Natural Language Processing

Start Date: 05/19/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/language-processing

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About Course

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.

Course Syllabus

In this module we will have two parts: first, a broad overview of NLP area and our course goals, and second, a text classification task. It is probably the most popular task that you would deal with in real life. It could be news flows classification, sentiment analysis, spam filtering, etc. You will learn how to go from raw texts to predicted classes both with traditional methods (e.g. linear classifiers) and deep learning techniques (e.g. Convolutional Neural Nets).

Deep Learning Specialization on Coursera

Course Introduction

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sent

Course Tag

Chatterbot Tensorflow Deep Learning Natural Language Processing

Related Wiki Topic

Article Example
History of natural language processing The history of natural language processing describes the advances of natural language processing (Outline of natural language processing). There is some overlap with the history of machine translation and the history of artificial intelligence.
Outline of natural language processing The following natural language processing toolkits are popular collections of natural language processing software. They are suites of libraries, frameworks, and applications for symbolic, statistical natural language and speech processing.
Outline of natural language processing The following technologies make natural language processing possible:
Natural language processing Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve natural language understanding, natural language generation (frequently from formal, machine-readable logical forms), connecting language and machine perception, managing human-computer dialog systems, or some combination thereof.
Outline of natural language processing Natural language processing can be described as all of the following:
Natural language processing in the late 1980s and mid 1990s, much Natural Language Processing research has relied heavily on machine learning.
Studies in Natural Language Processing Studies in Natural Language Processing is the book series of the
Outline of natural language processing The following outline is provided as an overview of and topical guide to natural language processing:
Empirical Methods in Natural Language Processing Empirical Methods in Natural Language Processing or EMNLP is a leading conference in the area of Natural Language Processing. EMNLP is organized by the ACM special interest group on linguistic data (SIGDAT).
Outline of natural language processing Natural language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields:
Natural language understanding Natural language understanding (NLU) is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU is considered an AI-hard problem.
Language Computer Corporation Language Computer Corporation (LCC) is a natural language processing research company based in Richardson, Texas. The company develops a variety of natural language processing products, including software for question answering, information extraction, and automatic summarization.
Natural language processing Formerly, many language-processing tasks typically involved the direct hand coding of rules, which is not in general robust to natural language variation. The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large "corpora" of typical real-world examples (a "corpus" (plural, "corpora") is a set of documents, possibly with human or computer annotations).
Outline of natural language processing Natural language processing – computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, dictation, publishing, translation, lip reading, and so on. Natural language processing is also the name of the branch of computer science, artificial intelligence, and linguistics concerned with enabling computers to engage in communication using natural language(s) in all forms, including but not limited to speech, print, writing, and signing.
Ubiquitous Knowledge Processing Lab DKPro contains basic natural language processing components like part-of-speech tagging and lemmatization.
Natural language user interface Natural language interfaces are an active area of study in the field of natural language processing and computational linguistics. An intuitive general natural language interface is one of the active goals of the Semantic Web.
AFNLP AFNLP (Asian Federation of Natural Language Processing Associations) is the organization for coordinating the natural language processing related activities and events in the Asia-Pacific region.
Natural language user interface Siri is an intelligent personal assistant application integrated with operating system iOS. The application uses natural language processing to answer questions and make recommendations.
Australasian Language Technology Association ALTA is a founding regional organization of the Asian Federation of Natural Language Processing (AFNLP).
Artificial intelligence Natural language processing gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation.