Foundation of NLP
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Foundation of NLP
NLP applies linguistic, statistical, and ML methods to turn raw text into structured signals that capture meaning and context; a simple example is dictionary-based sentiment scoring. Through preprocessing and a bag-of-words document–term matrix (counts/TF-IDF), text becomes usable for tasks like classification, retrieval, and quick insight generation.
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Requirements
- see DELTA course requirements
General Overview
Description
Introduction to NLP: A set of linguistic, statistical, and ML techniques that enable computers to process, analyze, and approximate understanding of textual data, capturing contextual nuances and the informational content embedded in language. Illustrative example — dictionary-based sentiment analysis: uses polarity lexicons to score words and aggregate sentiment at sentence or document level; valued for simplicity and transparency, but sensitive to context (e.g., negation, sarcasm, domain-specific terms). NLP pipeline & bag-of-words: begins with text preprocessing (tokenization, normalization, stopword removal, stemming/lemmatization), then constructs a document–term matrix (counts or TF-IDF) to power applications such as classification, retrieval, topic sketching, and exploratory analysis.
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Meet the instructors !
Stefan received a PhD from the University of Hamburg in 2007, where he also completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. He serves as an associate editor for the International Journal of Business Analytics, Digital Finance, and the International Journal of Forecasting, and as department editor of Business and Information System Engineering (BISE). Stefan has secured substantial amounts of research funding and published several papers in leading international journals and conferences. His research concerns the support of managerial decision-making using quantitative empirical methods. He specializes in applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.