Papers with NER models
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. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a type of annotation that classifies text into predefined classes such as person, location, organization etc. |
| Approach: | They propose to use a named entity annotation dataset for low resource Assamese language with a baseline NER model. |
| Outcome: | The proposed dataset is likely to be significant resource for deep neural based Assamese language processing. |