Displaying 1 to 20 from 30 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.

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 .

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).

frog - Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch

  •    C++

Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. All NLP modules are based on Timbl, the Tilburg memory-based learning software package. Most modules were created in the 1990s at the ILK Research Group (Tilburg University, the Netherlands) and the CLiPS Research Centre (University of Antwerp, Belgium). Over the years they have been integrated into a single text processing tool, which is currently maintained and developed by the Language Machines Research Group and the Centre for Language and Speech Technology at Radboud University Nijmegen. A dependency parser, a base phrase chunker, and a named-entity recognizer module were added more recently. Where possible, Frog makes use of multi-processor support to run subtasks in parallel. Various (re)programming rounds have been made possible through funding by NWO, the Netherlands Organisation for Scientific Research, particularly under the CGN project, the IMIX programme, the Implicit Linguistics project, the CLARIN-NL programme and the CLARIAH programme.

spacy-lookup - Named Entity Recognition based on dictionaries

  •    Python

spaCy v2.0 extension and pipeline component for adding Named Entities metadata to Doc objects. Detects Named Entities using dictionaries. The extension sets the custom Doc, Token and Span attributes ._.is_entity, ._.entity_type, ._.has_entities and ._.entities. Named Entities are matched using the python module flashtext, and looks up in the data provided by different dictionaries.

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