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Named Entity Recognition

94 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

State-of-the-art leaderboards

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. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input.

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

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

11 Oct 2018google-research/bert

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.

COMMON SENSE REASONING CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION QUESTION ANSWERING

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). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus.

COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS

Semi-supervised sequence tagging with bidirectional language models

ACL 2017 zalandoresearch/flair

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data.

CHUNKING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

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. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers.

NAMED ENTITY RECOGNITION

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. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering.

ENTITY LINKING NAMED ENTITY RECOGNITION 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. The purpose of NER is to find and classify tokens in text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others.

NAMED ENTITY RECOGNITION WORD EMBEDDINGS

The Natural Language Decathlon: Multitask Learning as Question Answering

ICLR 2019 salesforce/decaNLP

Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.

DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING

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. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF.

NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

EMNLP 2017 Franck-Dernoncourt/NeuroNER

Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems.

NAMED ENTITY RECOGNITION