Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
| Challenge: | Named Entity Recognition (NER) is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. |
| Approach: | They propose to use Named Entity Recognition (NER) to detect audio spans with entity mentions in medical records and then use it to evaluate the results. |
| Outcome: | The proposed pipeline is based on a large labeled segment of the Switchboard and Fisher audio datasets and compares it with a benchmark. |
Similar Papers
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. |
Medical Spoken Named Entity Recognition (2025.naacl-industry)
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Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter
| Challenge: | Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. |
| Approach: | They present a spoken NER dataset in the medical domain using pre-trained models that are encoder-only and sequence-to-sequence. |
| Outcome: | The dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types. |
Why Aren’t We NER Yet? Artifacts of ASR Errors in Named Entity Recognition in Spontaneous Speech Transcripts (2023.acl-long)
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Piotr Szymański, Lukasz Augustyniak, Mikolaj Morzy, Adrian Szymczak, Krzysztof Surdyk, Piotr Żelasko
| Challenge: | despite advances in language models, the transcript of spontaneous human-human conversations remains an insurmountable challenge for most models. |
| Approach: | They examine the relationship between ASR and NER errors which limit NER models' ability to recover entity mentions from spontaneous speech transcripts. |
| Outcome: | The proposed model fails even if no word errors are introduced by the ASR . the proposed model's performance deteriorates when applied to the ASL outputs . |
Extracting Biomedical Entities from Noisy Audio Transcripts (2024.lrec-main)
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Nima Ebadi, Kellen Morgan, Adrian Tan, Billy Linares, Sheri Osborn, Emma Majors, Jeremy Davis, Anthony Rios
| Challenge: | Named Entity Recognition (NER) is particularly affected by noise, often termed the ASR-NLP gap. |
| Approach: | They propose a dataset to bridge the ASR-NLP gap in the biomedical domain by extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. |
| Outcome: | The proposed method can clean 2,000 clean and noisy recordings and eliminate errors using zero-shot and few-shot methods. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
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| Challenge: | Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics. |
| Approach: | They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions. |
| Outcome: | The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
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 . |
Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework (2025.naacl-long)
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| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
| Approach: | They propose a framework for clinical named entity recognition that decomposes the entity recognition task into several retrievals of sub-types and then filters them. |
| Outcome: | The proposed framework improves on the clinical named entity recognition task. |
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. |
Decoding Text Spans for Efficient and Accurate Named-Entity Recognition (2026.acl-industry)
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| Challenge: | Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughput constraints in addition to strong accuracy. |
| Approach: | They propose a span-based NER framework that can be used to compute span representations at the final transformer stage, avoiding redundant computation in earlier layers. |
| Outcome: | The proposed framework matches competitive baselines while improving throughput and reducing computational cost. |