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primary topic: CWPK #64: Embeddings, Summarization and Entity Recognition
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CWPK #64: Embeddings, Summarization and Entity Recognition
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-11-12T17:47:58Z
content: Some Machine Learning Applied to NLP Problems In the last installment of Cooking with Python and KBpedia we collected roughly 45,000 articles from Wikipedia that match KBpedia reference concepts. We then did some pre-processing of the text using the gensim package to lower case the text, remove stop words, and identify bi-gram and tri-gram phrases. These types of functions and extractions are a strength of gensim, which should be part of your pre-processing arsenal. It is now time for us to process the natural language of this text and to use it for creating word and document embedding models. For the later, we will continue to use gensim. For the former, however, we will introduce and use a very powerful NLP package, spaCy. As noted earlier, spaCy has a clear function orientation to NLP and also has some powerful extension mechanisms. Our plan of attack in this installment is to finish the word embeddings with gensim, and then move on to explore the spaCy package. We will not explore all aspects of this package, but will focus on text summarization, and (named) entity recognition using both models and rule-based. Word and Document Embedding As we have noted in CWPK #61, there exist many pre-calculated word and document embedding models. However, because of the clean scope of KBpedia and our interest in manipulating embeddings for various learning models, we want to create our own embeddings. There are many scopes and methods for creating embeddings. Embeddings, you recall, are a vector representation of information in a reduced dimensional space. Embeddings are a proven way to represent sparse matrix information like language (meaning many dimensions of words and phrases matched to one another) in a more efficient coding format usable by a computer. Embedding scopes may range from words, phrases, sentences, paragraphs, sections of documents, or documents, as well as senses, topics, sentiments, categories or other relations that might cut across a given corpus. Meth…
encoded: Some Machine Learning Applied to NLP Problems In the last installment of Cooking with Python and KBpedia we collected roughly 45,000 articles from Wikipedia that match KBpedia reference concepts. We then did some pre-processing of the text using the gensim package to lower case the text, remove stop words, and identify bi-gram and tri-gram phrases. These types of functions and extractions are a strength of gensim, which should be part of your pre-processing arsenal. It is now time for us to process the natural language of this text and to use it for creating word and document embedding models. For the later, we will continue to use gensim. For the former, however, we will introduce and use a very powerful NLP package, spaCy. As noted earlier, spaCy has a clear function orientation to NLP and also has some powerful extension mechanisms. Our plan of attack in this installment is to finish the word embeddings with gensim, and then move on to explore the spaCy package. We will not explore all aspects of this package, but will focus on text summarization, and (named) entity recognition using both models and rule-based. Word and Document Embedding As we have noted in CWPK #61, there exist many pre-calculated word and document embedding models. However, because of the clean scope of KBpedia and our interest in manipulating embeddings for various learning models, we want to create our own embeddings. There are many scopes and methods for creating embeddings. Embeddings, you recall, are a vector representation of information in a reduced dimensional space. Embeddings are a proven way to represent sparse matrix information like language (meaning many dimensions of words and phrases matched to one another) in a more efficient coding format usable by a computer. Embedding scopes may range from words, phrases, sentences, paragraphs, sections of documents, or documents, as well as senses, topics, sentiments, categories or other relations that might cut across a given corpus. Meth…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: gensim
links to: spaCy
links to: text summarization
links to: (named) entity recognition
links to: CWPK #61
links to: documents
links to: senses
links to: topics
links to: categories
links to: BERT
links to: ELMo
links to: word embedding
links to: document embedding
links to: word2vec
links to: wikipedia-trigram.zip
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
links to: cb1-11
links to: cb1-12
links to: cb1-13
links to: cb1-14
links to: dimension reduction
links to: cb2-1
links to: cb2-2
links to: cb2-3
links to: cb2-4
links to: cb2-5
links to: cb2-6
links to: cb2-7
links to: cb2-8
links to: cb2-9
links to: cb2-10
links to: cb2-11
links to: cb2-12
links to: cb2-13
links to: cb2-14
links to: cb2-15
links to: cb2-16
links to: cb2-17
links to: cb2-18
links to: cb2-19
links to: FastText
links to: cb3-1
links to: cb3-2
links to: cb3-3
links to: cb3-4
links to: cb3-5
links to: cb5-1
links to: cb5-2
links to: cb5-3
links to: cb5-4
links to: cb5-5
links to: cb5-6
links to: cb7-1
links to: cb7-2
links to: cb7-3
links to: cb7-4
links to: cb7-5
links to: cb7-6
links to: NLP utilities

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

documents
label: doc2vec

OntoNotes 5

CWPK #63

one

catastrophic forgetting problem

EntityRuler

rule-based matching

What Are Word Embeddings for Text?

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data

Word Embeddings in Python with Spacy and Gensim

Tutorial: Build your own Skip-gram Embeddings and use them in a Neural Network

Transformers from Scratch

spaCy meets Transformers: Fine-tune BERT, XLNet and GPT-2

What is Word Embedding | Word2Vec | GloVe

Text Generation from Knowledge Graphs with Graph Transformers

GraphWriter

Knowledge Graph Empowered Entity Description Generation

KBpedia

code listing covering the series

direct download

notify me