Word2vec algorithm

Oct 1, 201426 likes12,347 views

Word2vec works by using documents to train a neural network model to learn word vectors that encode the words' semantic meanings. It trains the model to predict a word's context by learning vector representations of words. It then represents sentences as the average of the word vectors, and constructs a similarity matrix between sentences to score them using PageRank to identify important summary sentences.

How Does word2vec Work?
Andrew Koo - Insight Data Science

word2vec (Google, 2013)
? Use documents to train a neural network model
maximizing the conditional probability of context given the
word
? Apply the trained model to each word to get its
corresponding vector
? Calculate the vector of sentences by averaging the vector
of their words
? Construct the similarity matrix between sentences
? Use Pagerank to score the sentences in graph

1. Use documents to train a neural
network model maximizing the conditional
probability of context given the word
The goal is to optimize the parameters (��) maximizing the
conditional probability of context (c) given the word (w). D is the set
of all (w, c) pairs
For example: I ate a ��????�� at McDonald last night is more likely
given Big Mac

2. Apply the model to each word
to get its corresponding vector
word vector
(0.12, 0.23, 0.56)
(0.24, 0.65, 0.72)
(0.38, 0.42, 0.12)
(0.57, 0.01, 0.02)
(0.53, 0.68, 0.91)
(0.11, 0.27, 0.45)
(0.01, 0.05, 0.62)
The
Cardinals
will
win
the
world
series

3. Calculate the vector of sentences
by averaging the vector of their words
word vector
(0.12, 0.23, 0.56)
(0.24, 0.65, 0.72)
(0.38, 0.42, 0.12)
(0.57, 0.01, 0.02)
(0.53, 0.68, 0.91)
(0.11, 0.27, 0.45)
(0.01, 0.05, 0.62)
The
Cardinals
will
win
the
world
series
sentence vector
(0.28, 0.33, 0.49)

4. Construct the similarity
matrix between sentences
1
0.366
0.243
0.564
0.720
Sentence Vector
S��1
S��2
S��3
S��4
S��5
0.366
1
0.623
0.132
0.189
0.243
0.623
1
0.014
0.523
0.564
0.132
0.014
1
0.002
matrix * matrix.T similarity matrix
0.720
0.189
0.523
0.002
1

5. Use Pagerank to score the
sentences in graph
? Rank the sentences
with underlying
assumption that
��summary sentences��
are similar to most
other sentences

This document provides an overview of Word2Vec, a neural network model for learning word embeddings developed by researchers led by Tomas Mikolov at Google in 2013. It describes the goal of reconstructing word contexts, different word embedding techniques like one-hot vectors, and the two main Word2Vec models - Continuous Bag of Words (CBOW) and Skip-Gram. These models map words to vectors in a neural network and are trained to predict words from contexts or predict contexts from words. The document also discusses Word2Vec parameters, implementations, and other applications that build upon its approach to word embeddings.

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Monthly AI Tech Talks in Toronto 2019-08-28 https://www.meetup.com/aittg-toronto The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models. In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models. The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python. https://www.meetup.com/aittg-toronto/events/261940480/

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Word2Vec is a model that learns vector representations of words from large amounts of text. It represents words in a continuous vector space where semantically similar words are located close to each other. The model is trained using a simple neural network to predict words from context. Word2Vec has been shown to produce word embeddings that exhibit linguistic regularities and can be used as features for various natural language processing tasks. It has efficient implementations in libraries like Gensim that make it widely used.

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The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.

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Word2 vecankit_ppt

This document provides an overview of Word2Vec, a model for generating word embeddings. It explains that Word2Vec uses a neural network to learn vector representations of words from large amounts of text such that words with similar meanings are located close to each other in the vector space. The document outlines how Word2Vec is trained using either the Continuous Bag-of-Words or Skip-gram architectures on sequences of words from text corpora. It also discusses how the trained Word2Vec model can be used for tasks like word similarity, analogy completion, and document classification. Finally, it provides a Python example of loading a pre-trained Word2Vec model and using it to find word vectors, similarities, analogies and outlier words.

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Word2vec algorithm

1. How Does word2vec Work? Andrew Koo - Insight Data Science

2. word2vec (Google, 2013) ? Use documents to train a neural network model maximizing the conditional probability of context given the word ? Apply the trained model to each word to get its corresponding vector ? Calculate the vector of sentences by averaging the vector of their words ? Construct the similarity matrix between sentences ? Use Pagerank to score the sentences in graph

3. 1. Use documents to train a neural network model maximizing the conditional probability of context given the word The goal is to optimize the parameters (��) maximizing the conditional probability of context (c) given the word (w). D is the set of all (w, c) pairs For example: I ate a ��????�� at McDonald last night is more likely given Big Mac

4. 2. Apply the model to each word to get its corresponding vector word vector (0.12, 0.23, 0.56) (0.24, 0.65, 0.72) (0.38, 0.42, 0.12) (0.57, 0.01, 0.02) (0.53, 0.68, 0.91) (0.11, 0.27, 0.45) (0.01, 0.05, 0.62) The Cardinals will win the world series

5. 3. Calculate the vector of sentences by averaging the vector of their words word vector (0.12, 0.23, 0.56) (0.24, 0.65, 0.72) (0.38, 0.42, 0.12) (0.57, 0.01, 0.02) (0.53, 0.68, 0.91) (0.11, 0.27, 0.45) (0.01, 0.05, 0.62) The Cardinals will win the world series sentence vector (0.28, 0.33, 0.49)

6. 4. Construct the similarity matrix between sentences 1 0.366 0.243 0.564 0.720 Sentence Vector S��1 S��2 S��3 S��4 S��5 0.366 1 0.623 0.132 0.189 0.243 0.623 1 0.014 0.523 0.564 0.132 0.014 1 0.002 matrix * matrix.T similarity matrix 0.720 0.189 0.523 0.002 1

7. 5. Use Pagerank to score the sentences in graph ? Rank the sentences with underlying assumption that ��summary sentences�� are similar to most other sentences

�ݺ�ߣ

Word2vec algorithm

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