SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #63: Staging Data Sci Resources and Preprocessing
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CWPK #63: Staging Data Sci Resources and Preprocessing
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-11-10T05:23:22Z
content: Clean Corpora and Datasets are a Major Part of the Effort With our discussions of network analysis and knowledge extractions from our knowledge graph now behind us, we are ready to tackle the questions of analytic applications and machine learning in earnest for our Cooking with Python and KBpedia series. We will be devoting our next nine installments to this area. We devote two installments to data sources and input preparations, largely based on NLP (natural language processing) applications. Then we devote two installments to 'standard' machine learning (largely) using the scikit-learn packages. We next devote four installments to deep learning, split equally between the Deep Learning Graph (DGL) and PyTorch Geometric (PyG) frameworks. We conclude this Part VI with a summary and comparison of results across these installments based on the task of node classification. In this particular installment we flesh out the plan for completing these installments and discuss data sources and completing data prep needed for the plan. We provide particular attention to the architecture and data flows within the PyTorch framework. We describe the additional Python packages we need for this work, and install and configure the first ones. We discuss general sources of data and corpora useful for machine learning purposes. Our coding efforts in this installment will obtain and clean the Wikipedia pages that supplement the two structural and annotation sources based on KBpedia that were covered in the prior installment. These three sources of structure, annotations and pages are the input basis to creating our own embeddings to be used in many of the machine learning tests. Plan for Completion of Part VI The broad ecosystem of Python packages I was considering looked, generally, to be good choices to work together, as first outlined in CWPK #61. I had done an adequate initial diligence. But, how all of this was to unfold, what my plan of attack should be, became driving factors I …
encoded: Clean Corpora and Datasets are a Major Part of the Effort With our discussions of network analysis and knowledge extractions from our knowledge graph now behind us, we are ready to tackle the questions of analytic applications and machine learning in earnest for our Cooking with Python and KBpedia series. We will be devoting our next nine installments to this area. We devote two installments to data sources and input preparations, largely based on NLP (natural language processing) applications. Then we devote two installments to ‘standard’ machine learning (largely) using the scikit-learn packages. We next devote four installments to deep learning, split equally between the Deep Learning Graph (DGL) and PyTorch Geometric (PyG) frameworks. We conclude this Part VI with a summary and comparison of results across these installments based on the task of node classification. In this particular installment we flesh out the plan for completing these installments and discuss data sources and completing data prep needed for the plan. We provide particular attention to the architecture and data flows within the PyTorch framework. We describe the additional Python packages we need for this work, and install and configure the first ones. We discuss general sources of data and corpora useful for machine learning purposes. Our coding efforts in this installment will obtain and clean the Wikipedia pages that supplement the two structural and annotation sources based on KBpedia that were covered in the prior installment. These three sources of structure, annotations and pages are the input basis to creating our own embeddings to be used in many of the machine learning tests. Plan for Completion of Part VI The broad ecosystem of Python packages I was considering looked, generally, to be good choices to work together, as first outlined in CWPK #61. I had done an adequate initial diligence. But, how all of this was to unfold, what my plan of attack should be, became driving factors I …
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
has reply: comment-507804
has reply: comment-507807
links to: network analysis
links to: knowledge graph
links to: machine learning
links to: Cooking with Python and KBpedia
links to: NLP
links to: scikit-learn
links to: deep learning
links to: DGL
links to: PyG
links to: PyTorch
links to: CWPK #61
links to: Clojure
links to: KBpedia Web site
links to: owlready2
links to: Protégé
links to: RDFLib
links to: SPARQL
links to: NetworkX
links to: pandas
links to: CSV
links to: PyCaret
links to: PiePline
links to: Catalyst
links to: Poutyne
links to: torchtext
links to: Spotlight
links to: corpora
links to: computational linguistics
links to: pre-trained models
links to: Transformers
links to: BERT
links to: CBOW
links to: skip-gram
links to: GloVe
links to: word2vec
links to: fastText
links to: gensim
links to: spaCy
links to: HuggingFace datasets
links to: English Wikipedia dump
links to: Wikipedia2Vec pre-trained embeddings
links to: gensim datasets
links to: word2vec pre-trained models
links to: gensim datasets and models
links to: 11 pre-trained word embedding models
links to: Word vectors for 157 languages
links to: DBpedia entity typing + word embeddings
links to: CWPK #58
links to: GPUs
links to: Nvidia
links to: CUDA
links to: locally
links to: installation
links to: https://dumps.wikimedia.org/enwiki/20200920/
links to: downloads-2016-10
links to: Special:Export
links to: cb1-1
links to: cb1-2
links to: cb1-3
links to: cb1-4
links to: cb1-5
links to: cb1-6
links to: cb1-7
links to: cb1-8
links to: cb1-9
links to: cb1-10

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

PyG
label: install

GloVe
label: GloVe embeddings

gensim
label: installation

XML parsing and specific code
label: dedicated Wikipedia extractor

Wikipedia services
label: Corpus from a Wikipedia Dump

Building a Wikipedia Text Corpus for Natural Language Processing

kbpedia.org/cwpk-text/Wikipedia-pages-1.zip

find one

stop words

n-grams

Transformers: State-of-the-art Natural Language Processing

Convert Pandas Dataframe to PyTorch Tensor

PyTorch Dataset: Reading Data Using Pandas vs. NumPy

CWPK #56

The Most Complete Guide to PyTorch for Data Scientists

Awesome-Pytorch-list

PyTorch documentation

Building Efficient Custom Datasets in PyTorch

Getting Started with PyTorch: A Deep Learning Tutorial

Incredible PyTorch

Learning PyTorch with Examples

Natural Language in Python using spaCy: An Introduction

Gensim Tutorial – A Complete Beginners Guide

KBpedia

code listing covering the series

direct download

notify me