Word2vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.

Word2Vec

GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

Glove

https://en.wikipedia.org/wiki/Latent_semantic_analysis

Latent Semantic Analysis(LSA)-Reference

LSA is a well-known and widely used language model in natural language processing area.
LSA assumes that words that are close in meaning will occur in similar pieces of text (the distributional hypothesis). A matrix containing word counts per document (rows represent unique words and columns represent each document) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by taking the cosine of the angle between the two vectors (or the dot product between the normalizations of the two vectors) formed by any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.
It's the basic method of Glove and many other modern methods of word embedding.

Latent Semantic Analysis

The key idea of noise constractive estimation is to learn the feature of data distribution by learning from a dichotomy problem of selecting the data points from the original dataset from a mixed dataset made up of noises and the original datasets.

Noise Contractive Estimation

Ranking loss focuses more on relative distance instead of measuring the direct difference between the output with the expectation. 

Ranking Loss

Ensemble learning is a simple and effective technique for building a strong predictive model by combining the outputs of multiple, often weaker, individual models. This method aggregates the predictions from these constituent models to achieve a more robust and superior performance than any single model could attain on its own.

Ensemble Learning

BERT (Bidirectional Encoder Representations from Transformers) stands out as one of the most popular and extensively used pre-trained sequence encoding models in the field of Natural Language Processing. As a foundational model, it is trained using a self-supervised approach that combines two tasks: masked language modeling (MLM) and next sentence prediction (NSP). In MLM, the model predicts randomly masked words from their context, enabling it to learn deep bidirectional language representations. This dual-task training makes BERT a versatile foundation model adaptable to a wide array of NLP applications.

BERT (Bidirectional Encoder Representations from Transformers)

ELMo is a context-sensitive word embedding model designed to address the polysemy problem. It utilizes a CNN to transfer words into raw vectors, followed by a bidirectional LSTM to calculate a weighted average of the hidden layers, yielding the final word representations. Notably, when ELMo is applied to downstream natural language processing tasks, it requires crafting task-specific architectures and freezes the parameters of its pretrained model during supervised learning.

ELMo

1. LSA based method.
This kind of method is based on the intuition of SVD similar way. They try to do SVD on term-document matrices so that get the representation vectors.
2. local context based method.
This kind of method try to use the information gotten from the local context.
e.g.Word2Vec,Glove(Glove is actually a combination of those two methods.)

University of Michigan - Ann Arbor

Instead of one-hot encoding vector, we use featured representation to capture relationship between words.

In simple words, what are extracted features for each word? For example, in the following matrix "Man" and "Woman" have high score for "Gender" feature and low score for "Food" feature. As a result, the computer can capture their relation.



Featured representation: Word Embedding

https://www.coursera.org/learn/nlp-sequence-models

Sequence Models

1- Learn word embeddings from large text corpus.

2- Transfer embedding to new task with smaller training set.

3- Optional: Continue to fine tune the word embedding with new data.

Transfer Learning and Word Embedding

When there are many data in the learning step, and less data in the new task, such as:

1- Name entity recognition

2- Text summarization



But not:
Language Modeling, Machine Translation,...


Word embedding Applications

Algorithms used in word embedding

When learning word embeddings, we create an artificial task of estimating 
P(target/context). It is okay if we do poorly on this artificial prediction task; the more important by-product of this task is that we learn a useful set of word embeddings. 

 word embeddings

In a simple word,  word embeddings are numerical representations of texts.
In detail, when computer treat differents words, they will turn them into some atomic symbols. e.g. cat might be a, dogs might be b. But how can computer find the relationship between them? That's what word embeddings are. They willl build some relationships between words.

Word Embeddings

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique used to visualize high-dimensional data. In the context of word embeddings, it maps high-dimensional feature vectors into a 2D or 3D space, ensuring that semantically similar words are positioned in close proximity for visualization.

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