Displaying 1 to 3 from 3 results

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

  •    Python

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

flair - A very simple framework for state-of-the-art NLP

  •    Python

A very simple framework for state-of-the-art NLP. Developed by Zalando Research. A powerful syntactic-semantic tagger / classifier. Flair allows you to apply our state-of-the-art models for named entity recognition (NER), part-of-speech tagging (PoS), frame sense disambiguation, chunking and classification to your text.

RNNSharp - RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on

  •    CSharp

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above version. This page introduces what is RNNSharp, how it works and how to use it. To get the demo package, you can access release page.