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Proceedings of the 11th Knowledge Capture Conference, 2021 Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is ...
Armin Haller +5 more
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Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
Applied SciencesBiomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information
Yesol Park, Gyujin Son, Mina Rho
doaj +1 more source
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer [PDF]
arXiv, 2022 Named Entity Recognition is an information extraction task that serves as a preprocessing step for other natural language processing tasks, such as machine translation, information retrieval, and question answering. Named entity recognition enables the identification of proper names as well as temporal and numeric expressions in an open domain text ...
arxiv
Multimodal Locomotion: Next Generation Aerial–Terrestrial Mobile Robotics
Advanced Intelligent Systems, EarlyView., 2023 Aerial–terrestrial robots can achieve efficient energy consumption and robust environmental interaction by adding morphological features, adapting forms for locomotion transitions, and integrating multiple platforms. This next generation of mobile robots advances real‐world robotic deployment for operations with complex tasks and tackle environments ...
Jane Pauline Ramirez, Salua Hamaza
wiley +1 more source
OWNER — Toward Unsupervised Open-World Named Entity Recognition
IEEE AccessNamed Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP), traditionally addressed through supervised learning, which requires extensive annotated corpora. This requirement poses challenges, particularly in specialized domains
Pierre-Yves Genest +3 more
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CRFVoter: gene and protein related object recognition using a conglomerate of CRF-based tools
Journal of Cheminformatics, 2019 Background Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest.
Wahed Hemati, Alexander Mehler
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Named Entity Recognition in Indian court judgments [PDF]
arXiv, 2022 Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc.
arxiv
The multilingual named entity recognition framework [PDF]
Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - EACL '03, 2003 This paper presents a multilingual system designed to recognize named entities in a wide variety of languages (currently more than 12 languages are concerned). The system includes original strategies to deal with a wide variety of encoding character sets, analysis strategies and algorithms to process these languages.
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Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module
Applied Sciences, 2023 Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the
Jinxin Deng +4 more
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Biomedical Named Entity Recognition at Scale [PDF]
, 2021 Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status ...
Veysel Kocaman, David Talby
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