no code implementations • 17 Jan 2021 • Stefan Maetschke, David Martinez Iraola, Pieter Barnard, Elaheh ShafieiBavani, Peter Zhong, Ying Xu, Antonio Jimeno Yepes
A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities.
6 code implementations • ECCV 2020 • Xu Zhong, Elaheh ShafieiBavani, Antonio Jimeno Yepes
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.
Ranked #11 on Table Recognition on PubTabNet
no code implementations • WS 2020 • Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong, David Martinez Iraola
Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining.
no code implementations • EMNLP 2018 • Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
ROUGE is one of the first and most widely used evaluation metrics for text summarization.
no code implementations • CONLL 2018 • Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong, Fang Chen
Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation.
no code implementations • COLING 2018 • Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
We present a new summary evaluation approach that does not require human model summaries.
no code implementations • 20 Oct 2017 • Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
ROUGE is one of the first and most widely used evaluation metrics for text summarization.
no code implementations • COLING 2016 • Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
When making clinical decisions, practitioners need to rely on the most relevant evidence available.
no code implementations • 7 May 2016 • Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i. e., improving both informativity and grammaticality at the same time.