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 Stanford's CoreNLP.

Time and Place

Lectures:
Monday/Wednesday 8:30pm - 9:45pm in Social Sciences, Room 118

Labs:
Wednesday 10:00 - 11:50 in McClelland Park, Room 102

Instructor

Mihai Surdeanu
msurdeanu AT email DOT arizona DOT edu
Office: Gould-Simpson 811
Office Hours: by request