Named-entity recognition ≈ Named-entity recognition
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining Open
Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature ha…
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Neural Architectures for Named Entity Recognition Open
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing Open
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing …
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Publicly Available Clinical Open
Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such …
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BioCreative V CDR task corpus: a resource for chemical disease relation extraction Open
Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity re…
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Publicly Available Clinical BERT Embeddings Open
Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been mi…
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A Unified MRC Framework for Named Entity Recognition Open
The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not.Models are usually separately developed for the two tasks, since sequence labeling models, th…
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Deep learning with word embeddings improves biomedical named entity recognition Open
Motivation Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pr…
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Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features Open
Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of …
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention Open
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats…
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Fine-Grained Entity Recognition Open
Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g.,…
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Deep learning in clinical natural language processing: a methodical review Open
Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and co…
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Named Entity Recognition as Dependency Parsing Open
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the f…
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An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition Open
Motivation In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods a…
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The CHEMDNER corpus of chemicals and drugs and its annotation principles Open
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manual…
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Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings Open
We consider the task of named entity recognition for Chinese social media. The long line of work in Chinese NER has focused on formal domains, and NER for social media has been largely restricted to English. We present a new corpus of Weib…
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Deep Active Learning for Named Entity Recognition Open
Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed only…
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Unified Named Entity Recognition as Word-Word Relation Classification Open
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for …
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Adaptive Co-attention Network for Named Entity Recognition in Tweets Open
In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. Previous named entity recognit…
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TaggerOne: joint named entity recognition and normalization with semi-Markov Models Open
Motivation: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing…
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Bidirectional RNN for Medical Event Detection in Electronic Health Records Open
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatic…
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Neural Architectures for Nested NER through Linearization Open
We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In ou…
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Simplify the Usage of Lexicon in Chinese NER Open
Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM has achieved new benchmark results on several public Chinese NER datasets. However,…
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CHEMDNER: The drugs and chemical names extraction challenge Open
Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific …
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Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature Open
The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A…
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TENER: Adapting Transformer Encoder for Named Entity Recognition Open
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer is broadly adopted in various Natural Language Processing (…
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End-to-End Neural Entity Linking Open
Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual…
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Deep Exhaustive Model for Nested Named Entity Recognition Open
We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of …
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Learning Named Entity Tagger using Domain-Specific Dictionary Open
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts o…
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Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning Open
Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings.For languages where word boundaries are not readily identified in text, word segmentation is a ke…