101 Intro to nlp

Naive Bayes algorithm and CRFs

RNNs can handle sequences of words, characters, and other types of sequences

LSTMs and GRUs

Transformers, BERT, and GPT

Some applications of NLP include generating summarized reports of product reviews, aspect-based sentiment analysis, language translation, auto-generating content, Q&A systems, enhancing and correcting text, search engine recommendations, and building machine learning models.

Amazon uses NLP to generate summarized reports of product reviews by analyzing user feedback.

Grammarly is an example of an NLP tool that enhances and corrects text by suggesting rephrasing and fixing grammatical errors.

The NLP module will cover encoding techniques such as Bag of Words and TF-IDF.

Some key applications of NLP include chatbots, language translation, speech-to-text, live video closed captioning, number plate recognition, text summarization, sentiment analysis, and understanding sentiments from reviews.

The representation of text as a mathematical vector is crucial for NLP models because it allows the models to process and analyze the textual data in a numerical format, enabling mathematical operations and computations to be performed on the text.

Some common tasks in NLP include chatbots, language translation, speech-to-text, live video closed captioning, and number plate recognition. Additionally, text summarization, sentiment analysis, and understanding sentiments from reviews are also important applications of NLP.

In the session on Natural Language Processing (NLP), the speaker will provide an overview of the NLP module and discuss key applications of NLP. The session will cover basic terminology and preprocessing techniques unique to text and NLP. A simple task of determining the sentiment of COVID-related tweets will be tackled. The speaker acknowledges that this task is relatively simpler in NLP but will progress to more complex topics in future sessions. Examples and explanations will be provided throughout the session.

The purpose of binary classification is to determine whether a tweet is positive or negative.

The sentiment labels include extremely positive, extremely negative, positive, negative, and neutral.

The data imbalance is not severe, allowing for a multi-class classification approach.

The dataset is split using an 80-20 split.

The four building blocks of language in NLP are phonemes, morphemes, lexemes, and syntax.

Sentiment analysis is a common NLP task that involves determining the sentiment (positive or negative) of a text. It is useful in understanding public sentiment, such as during a pandemic, to educate and address concerns.

Two powerful tools for NLP tasks are the NLTK (Natural Language Toolkit) library and the SpaCy library.

The problem at hand is to analyze the sentiment of tweets related to a new COVID-19 strain, with the goal of understanding public sentiment and addressing concerns.

Tokenization is the process of splitting text into individual words or tokens. It is an important step in NLP as it allows the text to be processed and analyzed at the word level.

The bag-of-words representation is a way to represent text by counting the presence or absence of words in a document. It creates a vector where each element represents a word, and the value represents the frequency of that word in the document.

Bag-of-words representation can be used in logistic regression by assigning weights to the positive and negative words in the text. These weights help in predicting the sentiment or classification of the document.

Pre-processing in NLP involves cleaning and transforming the raw text data to make it suitable for analysis. It includes tasks like removing punctuation, converting text to lowercase, handling special cases like URLs and hashtags, and tokenization.

The purpose of pre-processing text in NLP is to clean and transform the raw text data into a format that is suitable for analysis. This involves tasks such as removing special characters, converting text to lowercase, tokenization, removing stop words, and applying techniques like stemming or lemmatization to reduce words to their root form.

The Bag of Words representation is a simple strategy where each document is represented as a set of words, ignoring word order. It involves creating a vocabulary of all unique words in the corpus and representing each document as a vector where each element represents the frequency of a word in the document. This representation is commonly used in text classification and information retrieval tasks.

PCA does not take class labels into consideration when reducing dimensions, making it class label agnostic. In some cases, using PCA for dimensionality reduction can lead to a worst case scenario where positive and negative points get completely jumbled up, resulting in data points that are not separable by any model. It is important to be cautious when using PCA for dimensionality reduction without considering class labels.

Regular expressions can be used to extract specific patterns from text, such as email addresses. NLTK provides word tokenization and sentence tokenization functions that can be used as an alternative to regular expressions. Sentence tokenization can be done by splitting text based on punctuation marks like periods, exclamation marks, or question marks. NLTK also includes functionalities for removing retweets, URLs, and hashtags in tweet pre-processing.

The purpose of the session mentioned in the text is to address any questions and confusion regarding the placement process.

The whole placements team, including some other team members, will be presenting at the session.

The driving force behind attending the session is the career state, as it will provide answers to many questions related to the career process.

The dictionary consists of three variables: bias term, sum of positive frequencies, and sum of negative frequencies.

Positive and negative frequencies for each keyword are calculated based on the presence of words with positive or negative connotations. Words with positive connotations have higher positive frequencies, while words with negative connotations have higher negative frequencies.

The plotted line is not a classifier but a 45-degree line used for visualization.

The constructed features are the sum of positive frequencies and the sum of negative frequencies.

The purpose of feature engineering with frequency-based dictionary is to create a three-dimensional model using the cumulative sum of positive and negative frequencies of words, which can indicate the likelihood of a positive tweet.

The feature engineering technique of frequency-based dictionary construction is inspired by naive base probabilities, where the frequency of occurrence of a word with positive class can indicate the likelihood of a positive tweet.

Instead of multiplying probabilities, the feature engineering technique of frequency-based dictionary construction sums logarithms of probabilities. This is a hackier version of naive base probabilities.

The function 'build frequencies' can be used to construct the frequency dictionary using positive and negative labels.

Feature engineering is the process of creating new features from existing data to improve the performance of a machine learning model. In the context of sentiment analysis, feature engineering involves extracting relevant information from text, such as word frequencies or sentiment scores, and using these features as inputs to the model. By incorporating additional information that captures the underlying sentiment of the text, feature engineering can help the model better understand and predict sentiment.

Lemmatization is the process of reducing words to their base or root form. In the context of sentiment analysis, lemmatization can improve the performance of the model by reducing the dimensionality of the input data. By reducing words to their base form, lemmatization helps to group together different variations of the same word, which can improve the model's ability to generalize and identify sentiment across different forms of a word. This can lead to more accurate predictions and improved model performance.

L1 regularization, also known as Lasso regularization, is a technique used in machine learning to reduce the dimensionality of the data by adding a penalty term to the loss function. This penalty term encourages the model to set some of the feature weights to zero, effectively eliminating irrelevant or useless features. By reducing the dimensionality of the data, L1 regularization helps to simplify the model and improve its interpretability, while also potentially reducing overfitting and improving generalization performance.

Logistic regression is a statistical model used for binary classification tasks, such as sentiment analysis. It works by estimating the probability that an input belongs to a certain class (e.g., positive or negative sentiment) based on a set of input features. Logistic regression uses a logistic function to map the linear combination of the input features to a probability value between 0 and 1. It is commonly used in sentiment analysis because it is computationally efficient, interpretable, and can handle high-dimensional data. Additionally, logistic regression allows for the incorporation of feature engineering techniques to improve its performance.

The purpose of L1 regularization in NLP is to create sparsity in weights, allowing only important ones to remain. It helps in feature selection by shrinking less important weights to zero. For example, in a linear regression model with L1 regularization, the objective function can be written as: $J(w) = \frac{1},{2m}\sum_{i=1}^{m}(y_i - h_w(x_i))^2 + \lambda\sum_{j=1}^{n}|w_j|$ where $h_w(x_i)$ is the predicted value for the $i$-th instance, $y_i$ is the true value, $x_i$ is the feature vector, $w_j$ is the weight associated with the $j$-th feature, $n$ is the number of features, and $\lambda$ is the regularization parameter.

The feature engineering method discussed in class is inspired by naive Bayes and logistic regression. It involves converting an empty tweet into a feature vector with two features and a bias term. The two features represent the counts of positive and negative sentiment words in the tweet. This method connects the two by using the counting mechanism of naive Bayes to capture the sentiment information in the tweet, and then applying logistic regression to learn the weights that determine the sentiment of the tweet.

Class imbalance in NLP can be handled using various strategies. Some strategies mentioned in the class include: up-sampling or down-sampling the minority or majority class respectively, assigning class-specific weights in the loss function, using different evaluation metrics that are robust to class imbalance such as F1 score or Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and using ensemble methods that combine multiple models trained on imbalanced data.

The purpose of the core math solution exercise mentioned in the class is to explore a core math solution to a problem in feature engineering. It aims to deepen the understanding of the mathematical concepts and principles behind feature engineering techniques, and how they can be applied to solve real-world NLP problems. By working on the exercise, students can gain insights into the mathematical foundations of NLP algorithms and develop a stronger grasp of the underlying principles.

Amazon uses NLP to generate summarized reports of product reviews by analyzing user feedback.

Detecting sarcasm in reviews is challenging when using NLP for language translation.

Grammarly is an example of an NLP tool that enhances and corrects text by suggesting rephrasing and fixing grammatical errors.

Bag of words and TF-IDF are encoding techniques that will be covered in the module.

Some common tasks in NLP include chatbots, language translation, speech-to-text, and live video closed captioning.

Number plate recognition is an interesting application of NLP, involving object detection and optical character recognition.

Text summarization is a useful task that allows for the generation of summaries from lengthy documents.

The session will cover basic terminology, pre-processing techniques, and a simple task of sentiment analysis on tweets related to the COVID crisis.

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

Classical NLP techniques, 1D convolutions, attention models in Transformers

Language translation, sentiment analysis

Topic modeling, text classification, information extraction

The goal is to determine whether a tweet is positive or negative.

The sentiment distribution in the data shows that there is some class imbalance, but it is not severe.

The general approach to solving the problem is to convert the text into a numerical vector.

One idea is to use the bag of words technique to represent the text. Another idea is to create a list of positive and negative words and count their occurrences in the text.

The four building blocks of language in NLP are phonemes, morphemes, syntax, and context.

The purpose of feature engineering with frequency-based dictionary is to create a numerical representation of text data by counting the frequency of words in a given corpus.

One challenge in using NLP for language translation is the difficulty of capturing the nuances and idiomatic expressions specific to each language.

The purpose of pre-processing in NLP is to clean and transform raw text data into a format that can be easily understood and processed by NLP algorithms.

The purpose of preprocessing in NLP is to clean and transform raw text data into a format that is suitable for analysis. This includes tasks such as removing punctuation, converting text to lowercase, handling special characters, and removing stop words.

Tokenization is the task of splitting text into individual words or tokens. It is an important step in NLP as it allows the analysis of text at a granular level and enables further processing such as counting word frequencies or training machine learning models.

Regular expressions can be used to detect and handle specific patterns in text data. In the case of preprocessing URLs and hashtags, regular expressions can be used to identify and remove URLs or extract meaningful information from hashtags by removing the hash symbol.

Bag of words can lead to high dimensionality, which can be problematic. It does not consider the order or context of words, resulting in the loss of important information. Additionally, it treats all words equally, disregarding their importance or frequency in the text.

The pre-processing steps for tweets may include removing retweets, URLs, and hashtags. These steps are necessary to clean the data and remove noise or irrelevant information.

Stop words are common words, such as 'and', 'the', 'is', etc., that are often removed during text pre-processing. They are removed because they do not carry much meaning and are not useful for analysis.

Stemming and lemmatization are techniques used to reduce words to their root form. Stemming removes suffixes from words to get the root, while lemmatization considers the vocabulary and grammar rules to find the root.

The main drawback of the Bag of Words approach is the high dimensionality when the vocabulary size is large compared to the number of data points.