| Challenge: | Existing methods for name tagging in low-resource languages or domains require extensive human efforts for training annotations. |
| Approach: | They propose a neural model for name tagging based on weakly labeled (WL) data. |
| Outcome: | The proposed model outperforms existing models in five low-resource languages and fine-grained food domains and shows that it is more efficient and efficient than existing models. |
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Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data (2021.acl-long)
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| Challenge: | Existing work focuses on learning deep NER models with weak supervision without any human annotation. |
| Approach: | They propose a framework that can suppress the noise of the weak labels and fine-tune over the strongly labeled data. |
| Outcome: | The proposed framework outperforms existing methods on Named Entity Recognition tasks with weak supervision and weakly labeled data. |
Named Entity Recognition through Deep Representation Learning and Weak Supervision (2021.findings-acl)
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| Challenge: | Weakly supervised named entity recognition (NER) uses noisy labels to estimate the true labels of a dataset. |
| Approach: | They propose a model to learn optimal assignments of latent NER tags using observed tokens and weak labels provided by labeling functions. |
| Outcome: | The proposed model improves the quality of weak labels on four public datasets. |
Weakly Supervised Named Entity Tagging with Learnable Logical Rules (2021.acl-long)
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| Challenge: | Existing methods for building entity tagging systems use weak supervision . previous methods focus on disambiguating entity types based on contexts and expert-provided rules . |
| Approach: | They propose a method that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. |
| Outcome: | The proposed method outperforms weakly supervised methods on three datasets . it rivals state-of-the-art supervised method with lexicon of over 2,000 terms . |
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
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| Challenge: | Existing approaches to improve supervised labeling with noisy training data do not take the input features into account or they need to learn the noise modeling from scratch. |
| Approach: | They propose to cluster training data using input features and compute different confusion matrices for each cluster. |
| Outcome: | The proposed model improves on low-resource named entity recognition settings in several languages, compared with other models which do not take the input features into account or need to learn noise modeling from scratch. |
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. |
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
| Outcome: | The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model. |
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
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| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Soft Gazetteers for Low-Resource Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing named entity recognition models use gazetteers to improve performance, but they are limited in coverage and do not exist in low-resource languages. |
| Approach: | They propose a method that integrates Wikipedia information into named entity models by cross-lingual entity linking. |
| Outcome: | The proposed method improves on four low-resource languages with Wikipedia . it incorporates available information from english knowledge bases into neural models . |
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
| Approach: | They propose a conditional hidden Markov model which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
| Outcome: | The proposed model outperforms state-of-the-art weakly supervised NER models on four benchmarks from various domains. |