - [i8]Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Corpus Wide Argument Mining - a Working Solution. CoRR abs/1911.10763 (2019) - [i7]Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim:
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis. CoRR abs/1911.11408 (2019) - 2018
- [c40]Liat Ein-Dor, Yosi Mass, Alon Halfon, Elad Venezian, Ilya Shnayderman, Ranit Aharonov, Noam Slonim:
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks. ACL (2) 2018: 49-54 - [c39]Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining. ACL (2) 2018: 599-605 - [c38]Ran Levy, Ben Bogin, Shai Gretz, Ranit Aharonov, Noam Slonim:
Towards an argumentative content search engine using weak supervision. COLING 2018: 2066-2081 - [c37]Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim:
Learning Sentiment Composition from Sentiment Lexicons. COLING 2018: 2230-2241 - [c36]Noam Slonim:
Project Debater. COMMA 2018: 4 - [c35]Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Listening Comprehension over Argumentative Content. EMNLP 2018: 719-724 - [c34]Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim:
Learning Concept Abstractness Using Weak Supervision. EMNLP 2018: 4854-4859 - [c33]Liat Ein-Dor, Alon Halfon, Yoav Kantor, Ran Levy, Yosi Mass, Ruty Rinott, Eyal Shnarch, Noam Slonim:
Semantic Relatedness of Wikipedia Concepts - Benchmark Data and a Working Solution. LREC 2018 - [c32]Charles Jochim, Francesca Bonin, Roy Bar-Haim, Noam Slonim:
SLIDE - a Sentiment Lexicon of Common Idioms. LREC 2018 - [c31]Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim:
A Recorded Debating Dataset. LREC 2018 - [e2]Noam Slonim, Ranit Aharonov:
Proceedings of the 5th Workshop on Argument Mining, ArgMining@EMNLP 2018, Brussels, Belgium, November 1, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-69-8 [contents] - [i6]Yosi Mass, Lili Kotlerman, Shachar Mirkin, Elad Venezian, Gera Witzling, Noam Slonim:
What did you Mention? A Large Scale Mention Detection Benchmark for Spoken and Written Text. CoRR abs/1801.07507 (2018) - [i5]Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim:
Learning Concept Abstractness Using Weak Supervision. CoRR abs/1809.01285 (2018) - 2017
- [c30]Roy Bar-Haim, Lilach Edelstein, Charles Jochim, Noam Slonim:
Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization. ArgMining@EMNLP 2017: 32-38 - [c29]Ran Levy
Unsupervised corpus-wide claim detection. ArgMining@EMNLP 2017: 79-84 - [c28]Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, Noam Slonim:
Stance Classification of Context-Dependent Claims. EACL (1) 2017: 251-261 - [c27]Eyal Shnarch, Ran Levy
GRASP: Rich Patterns for Argumentation Mining. EMNLP 2017: 1345-1350 - [e1]Ivan Habernal, Iryna Gurevych, Kevin D. Ashley, Claire Cardie, Nancy L. Green, Diane J. Litman, Georgios Petasis, Chris Reed, Noam Slonim, Vern R. Walker:
Proceedings of the 4th Workshop on Argument Mining, ArgMining@EMNLP 2017, Copenhagen, Denmark, September 8, 2017. Association for Computational Linguistics 2017, ISBN 978-1-945626-84-5 [contents] - [i4]Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim:
A Recorded Debating Dataset. CoRR abs/1709.06438 (2017) - 2016
- [c26]Dan Gutfreund, Yoav Katz, Noam Slonim:
Automatic Arguments Construction - From Search Engine to Research Engine. AAAI Fall Symposia 2016 - [c25]Yonatan Bilu, Noam Slonim:
Claim Synthesis via Predicate Recycling. ACL (2) 2016 - [c24]Orith Toledo-Ronen, Roy Bar-Haim, Noam Slonim:
Expert Stance Graphs for Computational Argumentation. ArgMining@ACL 2016 - [c23]Haggai Roitman, Shay Hummel, Ella Rabinovich, Benjamin Sznajder, Noam Slonim, Ehud Aharoni:
On the Retrieval of Wikipedia Articles Containing Claims on Controversial Topics. WWW (Companion Volume) 2016: 991-996 - 2015
- [c22]Ran Levy
TR9856: A Multi-word Term Relatedness Benchmark. ACL (2) 2015: 419-424 - [c21]Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, Noam Slonim:
Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection. EMNLP 2015: 440-450 - [c20]Yonatan Bilu, Daniel Hershcovich, Noam Slonim:
Automatic Claim Negation: Why, How and When. ArgMining@HLT-NAACL 2015: 84-93 - [i3]Iryna Gurevych, Eduard H. Hovy, Noam Slonim, Benno Stein:
Debating Technologies (Dagstuhl Seminar 15512). Dagstuhl Reports 5(12): 18-46 (2015) - 2014
- [c19]Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy
A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics. ArgMining@ACL 2014: 64-68 - [c18]Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh M. Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas C. Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund:
Claims on demand - an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora. COLING (Demos) 2014: 6-9 - [c17]Ran Levy, Yonatan Bilu, Daniel Hershcovich, Ehud Aharoni, Noam Slonim:
Context Dependent Claim Detection. COLING 2014: 1489-1500 - 2013
- [c16]Noam Slonim, Ehud Aharoni, Koby Crammer:
Hartigan's K-Means Versus Lloyd's K-Means - Is It Time for a Change? IJCAI 2013: 1677-1684 - [i2]Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby:
Multivariate Information Bottleneck. CoRR abs/1301.2270 (2013) - 2012
- [c15]Ruty Rinott, Boaz Carmeli, Carmel Kent, Yonatan Maman, Yoav Rubin, Noam Slonim:
Utilizing assigned treatments as labels for supervised machine learning in clinical decision support. IHI 2012: 493-502 - 2011
- [c14]Noam Slonim, Elad Yom-Tov, Koby Crammer:
Active Online Classification via Information Maximization. IJCAI 2011: 1498-1504 - [c13]Ruty Rinott, Boaz Carmeli, Carmel Kent, Daphna Landau, Yonatan Maman, Yoav Rubin, Noam Slonim:
Prognostic Data-Driven Clinical Decision Support - Formulation and Implications. MIE 2011: 140-144 - 2010
- [c12]Yossi Richter, Elad Yom-Tov
Predicting Customer Churn in Mobile Networks through Analysis of Social Groups. SDM 2010: 732-741
2000 – 2009
- 2009
- [c11]Elad Yom-Tov
Parallel Pairwise Clustering. SDM 2009: 745-755 - 2006
- [j2]Noam Slonim, Nir Friedman
Multivariate Information Bottleneck. Neural Comput. 18(8): 1739-1789 (2006) - [c10]Yevgeny Seldin, Noam Slonim, Naftali Tishby:
Information Bottleneck for Non Co-Occurrence Data. NIPS 2006: 1241-1248 - 2005
- [i1]Noam Slonim, Gurinder S. Atwal, Gasper Tkacik, William Bialek:
Estimating mutual information and multi-information in large networks. CoRR abs/cs/0502017 (2005) - 2003
- [j1]Noam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby:
Discriminative Feature Selection via Multiclass Variable Memory Markov Model. EURASIP J. Adv. Signal Process. 2003(2): 93-102 (2003) - 2002
- [c9]Noam Slonim, Gill Bejerano, Shai Fine, Naftali Tishby:
Discriminative Feature Selection via Multiclass Variable Memory Markov Model. ICML 2002: 578-585 - [c8]Noam Slonim, Yair Weiss:
Maximum Likelihood and the Information Bottleneck. NIPS 2002: 335-342 - [c7]Noam Slonim, Nir Friedman
Unsupervised document classification using sequential information maximization. SIGIR 2002: 129-136 - 2001
- [c6]Noam Slonim, Nir Friedman, Naftali Tishby:
Agglomerative Multivariate Information Bottleneck. NIPS 2001: 929-936 - [c5]Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby:
Multivariate Information Bottleneck. UAI 2001: 152-161 - 2000
- [c4]Naftali Tishby, Noam Slonim:
Data Clustering by Markovian Relaxation and the Information Bottleneck Method. NIPS 2000: 640-646 - [c3]Noam Slonim, Naftali Tishby:
Document clustering using word clusters via the information bottleneck method. SIGIR 2000: 208-215
1990 – 1999
- 1999
- [c2]Noam Slonim, Naftali Tishby:
Agglomerative Information Bottleneck. NIPS 1999: 617-623 - 1998
- [c1]Catriel Beeri, Gershon Elber, Tova Milo, Yehoshua Sagiv, Oded Shmueli, Naftali Tishby, Yakov A. Kogan, David Konopnicki, Pini Mogilevski, Noam Slonim:
WebSuite: A Tool Suite for Harnessing Web Data. WebDB 1998: 152-171
Coauthor Index
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