Developer friendly Natural Language Processing ✨
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Updated
Jun 30, 2025 - JavaScript
Developer friendly Natural Language Processing ✨
基于 TensorFlow & PaddlePaddle 的通用序列标注算法库(目前包含 BiLSTM+CRF, Stacked-BiLSTM+CRF 和 IDCNN+CRF,更多算法正在持续添加中)实现中文分词(Tokenizer / segmentation)、词性标注(Part Of Speech, POS)和命名实体识别(Named Entity Recognition, NER)等序列标注任务。
Extract city and country mentions from Text like GeoText without regex, but FlashText, a Aho-Corasick implementation.
Data for the HIPE 2022 shared task.
Tool for slot extraction from text
Contains jupyter notebooks, presentations and examples for Keras, Google AI Platform and Kubeflow.
Contains a Keras Bi-LSTM for Named Entity Recognition (This example demonstrates how you can use Kubeflow to train and deploy a Keras model with a custom prediction routine).
Project to extract entities from Job Description Articles.
Making a custom entity extraction model using spacy 3.5 using both conventional and transformer background. I will also try the spancat pipepline along with the ner.
Sample Recruitment Bot for Oracle Digital Assistant
A node binding for the MIT Information Extraction library.
NLP classification models built using intimate partner violence news articles
Successfully developed a Named Entity Recognition (NER) model on the BC5CDR dataset using Stacked Bidirectional GRUs with Attention mechanism, designed to accurately identify chemical and disease entities from biomedical texts.
Successfully developed a Named Entity Recognition (NER) model using a Bidirectional GRU with Attention on the MIT Movies dataset to identify and classify movie-related entities like titles, actors, and genres.
Documentation for Tisane
The tool for converting Edo era's Japanese business records (titles) into named entities. For this purpose, you need to make a user dictionary of Mecab.
Master Thesis Code
Successfully developed a Named Entity Recognition (NER) model for German text using a Bidirectional LSTM with Attention on the Multilingual NER dataset, effectively identifying entities across multilingual corpora with contextual understanding.
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