Delhi NCR JUG meetup - NLP - APIs - By Vikas Malik
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This document summarizes a presentation about exploring natural language processing APIs and building smart chatbots. It introduces the speaker and their background in NLP. It then defines NLP, outlines its history, and explains why NLP is important and difficult. It describes common NLP components and lists popular APIs that can be used to build chatbots, like API.ai, Watson, and Amazon Lex. Finally, it provides an overview of how an NLP-powered chatbot works and references additional resources.
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subrat
This document discusses using natural language processing (NLP) for searching intranets. It begins with an abstract that introduces NLP and the purpose of exploring its use for intranet searching. The introduction provides an overview of NLP, including that it uses tools from artificial intelligence to process natural languages in two ways: parsing and transition networks. The document then discusses the goals, levels, and applications of NLP, and how NLP is implemented through setting up dictionaries and relationships. It concludes that while still a developing area, NLP has shown promise for information access and will continue to be researched and developed for applications like intranet searching.
Intro
This document provides an overview of natural language processing (NLP). It discusses how NLP is used by major tech companies for applications like information retrieval, extraction, and machine translation. It also outlines some of the core challenges in NLP, including understanding syntax, semantics, anaphora resolution, and information extraction. The document concludes by listing some of the key topics that will be covered over the course of the NLP class, such as part-of-speech tagging, parsing, IR, question answering, and text summarization.
Intro
This document provides an overview of natural language processing (NLP). It discusses how NLP is used by major tech companies for applications like information retrieval, extraction, and machine translation. It also outlines some of the core challenges in NLP, including understanding syntax, semantics, anaphora resolution, and information extraction. The document concludes by listing some of the key topics that will be covered over the course of the NLP class, such as part-of-speech tagging, parsing, IR, question answering, and text summarization.
CNN for NLP using text analysis by using deep learning
The document provides an overview of natural language processing (NLP). It discusses how NLP systems can process written or spoken language. Understanding language involves morphological, syntactic, semantic and discourse analysis, while generation involves planning, syntactic and morphological processes. Ambiguities exist at various levels that NLP systems aim to resolve using techniques from linguistics, computer science, and other fields. The document outlines common NLP tasks and applications and briefly summarizes the history of the field.
Natural language processing ppt for engineering
Natural language processing (NLP) analyzes, understands, and generates human language through computational techniques. It borrows from linguistics, psychology, cognitive science, and statistics to make computers learn human language instead of requiring humans to learn computer languages. NLP involves natural language understanding to map input text to useful representations through morphological, syntactic, semantic, and discourse analysis, as well as natural language generation to synthesize output text through deep planning and syntactic generation. Fully human-level NLP is considered an AI-complete problem equivalent to developing human-level artificial intelligence.
Search-Driven Programming
This document discusses search-driven programming (SDP), where programmers search online for answers to errors or problems rather than relying only on their own knowledge or nearby colleagues. It argues that SDP is now the dominant way programming knowledge is accessed due to the aggregation of knowledge online. The document provides implications for language designers, suggesting they design languages and tools to encourage large online communities where problems are documented to be easily searchable, in order to leverage the "community intelligence" enabled by SDP. It proposes several specific strategies language designers could employ such as attracting prolific bloggers, using distinctive naming conventions, and designing tools that automatically document errors and solutions online.
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Delhi NCR JUG meetup - NLP - APIs - By Vikas Malik
- 1. DELHI NCR JUG MEETUP EXPLORING NATURAL LANGUAGE PROCESSING (NLP) API & BUILDING SMART CHAT BOTS
- 2. INTRODUCTION • Vikas Malik • Member of Delhi NCR JUG community since 2013 • 13 years of experience in Software Development in Java/J2EE • Currently working as Principal System Engineer with Aricent • Current domain – Telecom Services in cloud • Prior worked with various organizations IBM, NIIT, iBilt
- 3. NATURAL LANGUAGE PROCESSING • What is NLP ? • 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.
- 4. NATURAL LANGUAGE PROCESSING • History of NLP ? • 1950 - Alan Turing published an article titled "Computing Machinery and Intelligence“ • The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English • 1980 – Machine Learning Algorithms Introduced – Decision Tree, Hidden Markov Models • 2012 - till day – API.ai, Watson, Amazon Lex and many more…
- 5. NATURAL LANGUAGE PROCESSING • Why do we need NLP ? • To make technology more accessible to EVERYONE • Improve transition time • Bring back natural order to conversation, even with computers
- 6. WHY NLP IS DIFFICULT? • Natural language is extremely rich in form and structure, and very ambiguous. • One input can mean many different things. Ambiguity can be at different levels. • Lexical (word level) ambiguity -- different meanings of words • Syntactic ambiguity -- different ways to parse the sentence • Interpreting partial information -- how to interpret pronouns • Contextual information -- context of the sentence may affect the meaning of that sentence.
- 7. COMPONENTS OF NLP • Speech Recognition • Natural Language Understanding • Natural Language Generation • Language Translation
- 8. WHAT DO I NEED TO DO TO CRACK NLP? • Nothing!!! • Most of it is already solved. • You just need to start using it. • But HOW???
- 9. WHAT ARE MY OPTIONS • API.AI ( Google ) • Watson ( IBM ) • Amazon LEX ( Amazon ) • May be more…
- 10. HOW DOES IT WORK? Text To Speech Perform Action Set Context Extract Entities Identify Intent Speech To Text
- 11. LET’S BUILD A BOT
- 12. REFERENCES • https://api.ai/docs/getting-started/basics • https://www.ibm.com/watson/developercloud/doc/conversatio n/index.html • https://conversation-demo.mybluemix.net/ • http://docs.aws.amazon.com/lex/latest/dg/getting- started.html