Papers with ACE2005
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
| Outcome: | The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE. |
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)
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| Challenge: | Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system. |
| Approach: | They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news. |
| Outcome: | The proposed system detects event mentions and extracts events from financial news . it can generate large scale labeled data and extract events from entire document . |
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |
Nugget Proposal Networks for Chinese Event Detection (P18-1)
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| Challenge: | Nugget Proposal Networks (NPNs) can solve word-trigger mismatch problem . word-wise event detection models suffer from word-tree mismatch because of multiple triggers . |
| Approach: | They propose a novel way to detect event triggers in a character-wise paradigm . they propose entire trigger nuggets centered at each character regardless of word boundaries . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two datasets. |
Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing work on few-shot named entity recognition (NER) addresses flat entities instead of nested entities. |
| Approach: | They propose a method based on focusing, bridging and prompting for few-shot nested NER without using source domain data. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed several competing models on F1-score by 9.33% on ACE2004, 6.17% on ace2005, 9.40% on GENIA and 5.12% on KBP2017. |
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (2022.acl-long)
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| Challenge: | Constituency parsing and nested named entity recognition (NER) are similar tasks since they aim to predict a collection of nesting and non-crossing spans. |
| Approach: | They propose a model that uses a pointer network to predict a constituency tree's boundary . constituency parsing is an important task in natural language processing . |
| Outcome: | The proposed model achieves state-of-the-art performance on PTB among all BERT-based models and competitive performance on CTB7 in constituency parsing. |
Event Detection with Dual Relational Graph Attention Networks (2022.coling-1)
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| Challenge: | Existing approaches to event detection confuse syntactic relations and introduce redundant or noisy information, leading to performance degradation. |
| Approach: | They propose a model that exploits syntactic and semantic relations to alleviate the problem by combining syntatic and semantic knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a ACE2005 benchmark dataset. |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)
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| Challenge: | Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles. |
| Approach: | They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE. |
| Outcome: | The proposed system outperforms existing methods and is more powerful for sentences with multiple events. |
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition (NER) is a fundamental natural language processing task that extracts entities from texts. |
| Approach: | They propose a triaffine mechanism which integrates heterogeneous factors into a single model to fuse these factors into one model to achieve better span representation. |
| Outcome: | The proposed method outperforms previous span-based methods and achieves state-of-the-art F1 scores on nested NER datasets GENIA and KBP2017. |
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition assume entities are not nested within other entities, so-called flat NER. |
| Approach: | They propose a layered method for nested named entity recognition . they use a set of hidden states to exclude the influence of the best path . |
| Outcome: | The proposed method performs better on ACE2004, ACE2005, and GENIA datasets. |
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing (2022.acl-long)
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| Challenge: | Existing methods to recognize named entities have been criticized for their performance on flat NER but fail to handle nested entities. |
| Approach: | They propose to use a span-based constituency parser to tackle nested NER . they use lexicalized constituency trees to model nesting entities . |
| Outcome: | The proposed method achieves state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive performance on the GENIA platform. |
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (P19-1)
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| Challenge: | Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. |
| Approach: | They propose a Delta-learning approach to distill discrimination and generalization knowledge by incrementally learning and adaptively fusing event representation. |
| Outcome: | The proposed method significantly outperforms previous approaches on unseen/sparse trigger words and achieves state-of-the-art performance on ACE2005 and KBP2017 datasets. |
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)
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| Challenge: | Entity and Relation Extraction (ERE) is an important task in information extraction. |
| Approach: | They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module . |
| Outcome: | The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module . |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
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| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner. |
| Approach: | They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting. |