Papers with F1 value
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction (D19-57)
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| Challenge: | BioNLP 2019 Shared Tasks: binary relation extraction of SeeDev task . Biological information extraction (Bio-IE) is a new field of research . |
| Approach: | They propose to use convolutional neural networks and long short term memory networks to construct a binary relation extraction model. |
| Outcome: | The proposed method performed well in the binary relation extraction task. |
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)
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| Challenge: | Existing methods for event causality identification (ECI) rely on labeled data, but the scale of annotated datasets is limited. |
| Approach: | They propose a self-supervised framework to learn context-specific causal patterns from external causal statements and adopt a contrastive transfer strategy to incorporate the learned context- specific causal patterns into the target ECI model. |
| Outcome: | The proposed method significantly outperforms existing methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively). |
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021.acl-long)
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| Challenge: | Existing methods for event causality identification (ECI) rely on annotated training data. |
| Approach: | They propose a method to augment training data for event causality identification by iteratively generating new examples and classifying event causalities in a dual learning framework. |
| Outcome: | The proposed method outperforms existing methods on EventStoryLine and Causal-TimeBank. |
No Need for Large-Scale Search: Exploring Large Language Models in Complex Knowledge Base Question Answering (2024.lrec-main)
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| Challenge: | Knowledge Base Question Answering (KBQA) systems are a key research area in the field of natural language processing and information retrieval (IR). |
| Approach: | They propose to use large language models to convert natural language questions to structured knowledge representations by using a three-step fine-tune strategy to implement the KBQA system. |
| Outcome: | The proposed method achieves state-of-the-art performance across three datasets with a 79.9% F1 score. |
A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts (2025.findings-acl)
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| Challenge: | a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated (aligned) pairs of witnesses. |
| Approach: | The project is based on the relationship between available witnesses of biblical Hebrew and a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated pairs of witnesses. |
| Outcome: | The proposed model is non-neural and uses part-of-speech tags, hand-crafted rules per category and synthetically derived data. |