Handling Text: Document Retrieval & Classification

This lecture covers the fundamental tasks of document retrieval and classification in text analysis. It starts by explaining the challenges of handling unstructured textual data from various sources like the web and social media. The instructor introduces the concept of document retrieval, where documents are ranked based on their similarity to a query. Then, the focus shifts to document classification, where documents are assigned to predefined classes. The lecture also delves into sentiment analysis, determining the sentiment of a text, and topic detection, identifying prevalent topics in a collection of documents. Various techniques such as supervised learning, feature vectors, and bag-of-words models are discussed in detail, along with the importance of preprocessing steps like tokenization, stopword removal, and word normalization.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (37)

Related concepts (97)

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat

Unsupervised learning

Unsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.

Document retrieval

Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words. Document retrieval is sometimes referred to as, or as a branch of, text retrieval. Text retrieval is a branch of information retrieval where the information is stored primarily in the form of text.

Document classification

Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Bag-of-words model

The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier.

Nearest Neighbor Rules: Part 2 EE-566: Adaptation and learning

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Unsupervised Learning: PCA & K-means ME-390: Foundations of artificial intelligence

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Vision-Language-Action Models: Training and Applications

Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.

Ground Penetrating Radar: Data Analysis CIVIL-226: Introduction to machine learning for engineers

Explores the automated picking of reinforcement bars within Ground Penetrating Radar data using machine learning and signal processing techniques.

Document Analysis: Topic Modeling DH-406: Machine learning for DH

Explores document analysis, topic modeling, and generative models for data generation in machine learning.