Papers with named entity recognition task
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling (2020.acl-main)
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| Challenge: | Existing approaches to slot filling are expensive and time-consuming. |
| Approach: | They propose a Coarse-to-fine approach for cross-domain slot filling . they propose utterance templates to regularize the representation of utterrances . |
| Outcome: | The proposed model outperforms state-of-the-art approaches in slot filling . it can be applied to the cross-domain named entity recognition task . |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
Dice Loss for Data-imbalanced NLP Tasks (2020.acl-main)
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| Challenge: | Using dice loss, we find that data imbalance is a common issue in many NLP tasks . data imbalance affects the performance of many tasks, such as tagging and machine reading comprehension . |
| Approach: | They propose to use dice loss to replace the standard cross-entropy objective for data-imbalanced NLP tasks. |
| Outcome: | The proposed training objective achieves significant performance boost on a wide range of data imbalanced tasks. |
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
| Outcome: | The proposed method enhances the training performance of named entity recognition tasks while maintaining context-entity relationships. |
SeNsER: Learning Cross-Building Sensor Metadata Tagger (2020.findings-emnlp)
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| Challenge: | Sensor metadata tagging is a key component of smart building applications. |
| Approach: | They propose a framework that learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. |
| Outcome: | The proposed framework learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. |
OptSLA: an Optimization-Based Approach for Sequential Label Aggregation (2020.findings-emnlp)
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| Challenge: | Existing approaches to annotate data are based on binary and multi-choice problems, but little work has been done on complex tasks such as sequence labeling with imbalanced classes. |
| Approach: | They propose an optimization-based Sequential Label Aggregation method that integrates worker reliabilities and advanced deep learning techniques to conquer the challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods and is easier to interpret. |
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods to increase training data in low-resource domains may not be effective due to data scarcity. |
| Approach: | They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training. |
| Outcome: | The proposed method can significantly improve results on five domain pairs under different data regimes. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks. |
| Approach: | They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans. |
| Outcome: | The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets. |
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (2020.lrec-1)
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| Challenge: | a recent study shows that word embeddings can be useful for training downstream natural language processing tasks. |
| Approach: | They compare word embeddings obtained by word embeds from curated corpora with a language-dependent processing. |
| Outcome: | The proposed model compares word embeddings with word embeds from curated corpora and a language-dependent processing on two African languages. |
KinyaBERT: a Morphology-aware Kinyarwanda Language Model (2022.acl-long)
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| Challenge: | Pre-trained language models such as BERT are sub-optimal at handling morphologically rich languages. |
| Approach: | They propose a two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphology in a low-resource Kinyarwanda language. |
| Outcome: | The proposed model outperforms baseline models on the low-resource morphologically rich Kinyarwanda language by 2% in F1 score and 4.3% in average score of GLUE benchmark. |