Papers with Named entity recognition
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences (2024.naacl-long)
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| Challenge: | Existing models for named entity recognition fail in scientific domains such as biomedicine and chemistry. |
| Approach: | They propose a model to transfer knowledge from the biomedical domain to the target domain . they use pseudo labeling and contrastive learning to enhance discrimination . |
| Outcome: | The proposed model outperforms baseline models by up to 5% . the proposed model is based on a biomedical domain model and a chemical domain model . |
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection (D19-57)
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| Challenge: | Named entity recognition has been extensively studied on English news texts, but transfer to other domains and languages is still a challenging problem. |
| Approach: | They propose a system that provides a non-standard domain and language setting for pharmacological entity detection in Spanish texts and a sequencelabeling task that requires neither language nor domain expertise. |
| Outcome: | The proposed system achieves up to 88.6% F1 in the PharmaCoNER competition and is based on a sequence labeling task and training on annotated data. |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |
Named Entity Recognition in Multi-level Contexts (2020.aacl-main)
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| Challenge: | Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts. |
| Approach: | They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information. |
| Outcome: | The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information. |
Joint Learning of Named Entity Recognition and Entity Linking (P19-2)
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| Challenge: | Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected . |
| Approach: | They perform joint learning of named entity recognition and entity linking to leverage their relatedness. |
| Outcome: | The proposed model achieves competitive results with the state-of-the-art in both NER and EL tasks. |
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)
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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei
| Challenge: | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. |
| Approach: | They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model. |
| Outcome: | The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance. |
De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases. |
| Approach: | They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective . |
| Outcome: | The proposed method improves the performance of the generative NER model in various datasets. |
An Empirical Study on Fine-Grained Named Entity Recognition (C18-1)
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Khai Mai, Thai-Hoang Pham, Minh Trung Nguyen, Tuan Duc Nguyen, Danushka Bollegala, Ryohei Sasano, Satoshi Sekine
| Challenge: | Named entity recognition (NER) is a well studied topic in natural language processing. |
| Approach: | They propose to remove the CNN layer and use dictionary and category embeddings to improve Japanese FG-NER performance. |
| Outcome: | The proposed method improves Japanese FG-NER F-score from 66.76% to 75.18%. |
Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition break the recognition process into several sequential steps. |
| Approach: | They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it . |
| Outcome: | Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model. |
Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)
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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang
| Challenge: | Named entity recognition is a fundamental task in natural language processing. |
| Approach: | They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on nested and flat datasets. |
Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)
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| Challenge: | Named entity recognition (NER) is a key task reliant on textual data. |
| Approach: | They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries. |
| Outcome: | The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets. |
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT (2020.coling-main)
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| Challenge: | Named entity recognition (NER) is often addressed as a sequence classification task with each input consisting of one sentence of text. |
| Approach: | They propose a method to combine different predictions from multiple sentences in input samples to increase NER performance. |
| Outcome: | The proposed method improves on the state-of-the-art NER results on English, Dutch, and Finnish and achieves the best reported BERT-based results on German. |
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)
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| Challenge: | Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains. |
| Approach: | They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations. |
| Outcome: | The proposed system improves the NER performance even with a tiny amount of labeled data. |
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)
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| Challenge: | Named entity recognition (NER) is the task to detect and classify entity spans in text. |
| Approach: | They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results . |
| Outcome: | The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets. |
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)
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| Challenge: | Named entity recognition and relation extraction are two important fundamental problems. |
| Approach: | They propose to design two separate encoders to capture two different types of information in the representation learning process. |
| Outcome: | The proposed methods show significant improvements on standard datasets. |
A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing. |
| Approach: | They propose a query-parallel MRC-based approach to named entity recognition . the model is trained with parameter-efficient tuning technique, making it more data-efficient . |
| Outcome: | The proposed model performs competitively against strong baseline methods in resource-rich settings and achieves state-of-the-art results in low-resource settings. |
Hero-Gang Neural Model For Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) is a fundamental and important task in natural language processing. |
| Approach: | They propose a novel Hero-Gang Neural structure to leverage both global and local information to promote NER by using a Transformer-based encoder and a Gang module. |
| Outcome: | The proposed model can extract local features and position information from the Hero and Gang modules, and it performs on multiple datasets. |
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language. |
| Approach: | They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain. |
| Outcome: | The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL. |
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
A Data-driven Approach to Named Entity Recognition for Early Modern French (2022.coling-1)
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| Challenge: | Named entity recognition is an important task in natural language processing. |
| Approach: | They propose to use a data-driven approach to identify historical French with fine-grained annotations instead of a specialised architecture to tackle particularities. |
| Outcome: | The proposed corpus is larger than the most popular NER evaluation corpora for both Contemporary English and French. |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
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| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
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. |
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)
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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler
| Challenge: | Named entity recognition is a technique for extracting information from unstructured data with known labels. |
| Approach: | They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases . |
| Outcome: | The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods . |
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
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| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) (2022.lrec-1)
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| Challenge: | Existing medical social media corpora focus on a small set of entities and relations . existing text mining and information extraction methods focus on scientific text generated by researchers but their access to individual patient experiences or patient-doctor interactions is limited. |
| Approach: | The dataset consists of 2,100 medical tweets with approx. 6,000 entities and 2,200 relations. |
| Outcome: | The proposed dataset consists of 2,100 tweets with approx. 6,000 entities and 2,200 relations. |
NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
| Approach: | They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank. |
| Outcome: | The proposed dataset comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. |
Where are we in Named Entity Recognition from Speech? (2020.lrec-1)
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| Challenge: | Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs. |
| Approach: | They propose an original 3-pass approach and explore the capability of an E2E system to do structured NER. |
| Outcome: | The proposed system performs better than the current pipeline approach. |
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition identifies common classes of noun phrases in text, but these entity labels are sparse, limiting utility to downstream tasks. |
| Approach: | They propose a name-based named entity recognition model that annotates all content words with a fine-grained semantic class label. |
| Outcome: | The proposed model achieves 0.85 F1 on the science exam domain domain . the proposed model is a powerful tool for question answering and inference . |
Building OCR/NER Test Collections (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is the automatic recognition of spans of text as name mentions. |
| Approach: | They propose a method for annotating named entities on transcribed text . the transcriptions are all that is needed to evaluate the performance of OCR . |
| Outcome: | The proposed collection can be used to evaluate OCR and NER on transcribed text without re-annotation . the transcriptions are all that is needed to evaluate the performance of OCR . |
Merge and Label: A Novel Neural Network Architecture for Nested NER (P19-1)
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| Challenge: | Named entity recognition (NER) is one of the best studied tasks in natural language processing. |
| Approach: | They propose a neural network architecture that merges tokens and/or entities into nested entities and labels them independently. |
| Outcome: | The proposed approach achieves state-of-the-art F1 of 74.6 and improves with contextual embeddings to 82.4. |
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is the backbone of many NLP solutions. |
| Approach: | They propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. |
| Outcome: | The proposed model achieves better precision-recall trade-off at various precision levels. |
Neural Language Modeling for Named Entity Recognition (2020.coling-main)
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| Challenge: | Experimental results show that named entity recognition systems are faster and more flexible for the size of the corpus. |
| Approach: | They propose to use a neural language model as an alternative to the conditional random field layer for named entity recognition. |
| Outcome: | The proposed system has a significant speed advantage with a marginal performance degradation. |
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a key component of many text processing pipelines. |
| Approach: | They propose a new architecture tailored to the task of identifying named entities with data from multiple genres. |
| Outcome: | The proposed architecture outperforms baseline and competitive methods on all three setups with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. |
Learning In-context Learning for Named Entity Recognition (2023.acl-long)
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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
| Challenge: | Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations. |
| Approach: | They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. |
| Outcome: | The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors. |
A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space . |
| Approach: | They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset . |
| Outcome: | The proposed method outperforms existing methods on eight widely-used NER datasets. |
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 . |