Notes:
Word embedding is a technique in natural language processing that represents words or phrases in a high-dimensional, continuous vector space. It maps each word or phrase to a fixed-length vector of real numbers, where the vector components capture the semantic meaning of the word or phrase. For example, words and phrases that are semantically similar, such as “cat” and “kitten” or “king” and “queen,” would have similar or close vector representations in the word embedding space.
Word embedding is used for a variety of natural language processing tasks, such as language modeling, text classification, and machine translation. It allows machine learning algorithms to work with words and phrases in a continuous, numerical representation, rather than as discrete, symbolic tokens. This can improve the performance and accuracy of natural language processing systems, by providing a more intuitive and expressive representation of the meaning of words and phrases.
Word embedding is typically learned from large amounts of text data using unsupervised learning algorithms, such as word2vec and GloVe. These algorithms analyze the co-occurrence of words in the training data to learn the relationships between words and their meanings, and to generate the vector representations of words in the embedding space. Once the word embedding has been learned, it can be used as a lookup table to map words and phrases to their corresponding vector representations, and can also be fine-tuned or adjusted for specific tasks or domains.
- Distributional semantic model is a type of computational model that is used to represent the meanings of words and phrases in a language. It is based on the idea that the meaning of a word can be inferred from the context in which it is used, and it typically involves representing words as vectors in a high-dimensional space.
- Distributed representation refers to the idea that the meaning of a word or concept can be represented by a distributed pattern of activation across a large number of neurons or dimensions in a neural network or other computational model. This is in contrast to a localist representation, where each word or concept is represented by a single neuron or dimension.
- Neural word embedding is a technique for representing words as vectors in a high-dimensional space, typically using a neural network. The vectors are learned in a way that reflects the relationships between words in the language, and they can be used as input to other machine learning models to perform tasks such as language translation or text classification.
- Semantic vector space is a high-dimensional space in which words or phrases are represented as vectors, with the vectors reflecting the meanings of the words or phrases. Semantic vector spaces are often used in natural language processing (NLP) to represent the meanings of words in a way that can be understood by computational models.
- Vector space model is a mathematical model that represents the relationships between a set of vectors in a high-dimensional space. Vector space models are often used in natural language processing (NLP) to represent the meanings of words and phrases, and to compute similarities between them.
- Word vectors are numerical representations of words that are used to encode the meanings of words in a high-dimensional space. Word vectors are often learned using techniques such as neural word embedding, and they can be used as input to machine learning models to perform tasks such as language translation or text classification.
Resources:
- eigenwords .. eigenword resource page
- gensim .. easy to build prototypes with various models
- glove .. an unsupervised learning algorithm for obtaining vector representations for words
- tensorflow word2vec tutorial .. used for learning vector representations of words, called “word embeddings”
- word2vec .. an implementation of the continuous bag-of-words (cbow) and the skip-gram model (sg)
- word2vec in java .. neural word embeddings in java and scala
- wordvectors.org .. you can upload the filtered vectors
Wikipedia:
See also:
100 Best Word2vec Videos | Word2vec & Dialog Systems 2016
- Webinar on Word Embeddings | HackerEarth
- Data Mining Lab – Word Embeddings (Part 2)
- Data Mining Lab – Word Embeddings (Part 1)
- Word Sense Disambiguation on Linguistic Regularity and Classification Accuracy of Word Embeddings
- Use of word embedding algorithms and siamese recurrent neural networks
- Datageeks Data Day 2017 – Fabian Dill – Word Embeddings – Part 1 of 2
- Datageeks Data Day 2017 – Fabian Dill – Word Embeddings – Part 2 of 2
- Word Embeddings II
- ADLxMLDS Lecture 4: Word Embeddings (17/10/16 + 17/10/23)
- Debiasing Word Embeddings
- WORD EMBEDDINGS
- ADLxMLDS Lecture 4: Word Embeddings (17/10/19)
- Word Embeddings I
- Collaboratively Improving Topic Discovery and Word Embeddings
- Meetup #30 – Piero Molino: Word Embeddings – Past, Present and Future
- Big Data and Info Retrieval Lecture 5 (171012) – word embedding vis, RNNs
- Word Embedding Models (Lecture 7; October 4, 2017)
- Big Data and Info Retrieval Lecture 4 (170928) – word embedding
- Big Data and Info Retrieval Lecture 3 (170921) – topic modeling, word embedding
- Domain Specific Word Embedding for Cybersecurity Text by Roy Arpita
- Learning to Compute Word Embeddings On the Fly by Dzmitry Bahdanau
- Marco Bonzanini – Word Embeddings for Natural Language Processing in Python
- Word Embeddings for Natural Language Processing in Python – MARCO BONZANINI
- Dynamic Word Embeddings
- Lev Konstantinovskiy – Text similiarity with the next generation of word embeddings in Gensim
- Word Embeddings
- A tool kit for query the document, word embedding
- Marco Bonzanini – Word Embeddings for Natural Language Processing in Python
- Collaboratively Improving Topic Discovery and Word Embeddings
- June 20: practical AI workshop – Rachel Thomas, word embeddings and data biases
- Machine Learning Project 4: Gender Bias in Word Embeddings
- Word Embeddings, Bias in ML, Why You Don’t Like Math, & Why AI Needs You
- Learn how to say this word: “Embedding”
- ConceptVector: Building User-Driven Concepts via Word Embedding
- Piero Molino- Word Embeddings: History, Present and Future AIWTB 2017
- Lev Konstantinovskiy – Next generation of word embeddings in Gensim
- Text Similarity Based on Word Embeddings, Syntax Trees
- What is WORD EMBEDDING? What does WORD EMBEDDING mean? WORD EMBEDDING meaning & explanation
- Lev Konstaninovskiy – Word Embedding For Fun and Profit
- Word-Embedding-Enhanced Alexa Skill on an Echo – Demo
- Representations for Language: From Word Embeddings to Sentence Meanings
- D2L4 Word Embeddings – Word2Vec (by Antonio Bonafonte)
- Word Embeddings applied to Nao’s speech
- Acoustic word embeddings
- Introdução aos Word Embeddings
- contextual word embeddings for recomendor systems by Akhil Gupta 36:18
- 20161221 neural network word embedding backward
- 20161220 neural network onehot word embedding
- ML Lecture 14: Unsupervised Learning – Word Embedding
- James Thorne – A Convolution Kernel for Sentiment Analysis using Word-Embeddings
- Document and Word Embeddings for Text Mining
- Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
- Actionable and Political Text Classification Using Word Embeddings and LSTM
- Decoding Fashion Contexts Using Word Embeddings
- Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings
- Word embedding in Spanish at Universidad Nacional Colombia 2016
- Martin Jaggi – Deep Learning for Text – From Word Embeddings to Convolutional Neural Networks
- Introduction to Word Embeddings
- Lev Konstantinovskiy – Word Embeddings for fun and profit in Gensim
- Word Embedding Explained and Visualized – word2vec and wevi
- Revisiting Word Embedding for Contrasting Meaning
- Specializing Word Embeddings for Similarity or Relatedness
- Francois Scharffe: Word embeddings as a service
- Dependency-based word embeddings
- Word: Embedding a Video
- BDSBTB 2015: Marek Kolodziej, Unsupervised NLP Using Word Embeddings, Scala and Apache Spark
- Word Embedding: from theory to practice
- Stockholm NLP Meetup: Word Embedding from Theory to Practice
- Excel Tutorial: Excel Word embedding | ExcelCentral.com
- Microsoft Word: Embedding files in your document. AOTraining.net