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NER

The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...

Papers

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Showing 1376–1400 of 1729 papers

TitleStatusHype
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsCode0
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified FrameworkCode0
Towards De-identification of Legal Texts—0
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019Code0
Why Attention? Analyzing and Remedying BiLSTM Deficiency in Modeling Cross-Context for NER—0
Named Entity Recognition -- Is there a glass ceiling?Code0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data—0
Recent Advances in End-to-End Spoken Language Understanding—0
Gated Task Interaction Framework for Multi-task Sequence TaggingCode0
Language-Agnostic Syllabification with Neural Sequence LabelingCode0
Named Entity Recognition System for Sindhi Language—0
Classification Attention for Chinese NER—0
Towards Interpretable Evaluations: A Case Study of Named Entity Recognition—0
Automatic Identification and Normalisation of Physical Measurements in Scientific LiteratureCode0
Portuguese Named Entity Recognition using BERT-CRFCode0
Using Chinese Glyphs for Named Entity RecognitionCode0
Named Entity Recognition with Partially Annotated Training Data—0
Neural Correction Model for Open-Domain Named Entity RecognitionCode0
From English to Code-Switching: Transfer Learning with Strong Morphological CluesCode0
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis—0
Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER—0
CrossWeigh: Training Named Entity Tagger from Imperfect AnnotationsCode0
All Roads Lead to UD: Converting Stanford and Penn Parses to English Universal Dependencies with Multilayer Annotations—0
Improving NER Models by exploiting Named Entity Gazetteer as External Knowledge—0
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