Summary of Прикладное машинное обучение 1. Intro to NLP. Word embeddings

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00:00:00 - 01:00:00

In this video, the speaker introduces the topic of natural language processing (NLP) and its applications, such as sentiment analysis and fake news detection. They discuss the importance of representing text data in a machine-readable format and how word embeddings have allowed NLP to advance. The speaker also talks about the challenges of word normalization and introduces various techniques for text preprocessing. They explain the concept of word embeddings, which are vector representations of words, and their role in capturing context and meaning. The video also explores different methods of building a knowledge base for NLP and the challenges associated with language evolution. Lastly, the speaker discusses the use of word embeddings in matrix factorization, the role of context in word meaning, and techniques to accelerate model training.

00:00:00 In this section, the speaker introduces the topic of natural language processing (NLP) and its applications. They discuss the different tasks that can be solved using NLP methods, such as sentiment analysis, spam filtering, topic prediction, fake news detection, and graph analysis. The speaker also mentions the importance of representing text data in a machine-readable format, and how word embeddings have allowed NLP to advance to new heights. They highlight the relevance and achievements of deep learning in NLP, particularly in the context of word vector representations.
00:05:00 In this section, the speaker introduces the concept of labeled data and different types of labels that can be used in natural language processing tasks. He explains that labels can be binary (e.g., positive/negative sentiment), multi-class (e.g., categorizing news articles into different topics), or continuous (e.g., predicting salaries based on text descriptions). The speaker also discusses the process of feature extraction and representation of text data, emphasizing the challenge of extracting meaning from text and the need for effective techniques in representing text data. He suggests dividing text into tokens (words or characters) as a basic unit for working with text data.
00:10:00 In this section, the speaker discusses the concept of word embeddings and how they can be used to represent text. The basic idea is to assign a unique index to each word in a dictionary and then map each word to a vector, such as a one-hot vector. These vectors can then be combined to represent a text as a set of these vectors. However, this approach has limitations, as it assumes that words are independent of each other and ignores the sequential nature of text. Additionally, the size of the vector representing the text can become very large if the dictionary has many unique words. The speaker suggests that normalizing the text and reducing the size of the dictionary can help address these issues.
00:15:00 In this section, the speaker discusses the challenges of bringing words to their base form in natural language processing (NLP). He explains that languages like Russian have a large number of word forms and inflections, making it difficult to standardize words. One approach is to strip the word of suffixes and other parts using strict rules, but this method is not very effective. Another approach is to store a dictionary of words and their corresponding base forms, but this approach is limited to existing words and cannot handle new words or typos. The speaker mentions different methods like Porter's stemming algorithm and Lancaster stemming, which are popular options for text preprocessing. He also introduces the concept of word embeddings, which are representations of words as vectors in a high-dimensional space. Word embeddings are useful for understanding the connections and similarities between words. The speaker mentions the WordNet database, which is a large graph of words categorized into synsets based on their semantic relationships. However, WordNet has some limitations, and he encourages further exploration of the topic for those interested in linguistics and NLP.
00:20:00 In this section, the speaker discusses the challenges of building a knowledge base for natural language processing (NLP). One challenge is that language constantly evolves, with new words and expressions being added over time. Additionally, the subjective nature of language means that a knowledge base developed by a small group of experts may not capture all the nuances of language spoken by a large and diverse population. To address these challenges, different methods of word representation and preprocessing tools are introduced, such as using stop words, libraries like BeautifulSoup for web scraping, regular expressions for text manipulation, and specific libraries for the Russian language. The speaker also mentions problems related to capitalization, punctuation, abbreviations, and hashtags, and suggests using n-grams to preserve word order.
00:25:00 In this section, the speaker introduces the concept of word embeddings in natural language processing. They explain that instead of working with individual tokens, it is more helpful to work with n-grams or bigrams, which are sequences of adjacent words. The challenge, however, is that the size of the vocabulary increases exponentially when considering all possible word pairs. To address this, the speaker suggests focusing on informative n-grams or collocations and discarding common and rare n-grams. They then introduce the concept of TF-IDF (Term Frequency-Inverse Document Frequency) as a way to measure the frequency and importance of tokens in a document. By multiplying the TF and IDF scores, they obtain a useful representation of the importance of each token.
00:30:00 In this section, the speaker discusses the concept of word embeddings in natural language processing (NLP). Word embeddings are informative vector representations of words that capture the context and meaning of the word. The speaker explains that traditional one-hot vector representations of words have limitations, including being orthogonal and sparse. To address these limitations, the speaker introduces the idea of skip-grams, where the context of a word is taken into account. By considering the surrounding words, skip-grams provide a more informative representation of words. These word embeddings can then be used for various NLP tasks.
00:35:00 In this section, the speaker introduces the concept of word embeddings, which are used in natural language processing (NLP) tasks. Word embeddings allow us to represent words as lower-dimensional vectors, making it easier to analyze and process large amounts of text data. The speaker explains that context plays a crucial role in understanding the meaning and significance of words, and introduces the idea of collocations, or words that frequently appear together. By calculating the co-occurrence frequency of words, we can determine their importance and represent them as vectors in a matrix. This matrix can then be subjected to matrix factorization techniques like Singular Value Decomposition (SVD) to obtain informative word embeddings.
00:40:00 In this section, the speaker discusses the concept of word embeddings and how they can be obtained through matrix factorization. They explain that word embeddings can capture the contextual information of words, allowing us to represent them as vectors. By performing operations on these vectors, such as vector addition and subtraction, we can derive meaningful results. The speaker also mentions the importance of understanding the amount of information contained in the context of a word. They propose the idea of predicting the context of a word based on its word vector, which can be achieved through classification tasks. Overall, the section introduces the concept of word embeddings and discusses their potential applications in natural language processing.
00:45:00 In this section, the speaker discusses the problem of word embeddings in natural language processing (NLP). With a fixed vocabulary size, there are a finite number of choices that can be made. The goal is to predict the context based on a word or vice versa. Different loss functions can be used, such as cross-entropy or likelihood, depending on the classification task. The speaker explains that by generating the training dataset from unlabeled data, such as Russian classics or Wikipedia, a large dataset can be obtained. The speaker then introduces a simple architecture where word embeddings are used to predict the context of a word or the word given the context. The model is trained using gradient descent and backpropagation. The speaker concludes that by using word embeddings, meaning can be extracted from the context of written texts, and this approach allows for supervised learning from unlabeled data.
00:50:00 In this section, the speaker discusses several ways to accelerate model training in natural language processing (NLP). One approach is to reduce the complexity of the data by grouping frequently occurring words that have the same meaning together. Another idea is to use negative sampling, which involves randomly choosing examples from the negative class during training. This helps the model focus on correctly predicting the positive class. Additionally, the speaker introduces the concept of subsampling, which adjusts the frequency of words based on their occurrence in the training data. These techniques not only help speed up training but also provide insights into their applicability to other tasks.
00:55:00 In this section, the speaker discusses word embeddings and their applications in natural language processing. Word embeddings are representations of words in the form of vectors that capture their meanings and relationships. They are useful in various NLP tasks, such as word prediction and document classification. Different approaches, such as skip-gram and GloVe, are used to construct these embeddings. Transfer learning is another important concept introduced by word embeddings, allowing models trained on one task to be applied to another task. However, caution should be exercised when transferring embeddings between domains, as they may not perform well in different contexts. The lecture also touches on the visualization of word embeddings and their use in tasks like translation.

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The video explores various methods of word embeddings in NLP and highlights the limitations of the traditional "bag of words" model. It introduces the more advanced Word2Vec model and its variations, which generate vector representations of words based on context, resulting in more accurate word embeddings. The video concludes by mentioning upcoming discussions on contextual embeddings in subsequent sections.