Challenge: Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation.
Approach: They propose a framework that combines active learning and weak supervision to solve this problem.
Outcome: The proposed framework enables learning of high-quality models from a dozen labeled examples.

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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.
Outcome: The proposed model improves the quality of weak labels on four public datasets.
Exploiting Structure in Representation of Named Entities using Active Learning (C18-1)

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Challenge: Named entities are atomic objects of reference and reasoning in many knowledge-centric applications.
Approach: They propose an active-learning based framework that drastically reduces the labeled data required to learn entities' structures.
Outcome: The proposed framework outperforms handwritten programs and supervised learning models in relation extraction and entity resolution tasks.
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 .
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities (N19-1)

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Challenge: Scholars in interdisciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora.
Approach: They propose an active learning solution for named entity recognition that maximizes a custom model’s improvement per additional unit of manual annotation.
Outcome: The proposed model reduces required annotation by 20-60% and outperforms a competitive active learning baseline.
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification (C18-1)

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Challenge: a recent study compares semi-supervised learning methods with bootstrapping methods . semi-semi-supervised methods reduce the amount of semantic drift introduced by iterative approaches .
Approach: They propose to adapt three semi-supervised representation learning methods to an information extraction task . they show that all methods outperform state-of-the-art semi-representation learning methods .
Outcome: The proposed methods outperform state-of-the-art semi-supervised methods on named entity classification task.
Weakly Supervised Attention Networks for Entity Recognition (D19-1)

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Challenge: Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain.
Approach: They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels.
Outcome: The proposed model performs reasonably well on the task of entity recognition despite not having access to token-level ground truth data.
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)

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Challenge: Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories.
Approach: They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy.
Outcome: The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios.
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.
Contextualized Weak Supervision for Text Classification (2020.acl-main)

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Challenge: Existing methods for weakly supervised text classification generate pseudo-labels in a context-free manner, thus, the ambiguous, context-dependent nature of human language has been long overlooked.
Approach: They propose a framework that provides contextualized weak supervision for text classification . they leverage contextualized representations of word occurrences and seed word information .
Outcome: The proposed framework provides contextualized weak supervision for text classification . it leverages representations of word occurrences and seed word information to differentiate interpretations . the proposed framework also disambiguates initial seed words, making it fully contextualized .
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.
Outcome: The proposed model improves performance by using type labels generated from a BERT Masked Language Model given a mention in a sentence.

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