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Lili Kotlerman

dblp:40/8776 · DBLP profile ↗
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7ranked-venue papers
3as first author
0since 2021 · last 2019
0000-0002-3141-9475ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Language models and text generation · 38% Information extraction and text analysis · 37% Question answering and dialogue systems · 25%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
argument mining		 0.522019
A Dataset of General-Purpose Rebuttal · EMNLP/IJCNLP (1) 2019
Listening Comprehension over Argumentative Content · EMNLP 2018
Natural language and speech › Language models and text generation › text generation › argument generation
rebuttal generation		 0.412019
A Dataset of General-Purpose Rebuttal · EMNLP/IJCNLP (1) 2019
Natural language and speech › Language models and text generation
text generation		 0.112019
A Dataset of General-Purpose Rebuttal · EMNLP/IJCNLP (1) 2019

Methods — techniques the papers use, named apart from their topics

dataset construction · 0.4question generation · 0.3natural language inference · 0.3
YearPublicationVenuePosition
2019 A Dataset of General-Purpose Rebuttal
abstract
Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
 EMNLP/IJCNLP (1)7
2018 Listening Comprehension over Argumentative Content
abstract
Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
 EMNLP4
2018 A Recorded Debating Dataset
Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Jeff Kuo, Samuel Thomas 0001, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
 LREC7
2018 Clustering small-sized collections of short texts
Lili Kotlerman, Ido Dagan, Oren Kurland
 Inf. Retr. J.1
2017 Joint Learning of Correlated Sequence Labeling Tasks Using Bidirectional Recurrent Neural Networks
Vardaan Pahuja, Anirban Laha, Shachar Mirkin, Vikas C. Raykar, Lili Kotlerman, Guy Lev
 INTERSPEECH5
2015 Textual entailment graphs
abstract
Abstract In this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.
Lili Kotlerman, Ido Dagan, Bernardo Magnini, Luisa Bentivogli
 Nat. Lang. Eng.1
2010 Directional distributional similarity for lexical inference
abstract
Abstract Distributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion. We identify desired properties of such measures for lexical inference, specify a particular measure based on Average Precision that addresses these properties, and demonstrate the empirical benefit of directional measures for two different NLP datasets.
Lili Kotlerman, Ido Dagan, Idan Szpektor, Maayan Zhitomirsky-Geffet
 Nat. Lang. Eng.1