(2013) Word Embeddings and Learning Word Embeddings Quiz

Word embeddings = Produced by training neural networks Continuous bag-of-words model = Training a classifier to predict a word from its context Continuous skip-gram model = Training a classifier to predict context from a given word Skip-gram model = Used for binary classification to determine if words are contextually related

Neural networks = Have weight matrices that they 'learn' Perceptron = Similar to neural networks in weight learning Indicator vector = Represents a word in the context of word embeddings Weight matrix = Used to calculate word embeddings for specific words

Continuous bag-of-words model = Predicting a word from its context Continuous skip-gram model = Predicting context from a given word Word2vec = Implementation by Google Skip-gram model = Binary classification for context word similarity

Learning word embeddings = Training neural networks on textual data Semantic similarity = High probability of 'milk' being a real context word of 'cheese' Binary classification = Determining if 'robot' is a real context word of 'cheese' Goat milk cheese = 'Garrotxa' example used in training models

Word embeddings = Vector representation of words in a continuous vector space Skip-gram model = Learning embeddings by predicting context words given a target word Negative sampling = Technique for generating negative examples in word embedding training Logistic function = Function used to map dot product values to a probability range [0, 1]

Initialize word vectors = Starting step where word vectors are set with random values Compute probabilities for examples = Step involving calculating probabilities for positive and negative examples Apply learning algorithm = Process of updating word vectors based on computed probabilities Repeat steps 2 & 3 = Iterative process to refine word embeddings over multiple iterations

wicked = Negative example probability mattress = Negative example probability goat = Positive example probability doubts = Negative example probability

Generate negative examples = Step involving randomly sampling words from the entire vocabulary Predict context words = Step where embeddings are learned by predicting context words given a target word Update word vectors = Process of applying a learning algorithm to update the word vectors Set random values = Initial step of setting all word vectors with random values

robot = Positive example in training data metal = Negative example generated through negative sampling packages = Negative example generated through negative sampling milk = Positive example in training data

Calculate probabilities for examples = Step 2 in learning embeddings with skip-gram model Apply stochastic gradient descent (SGD) = Common algorithm used to update word vectors based on calculated probabilities Randomly sample words from entire vocabulary = Technique used to generate negative examples in training data Refine word embeddings iteratively = Process of repeating steps 2 & 3 multiple times for improving embeddings

Step 1: Initialize vectors with random values = Starting point for learning word embeddings Step 2: Compute probability of a positive example = Calculating the likelihood of a specific word combination Step 3: Update the vectors so that their dot product increases = Adjusting vectors to improve performance Outlook & Wrapping Up: Using word embeddings for classifiers = Applying word embeddings in machine learning tasks

Word embeddings = Replacing feature vectors in sequence labeling Vector representation = Mapping words to embeddings in neural networks Natural language processing = Using embeddings to represent words in classification tasks Deep learning = Utilizing embeddings to enhance performance in machine learning models

Replacing bag-of-words in classification = Improving feature representation Representing each word by its embedding = Enhancing model understanding of semantic relationships Averaging the embeddings of all words in a sentence = Capturing overall context in the embedding space Mapping words to embeddings as the first step in neural networks = Enabling efficient information processing

𝑃(+ | milk, cheese) = 𝜎 ( -1.71 0.36 -0.50 ... ⋅ -0.63 0.99 ... ) ≈ 𝜎 (−0.73) ≈ 0.33 = Calculating the probability of a positive example Learning embeddings with the skip-gram model = Training a model to generate word representations Initializing vectors with random values = Setting the starting points for vector optimization Updating vectors so that their dot product increases = Enhancing vector relationships for better performance

Using word embeddings for classifiers = Improving classification accuracy Replacing bag-of-words in classification with embeddings = Enhancing text representation quality Mapping words to embeddings in neural networks = Enabling efficient information processing Applying embeddings in machine learning tasks = Enhancing model understanding of semantic meanings

Word embeddings can replace feature vectors in sequence labeling = Enhancing the performance of sequence labeling models Word embeddings replacing bag-of-words in classification = Improving classification accuracy and semantic understanding Averaging the embeddings of all words in a sentence = Capturing overall context and meaning of the sentence Mapping words to embeddings as the first step in any neural network model = Ensuring efficient information representation for neural networks