Natural Language Processing (7/8)

Description

Stanford University. — Coursera, 2012. — eLearning (Video+PDF slides) 960x540 / H264 ~54 kbps / AAC ~705 / KbpsВидеокурс по обработке текстов, написанных на естественных языках. Курс разбит по неделям обучения. Недели содержат разное количество тем. Слайды недели собраны в файлы PDF и презентации PPTX, которыми можно пользоваться для закрепления знаний.Архив разбит по неделям на восемь частей. Каждой частью архива можно пользоваться отдельно. В настоящем архиве материалы первой недели.This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford's exciting forays into large scale online instruction. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford's courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your smartphone.Courses list:Week 1 - Course IntroductionWeek 1 - Basic Text ProcessingWeek 1 - Edit DistanceWeek 2 - Language ModelingWeek 2 - Spelling CorrectionWeek 3 - Text ClassificationWeek 3 - Sentiment AnalysisWeek 4 - Discriminative classifiers: Maximum Entropy classifiersWeek 4 - Named entity recognition and Maximum Entropy Sequence ModelsWeek 4 - Relation ExtractionWeek 5 - Advanced Maximum Entropy ModelsWeek 5 - POS TaggingWeek 5 - Parsing IntroductionWeek 5 - Instructor ChatWeek 6 - Probabilistic ParsingWeek 6 - Lexicalized ParsingWeek 6 - Dependency Parsing (Optional)Week 7 - Information RetrievalWeek 7 - Ranked Information RetrievalWeek 8 - SemanticsWeek 8 - Question AnsweringWeek 8 - SummarizationWeek 8 - Instructor Chat II