Named-entity recognition

While zero-shot NER approaches yield impressive outcomes, they operate under the assumption that all entity types are predefined and known. This limitation makes their application impossible in novelty detection, exploration, or knowledge graph construction scenarios. To address this shortcoming, we introduce OWNER, our unsupervised and open-world NER model, which does not need annotations in the target domain (similar to zero-shot) and does not require knowledge of the target entity types or their number.

An AI-powered, but model-agnostic name-entity recognition toolkit.

Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results. - Franck-Dernoncourt/NeuroNER

Build a deep learning model for predicting the named entities from text. - floydhub/named-entity-recognition-template

natural-language-processing hanlp named-entity-recognition dependency-parser part-of-speech-tagger chinese-word-segmentation ... 文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法 · nlp sentiment-analysis unsupervised named-entity-recognition text-summarization dependency-parser keyword-extraction text-segmentation text-cleaning gitee new-word-discovery pyhanlp harvesttext

Unsupervised Corss-lingual model transfer for named entity recognition with contextualized word representations - GitHub - qtxcm/UCT-NER: Unsupervised Corss-lingual model transfer for named entity recognition with contextualized word representations

Pytorch-Named-Entity-Recognition-with-BERT. Contribute to kamalkraj/BERT-NER development by creating an account on GitHub.

Self-supervised NER prototype - updated version (69 entity types - 17 broad entity groups). Uses pretrained BERT models with no fine tuning. State-of-art performance on 3 biomedical datasets - ajitrajasekharan/unsupervised_NER

Named Entity Recognition using multilayered bidirectional LSTM - monikkinom/ner-lstm

Framework to learn Named Entity Recognition models without labelled data using weak supervision. - NorskRegnesentral/weak-supervision-for-NER

In this repo you can find several neural network architectures for named entity recognition from the paper "Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition" https://arxiv.org/pdf/1709.09686.pdf, which is inspired by LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf.

Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation · Introducing NER-UK 2.0: A Rich Corpus of Named Entities for Ukrainian · Entity Embellishment Mitigation in LLMs Output with Noisy Synthetic Dataset for Alignment · Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition...

Build an end to end pipeline for Named Entity Recognition (NER) by a pretrained Huggingface transformer, BERT and deploy to google cloud platform using Docker, CI/CD tool: CircleCI. - malleswarigelli/NameEntityRecoginition

Name Entity Recognition using Python and Keras. Contribute to Akshayc1/named-entity-recognition development by creating an account on GitHub.

This repository keep my research materials about Named Entity Recognition using Transfer Learning. Date · Title · Summary · Pass 0 · Pass 1 · Pass 2 · 2019 · Unsupervised Cross-lingual Representation Learning at Scale · Date · Title · Summary · Pass 0 ·

December 6, 2016 - Now I want to train a Chinese Named Entity model, but most of articles I saw about Named Entity Recognition are talking that it need people to label a tokenized dataset for trainning, just as I see in https://spacy.io/docs/usage/entity-recognition#updating . If spaCy provide a way to suggest the Possible Named Entity Candidates (Such as unsupervised trainning a dataset), then I can pick enough number of correct Named Entity out and use that for further trainning.

Named Entity Recognition (LSTM + CRF) - Tensorflow - guillaumegenthial/sequence_tagging

In this project, we will work with a NER dataset provided in kaggle. This is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens.

Named-entity recognition (NER) is a task of NLP that seeks to locate and classify named entity mentioned in unstructured text. In this repository, I do a quick overview of supervised and unsupervised methods for this task.