Displaying 1 to 20 from 32 results

snips-nlu - Snips Python library to extract meaning from text

  •    Python

Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.




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.

CoreNLP - Stanford CoreNLP: A Java suite of core NLP tools.

  •    Java

Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

Stanza - A Python NLP Library for Many Human Languages

  •    Python

Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.


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.

spark-nlp - Natural Language Understanding Library for Apache Spark.

  •    Jupyter

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

spacy-streamlit - 👑 spaCy building blocks and visualizers for Streamlit apps

  •    Python

This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more. The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

pymetamap - Python wraper for MetaMap

  •    Python

Python wrapper around MetaMap. This will take a list of sentences and extract concepts using MetaMap then return them in the form of a list of Concept objects. Note: This code does not work with Windows because of my use of NamedTemporaryFile in SubprocessBackend.py.

wikipedia_ner - :book: Labeled examples from wiki dumps in Python

  •    Jupyter

Tool to train and obtain named entity recognition labeled examples from Wikipedia dumps. Usage in IPython notebook (nbviewer link).

europeananp-ner - Named Entities Recognition Annotator Tool for Europeana Newspapers

  •    Java

This tool takes container documents (MPEG21-DIDL, METS), parses all references to ALTO files and tries to find named entities in the pages (with most models: Location, Person, Organisation, Misc). The aim is to keep the physical location on the page available through the whole process to be able to highlight the results in a viewer. Read more about it on the KBNLresearch blog.

ner - Named Entity Recognition

  •    Python

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. NER class from ner/network.py provides methods for construction, training and inference neural networks for Named Entity Recognition.

horus-ner - HORUS: A framework to boost NLP tasks

  •    Python

HORUS is meta and multi-level framework designed to provide a set of features at word-level to boost natural language frameworks. It's architecure is based on image processing and text classification clustering algorithms and shows to be helpful especially to noisy data, such as microblogs. We are currently investigating Named Entity Recognition (NER) as use case. This version supports the identification of classical named-entity types (LOC, PER, ORG).






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