Effi Levi


Detecting Narrative Elements in Informational Text
Effi Levi | Guy Mor | Tamir Sheafer | Shaul Shenhav
Findings of the Association for Computational Linguistics: NAACL 2022

Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) – a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.


Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference
Effi Levi | Roi Reichart | Ari Rappoport
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


How Well Do Distributional Models Capture Different Types of Semantic Knowledge?
Dana Rubinstein | Effi Levi | Roy Schwartz | Ari Rappoport
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)