Syllabus
processing morphology engineering approach to morphology, lemmatization unsupervised and lightly-supervised morphology
Linguistica, Yarowski & Wicentowski 2001, Schoene & Jurafsky 2001, Morfessor sentiment analysis entities named, unnamed and structured entities recognition, normalization, standardization, linking, knowledge graphs intent detection relation extraction
Natural Language Generation (NLG) generation of documents vs. short texts/phrases classical NLG vs neural NLG document planning, microplanning, lexicalization, realization
Annotation
The course surveys solutions to common NLP tasks ranging from entity recognition to text generation. It evaluates various approaches (machine learning, rules, larger resources, ...) and their combinations.
Part of the course consists of students presenting and discussing papers relevant to a give topic. Each student implements a prototype system solving a particular task.