Most of web data today consists of unstructured text.
This course will cover the fundamental knowledge necessary to organize such texts,
search them in a meaningful way, and extract relevant information from them.
This course will teach natural language processing through the design and development of
end-to-end natural language understanding applications, including sentiment analysis
(e.g., is this review positive or negative?), information extraction
(e.g., extracting named entities and their relations from text), and question answering
(retrieving exact answers to natural language questions such as "What is the capital
of France?" from large document collections). We will use several natural language
processing (NLP) and machine learning (ML) toolkits, such as NLTK, scikit-learn, and
Time and Place
Tuesday/Thursday 9:30am - 10:45am in Engineering, Room 304
Labs: NO LAB this semester!
msurdeanu AT email DOT arizona DOT edu
Office: Gould-Simpson 811
Office Hours: by request