Aynat Rubinstein


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NLP in the DH pipeline: Transfer-learning to a Chronolect
Aynat Rubinstein | Avi Shmidman
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

A big unknown in Digital Humanities (DH) projects that seek to analyze previously untouched corpora is the question of how to adapt existing Natural Language Processing (NLP) resources to the specific nature of the target corpus. In this paper, we study the case of Emergent Modern Hebrew (EMH), an under-resourced chronolect of the Hebrew language. The resource we seek to adapt, a diacritizer, exists for both earlier and later chronolects of the language. Given a small annotated corpus of our target chronolect, we demonstrate that applying transfer-learning from either of the chronolects is preferable to training a new model from scratch. Furthermore, we consider just how much annotated data is necessary. For our task, we find that even a minimal corpus of 50K tokens provides a noticeable gain in accuracy. At the same time, we also evaluate accuracy at three additional increments, in order to quantify the gains that can be expected by investing in a larger annotated corpus.

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The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing
Valentina Pyatkin | Shoval Sadde | Aynat Rubinstein | Paul Portner | Reut Tsarfaty
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.


Proceedings of the IWCS 2013 Workshop on Annotation of Modal Meanings in Natural Language (WAMM)
Paul Portner | Aynat Rubinstein | Graham Katz
Proceedings of the IWCS 2013 Workshop on Annotation of Modal Meanings in Natural Language (WAMM)

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Toward Fine-grained Annotation of Modality in Text
Aynat Rubinstein | Hillary Harner | Elizabeth Krawczyk | Daniel Simonson | Graham Katz | Paul Portner
Proceedings of the IWCS 2013 Workshop on Annotation of Modal Meanings in Natural Language (WAMM)