Papers with Chunking
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
| Approach: | They propose to combine self-training with noise handling to clean unlabeled data . they propose to model clean and noisy labels separately to improve performance . |
| Outcome: | The proposed method performs better than baseline methods on Chunking and NER. |
To compress or not to compress? A Finite-State approach to Nen verbal morphology (2020.acl-srw)
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| Challenge: | a transitive verb takes up to 1,740 unique features and is highly complex, with a morphological complexity of 80.3% . a finite-state approach has been used to build morphology and phonology resources for Nen, an underresourced language in Papua New Guinea. |
| Approach: | They propose to use Finite-State methods to build a verbal morphological parser for an under-resourced Papuan language, Nen. |
| Outcome: | The proposed model is half the size of the full decomposed model, while the 'Chunking' model is under half the scale of the decomposer, with an overall accuracy of 80.3%. |
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)
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| Challenge: | Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information. |
| Approach: | They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations . |
| Outcome: | The proposed architecture outperforms the best reported results on two standard sequence labeling tasks. |
Design Challenges and Misconceptions in Neural Sequence Labeling (C18-1)
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| Challenge: | Existing neural sequence labeling models have been used for many tasks such as POS tagging, chunking and named entity recognition (NER). |
| Approach: | They propose to replicate twelve neural sequence labeling models and compare them to three benchmarks to find out which models are effective and which are inconsistent. |
| Outcome: | The proposed models are compared on NER, Chunking, and POS tagging benchmarks. |
The ACL OCL Corpus: Advancing Open Science in Computational Linguistics (2023.emnlp-main)
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| Challenge: | ACL OCL is a scholarly corpus derived from the ACL Anthology . it provides metadata, PDF files, citation graphs and additional structured full texts . |
| Approach: | They present ACL OCL, a scholarly corpus derived from the ACL Anthology . it integrates metadata, PDF files, citation graphs and additional structured full texts . they highlight how it applies to observe trends in computational linguistics . |
| Outcome: | The ACL OCL spans seven decades and contains 73,285 papers . the scholarly corpus is based on the ACL Anthology and is available from HuggingFace . |