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Natural Language Processing History

Natural Language ProcessingLearn basics of Natural Language Processing

Natural language processing(NLP) is a field of artificial intelligence, computational linguistics, and computer science which is related to the interaction between human (natural) languages and computers. From LiveEdu.tv, you will learn NLP the right way by following premium tutorials and also finding other resources. Improve your career by getting the pro subscription. Let’s build things together.

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Natural Language Processing Introduction

Natural language processing(NLP) is a field of artificial intelligence, computational linguistics, and computer science and is related to the interaction between human (natural) languages and computers. There are numerous challenges in the field which include natural language generation, natural language understanding, connecting languages and machine perception.

History of Natural Language Processing

The history of Natural Language Processing can be traced back to 17th century when philosophers such as Leibniz and Descartes put forward proposals for codes to relate words between languages. Although these proposals only remained theoretical back then, they laid the ground for the development of an actual machine. The first patents for translating machine was applied in the mid-1930s by Georges Artsrouni's proposal. From that time, machine translation has undergone several changes. Below are some important changes that took place.

  • 1950: Alan Turing in his article, "Computing Machinery and Intelligence" proposed the Turing test as a criterion of intelligence. The Turing test questioned the ability for machines to exhibit intelligent behavior equivalent to that of humans.
  • 1957: The Noam Chomsky’s Syntactic Structures helped revolutionized Linguistics with universal grammar.
  • 1969: Roger Schank introduced a model that was partially influenced by the work of Sydnel lamb which was used extensively by Schank's students at Yale University.
  • 1970: William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Many programmers also began writing conceptual ontologies which structured real-world information into computer-understandable data in the 1970s.
  • 1980: A revolution in NLP began in the 1980s with the introduction of machine learning algorithms for language processing. This was because of the steady increase in computational power resulting from Moore's Law and the gradual lessening of the dominance of Chomskyan theories of linguistics, whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing.
  • 2006: Watson, a question-answering computer system capable of answering questions posed in Natural language was developed in IMB's DeepQA project by a research team which was led by Principal Investigator David Ferrucci.

Natural Language Processing Tools

Like any subfield of artificial intelligence, Natural language processing is huge and requires tools to work effectively and efficiently. The tools will help you gain an advantage and makes work easy. Let us list some of the best Natural language processing tools out there. We will focus only on open source tools because they are easy to acquire.

  • Stanford's Core NLP Suite The suite is a GPL-licensed framework that has tools for tokenization, grammar parsing, entity recognition, etc. It can process text in Chinese, English, and Spanish.
  • Natural Language Toolkit Natural Language Toolkit is a simple Python toolkit that lets you manipulate text according to your needs. It can help you parse, tokenize and do other normal stuff.
  • Apache Lucene and Solr This toolkit enables you to work with powerful text manipulations including tokenization, finite state automatons, etc.
  • Apache OpenNLP: Apache OpenNLP has a different approach to Stanford's Library. It is Apache-licensed suite and offers same functionality that of Stanford NLP suite.
  • GATE and Apache UIMA Both tools offer processing capabilities that work with different processing steps to build complex NLP workflows.

Education Ecosystem Natural Language Processing Project Creators

If you are wondering where to get started to learn NLP, then we will recommend you to watch NLP Project Creators on Education Ecosystem. Let us list top 5 NLP Project Creators on Education Ecosystem.

Project Creators!

Natural Language Processing Best Books

There are plenty of Natural Language Processing books online. The best way to start learning NLP is to invest in the books. So, why the wait? Let’s go through the best books for learning NLP. These books are categorized into Beginner, Intermediate and Advanced. So pick the book that best suits your level.

  • Book cover

    Advanced Applications of Natural Language Processing for Performing Information Extraction

    by Mário Rodrigues, António Teixeira

    This book explains how you can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE.

  • Book cover

    Natural Language Processing: Semantic Aspects

    by Epaminondas Kapetanios, Doina Tatar, Christian Sacarea

    This book introduces the semantic aspects of natural language processing and its applications. Topics covered include measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms.

  • Book cover

    Natural Language Processing: EAIA '90

    by Miguel Filgueiras,Luis Damas, Nelma MoreiraAna P. Tomas

    This volume is the proceedings of the Second Advanced School on Artificial Intelligence (EAIA '90) held in Guarda, Portugal, October 8-12, 1990. The focus of the contributions is natural language processing. Two types of subject are covered: - Linguistically motivated theories, presented at an introductory level, such as X-bar theory and head- driven phrase structure grammar, - Recent trends in formalisms which will be familiar to readers with a background in AI, such as Montague semantics and situation semantics.

Natural Language Processing Projects

The best way to learn is to evolve yourself with Projects. Let’s take a look at some of the best NLP projects that you can follow. You can also find NLP projects on Education Ecosystem. If you are interested in finding projects on Education Ecosystem, check the Education Ecosystem NLP Project Creators section for more information.

Classify the tweets for sports, business, politics, entertainment etc and detect trending tweets in those domain

Explore this project!

Use online movie reviews to predict reviews of new movies.

Explore this project!

Take a list of reviews about a restaurant, and generate a single English summary for that restaurant.

Explore this project!

Build a system that can have a conversation with you. The user types messages, and your system replies based on the user's text. Many approaches here ... you could use a large twitter corpus and do language similarity

Explore this project!

Collect tweets for various categories hourly, daily base, identify trending discussion, perform semantic analysis and create kinda news system (Check Frrole product)

Explore this project!

Natural Language Processing Community

The community of data visualization is big. There are plenty of websites you can find a community and become part of it. Let’s list some of them below.

Natural Language Processing Gurus

Rockstars!
  • Cyril Allauzen

    Cyril Allauzen is a prominent scientist working at Google. He is from New York and his research surrounds around finite-state methods. In the past, he worked as a researcher in the AT&T Research.

    Cyril Allauzen
  • Shay Cohen

    Shay Cohen

    Shay Cohen is a the Chancellor's Fellow/Lecturer at the University of Edinburgh. He has wide range of interest, ranging from the statistical learning to computational linguistic. His interest extends to the Natural Language Processing.

  • Michael Collins

    Michael Collins is a professor of Computer Science at Columbia University. He has also been the editor-in-chief for many ACL-Sponsored journal. His main interest lies in NLP. He also teaches Natural Language Processing on Coursera.

    Michael Collins
  • Kathleen McKeown

    Kathleen McKeown

    Kathleen McKeown is currently the director for the Institute for Data Science and Engineering. Her major interest is in natural language processing..

  • Ralph Grishman

    Ralph Grishman is the professor at Computer Science Dept. in the New York University. He is the founder of the Proteus Project. The project is created solely on Natural Language Processing research.

    Ralph Grishman

Natural Language Processing Conferences

As Robotics is a trending topic in the market, there are many conferences out there that you can attend. Let’s list some of the best Robotics conferences out there.