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. |
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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. |
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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. |
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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 . |
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Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
| Approach: | They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision. |
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
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Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning (P19-1)
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| Challenge: | Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. |
| Approach: | They propose a method to perform named entity recognition using unlabeled data and named entity dictionaries. |
| Outcome: | The proposed method can estimate task loss as if there is fully labeled data. |
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. |
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Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model (2021.acl-long)
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| Challenge: | Existing methods for fine-grained entity typing use weak labels that are automatically generated. |
| Approach: | They propose to obtain training data by using a BERT Masked Language Model (MLM) given a mention in a sentence, they construct an input for the MLM so it predicts context dependent hypernyms of the mention, which can be used as type labels. |
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Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
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Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
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