Oren Tsur


2021

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With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms
Yotam Shichel | Meir Kalech | Oren Tsur
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Sentence Compression is the task of generating a shorter, yet grammatical, version of a given sentence, preserving the essence of the original sentence. This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed – find the best candidates for compression in order to maximize both compression rate and quality. Given a required compression ratio, we consider two scenarios: (i) single-sentence compression, and (ii) sentences-sequence compression. In the first scenario our optimizer is trained to predict how well each sentence could be compressed while meeting the specified ratio requirement. In the latter, the desired compression ratio is applied to a sequence of sentences (e.g., a paragraph) as a whole, rather than on each individual sentence. To achieve that we use B-BOC to assign an optimal compression ratio to each sentence, then cast it as a Knapsack problem which we solve using bounded dynamic programming. We evaluate B-BOC on both scenarios on three datasets, demonstrating that our optimizer improves both accuracy and Rouge-F1-score compared to direct application of other compression algorithms.

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Open-Mindedness and Style Coordination in Argumentative Discussions
Aviv Ben-Haim | Oren Tsur
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Linguistic accommodation is the process in which speakers adjust their accent, diction, vocabulary, and other aspects of language according to the communication style of one another. Previous research has shown how linguistic accommodation correlates with gaps in the power and status of the speakers and the way it promotes approval and discussion efficiency. In this work, we provide a novel perspective on the phenomena, exploring its correlation with the open-mindedness of a speaker, rather than to her social status. We process thousands of unstructured argumentative discussions that took place in Reddit’s Change My View (CMV) subreddit, demonstrating that open-mindedness relates to the assumed role of a speaker in different contexts. On the discussion level, we surprisingly find that discussions that reach agreement present lower levels of accommodation.

2019

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Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Svitlana Volkova | David Jurgens | Dirk Hovy | David Bamman | Oren Tsur
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

2017

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Proceedings of the Second Workshop on NLP and Computational Social Science
Dirk Hovy | Svitlana Volkova | David Bamman | David Jurgens | Brendan O’Connor | Oren Tsur | A. Seza Doğruöz
Proceedings of the Second Workshop on NLP and Computational Social Science

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ConStance: Modeling Annotation Contexts to Improve Stance Classification
Kenneth Joseph | Lisa Friedland | William Hobbs | David Lazer | Oren Tsur
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model’s interpretable parameters shed light on the effects of each context.

2016

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Proceedings of the First Workshop on NLP and Computational Social Science
David Bamman | A. Seza Doğruöz | Jacob Eisenstein | Dirk Hovy | David Jurgens | Brendan O’Connor | Alice Oh | Oren Tsur | Svitlana Volkova
Proceedings of the First Workshop on NLP and Computational Social Science

2015

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A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns
Oren Tsur | Dan Calacci | David Lazer
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media
Alice Oh | Benjamin Van Durme | David Yarowsky | Oren Tsur | Svitlana Volkova
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

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As Long as You Name My Name Right: Social Circles and Social Sentiment in the Hollywood Hearings
Oren Tsur | Dan Calacci | David Lazer
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

2013

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Authorship Attribution of Micro-Messages
Roy Schwartz | Oren Tsur | Ari Rappoport | Moshe Koppel
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2010

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Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Dmitry Davidov | Oren Tsur | Ari Rappoport
Coling 2010: Posters

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Semi-Supervised Recognition of Sarcasm in Twitter and Amazon
Dmitry Davidov | Oren Tsur | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

2007

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Using Classifier Features for Studying the Effect of Native Language on the Choice of Written Second Language Words
Oren Tsur | Ari Rappoport
Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition

2004

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BioGrapher: Biography Questions as a Restricted Domain Question Answering Task
Oren Tsur | Maarten de Rijke | Khalil Sima’an
Proceedings of the Conference on Question Answering in Restricted Domains