Named entity recognition
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Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods consider the named entity recognition as span classification task, can deal with nested entities naturally.
7p viberkshire 09-08-2023 8 4 Download
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Named entities containing other named entities inside are referred to as nested entities, which commonly exist in news articles and other documents. However, most studies in the field of Vietnamese named entity recognition entirely ignore nested entities.
7p viberkshire 09-08-2023 8 4 Download
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Named entities (NE) are phrases that contain the names of persons, organizations, locations, times, quantities, email, phone number, etc., in a document. Named Entity Recognition (NER) is a fundamental task that is useful in many applications, especially in information extraction and question answering.
11p viberkshire 09-08-2023 7 5 Download
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MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java.
4p visteverogers 24-06-2023 5 2 Download
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Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies.
9p vighostrider 25-05-2023 3 2 Download
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Accurate extraction of breast cancer patients’ phenotypes is important for clinical decision support and clinical research. This study developed and evaluated cancer domain pretrained CancerBERT models for extracting breast cancer phenotypes from clinical texts.
9p vighostrider 25-05-2023 2 2 Download
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This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and Methods: We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries.
9p vighostrider 25-05-2023 3 2 Download
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The model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The e®ect of the model was demonstrated by in-domain and crossdomain experiments, achieving promising results.
17p redemption 20-12-2021 14 1 Download
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Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain.
17p viwyoming2711 16-12-2020 13 1 Download
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The significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction.
9p vikentucky2711 26-11-2020 10 0 Download
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Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.
9p vioklahoma2711 19-11-2020 16 3 Download
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Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance.
14p viflorida2711 30-10-2020 9 2 Download
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In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles. After NER is applied to articles, the next step is to normalize the identified names into standard concepts (i.e., disease names are mapped to the National Library of Medicine’s Medical Subject Headings disease terms).
12p viflorida2711 30-10-2020 14 2 Download
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Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks.
12p viflorida2711 30-10-2020 17 2 Download
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Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.
11p viconnecticut2711 29-10-2020 21 1 Download
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For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or “grounding.” Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets.
14p viconnecticut2711 28-10-2020 13 1 Download
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Biomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, neural network models are used to automatically learn features.
13p vicolorado2711 23-10-2020 12 1 Download
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In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora.
11p vicolorado2711 23-10-2020 15 0 Download
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Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis.
14p vicolorado2711 22-10-2020 10 0 Download
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Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers.
15p vicolorado2711 22-10-2020 19 0 Download