Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information (2020.findings-emnlp)
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| Challenge: | Existing studies have shown that named entity recognition (NER) is effective in encoding and aggregating syntactic information, but they lack the appropriate knowledge to model such properties. |
| Approach: | They propose to leverage syntactic information by leveraging attentive ensembles to model NER . they propose key-value memory networks, syntax attention and gate mechanism for encoding, weighting and aggregating syntaktic information. |
| Outcome: | The proposed model outperforms previous studies on six English and Chinese benchmark datasets. |
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons (2020.findings-emnlp)
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| Challenge: | Existing named entity recognition systems require large scale labeled data to perform, while annotation of NER data is laborious and time-consuming. |
| Approach: | They propose to adjust an existing named entity recognition system to recognize entity types not defined in the system. |
| Outcome: | The proposed method can be quickly adjusted to a named entity recognition system. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
NLATool: an Application for Enhanced Deep Text Understanding (C18-2)
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Markus Gärtner, Sven Mayer, Valentin Schwind, Eric Hämmerle, Emine Turcan, Florin Rheinwald, Gustav Murawski, Lars Lischke, Jonas Kuhn
| Challenge: | a wide range of subfields in natural language processing see systems solving their tasks with sufficiently high-quality levels. |
| Approach: | They propose a web application that supports text annotation and enriches the text with additional information from a number of sources directly within the application. |
| Outcome: | The proposed web application is based on a human-centered design process . it offers a rich visualization of texts and the entities mentioned in them through an easy to use interface. |
UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
| Approach: | They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity. |
| Outcome: | The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER . |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
Code and Named Entity Recognition in StackOverflow (2020.acl-main)
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| Challenge: | StackOverflow has 15 million programming related questions written by 8.5 million users . however, there is a lack of fundamental NLP resources and techniques for identifying software-related named entities within natural language sentences. |
| Approach: | They propose a named entity recognition corpus for the computer programming domain with 15,372 sentences annotated with 20 fine-grained entity types. |
| Outcome: | The proposed model improves on 152 million sentences from StackOverflow and achieves 79.10 F-1 score for code and named entity recognition. |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
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Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy (2025.coling-main)
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| Challenge: | Existing models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), which hinders the achievement of satisfactory performance. |
| Approach: | They propose a framework which fully leverages sentence-level information to improve OOE-NER performance by exploiting pre-trained language models' ability to understand target entity’s sentence context with a template set and refines sentence representation based on positive and negative templates. |
| Outcome: | The proposed framework outperforms state-of-the-art models on five datasets on named entity recognition (NER) tasks. |