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.

Sample Recruitment Bot for Oracle Digital Assistant

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.

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 using a Bidirectional GRU with Attention on the MIT Movies dataset to identify and classify movie-related entities like titles, actors, and genres.

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.

How to build a baby-BERT : I analyze BiLSTMs combined with Conditional Random Fields for Named Entity Recognition & contrasts a Neural-CRF tagger against a baseline BiLSTM model, exploring how probabilistic sequence dependencies improve contextual understanding beyond token-level classification.

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.

Documentation for Tisane

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.