Amir Menczel


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Learning to combine Grammatical Error Corrections
Yoav Kantor | Yoav Katz | Leshem Choshen | Edo Cohen-Karlik | Naftali Liberman | Assaf Toledo | Amir Menczel | Noam Slonim
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F-score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F-0.5 score by 3.7 points over the best result reported.

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From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
Roy Bar-Haim | Dalia Krieger | Orith Toledo-Ronen | Lilach Edelstein | Yonatan Bilu | Alon Halfon | Yoav Katz | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.


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Learning Sentiment Composition from Sentiment Lexicons
Orith Toledo-Ronen | Roy Bar-Haim | Alon Halfon | Charles Jochim | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 27th International Conference on Computational Linguistics

Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.