Patricia Lichtenstein


2020

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Proceedings of the Second Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee | Anna Feldman | Debanjan Ghosh
Proceedings of the Second Workshop on Figurative Language Processing

2018

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Proceedings of the Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee
Proceedings of the Workshop on Figurative Language Processing

2017

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Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning
Ekaterina Shutova | Lin Sun | Elkin Darío Gutiérrez | Patricia Lichtenstein | Srini Narayanan
Computational Linguistics, Volume 43, Issue 1 - April 2017

Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques—with little or no annotation—to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups—English, Spanish, and Russian—achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.

2016

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Detecting Cross-Cultural Differences Using a Multilingual Topic Model
E.D. Gutiérrez | Ekaterina Shutova | Patricia Lichtenstein | Gerard de Melo | Luca Gilardi
Transactions of the Association for Computational Linguistics, Volume 4

Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society. Yet, the majority of text-mining methods to date focus on the analysis of monolingual texts. In contrast, we present a statistical model that simultaneously learns a set of common topics from multilingual, non-parallel data and automatically discovers the differences in perspectives on these topics across linguistic communities. We perform a behavioural evaluation of a subset of the differences identified by our model in English and Spanish to investigate their psychological validity.

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Proceedings of the Fourth Workshop on Metaphor in NLP
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein
Proceedings of the Fourth Workshop on Metaphor in NLP

2015

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Proceedings of the Third Workshop on Metaphor in NLP
Ekaterina Shutova | Beata Beigman Klebanov | Patricia Lichtenstein
Proceedings of the Third Workshop on Metaphor in NLP

2014

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Proceedings of the Second Workshop on Metaphor in NLP
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein
Proceedings of the Second Workshop on Metaphor in NLP