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Named Entity Recognition (NER)

106 papers with code · Natural Language Processing

Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

Mark Watney visited Mars
B-PER I-PER O B-LOC

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Greatest papers with code

Semi-Supervised Sequence Modeling with Cross-View Training

EMNLP 2018 tensorflow/models

We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.

CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION (NER) UNSUPERVISED REPRESENTATION LEARNING

Deep contextualized word representations

HLT 2018 zalandoresearch/flair

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

CITATION INTENT CLASSIFICATION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION (NER) NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS

Neural Architectures for Named Entity Recognition

HLT 2016 zalandoresearch/flair

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.

NAMED ENTITY RECOGNITION (NER)

Named Entity Recognition with Bidirectional LSTM-CNNs

TACL 2016 zalandoresearch/flair

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.

ENTITY LINKING FEATURE ENGINEERING NAMED ENTITY RECOGNITION (NER) WORD EMBEDDINGS

Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition

27 Sep 2017deepmipt/DeepPavlov

Named Entity Recognition (NER) is one of the most common tasks of the natural language processing.

NAMED ENTITY RECOGNITION (NER) WORD EMBEDDINGS

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

ACL 2016 guillaumegenthial/sequence_tagging

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.

FEATURE ENGINEERING NAMED ENTITY RECOGNITION (NER) PART-OF-SPEECH TAGGING