The document provides an overview of natural language processing (NLP), including its components, terminology, applications, and challenges. It discusses how NLP is used to teach machines to understand human language through tasks like text summarization, sentiment analysis, and machine translation. The document also outlines some popular NLP libraries and algorithms that can be used by developers, as well as current research areas and domains where NLP is being applied.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Natural language processing PPT presentationSai Mohith
A ppt presentation for technicial seminar on the topic Natural Language processing
References used:
Slideshare.net
wikipedia.org NLP
Stanford NLP website
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning.
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
A simple introduction to Natural Language Processing, with its examples, and how it works with the flowchart.
Natural Language Understanding, Natural Language Generation activities.
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Natural language processing PPT presentationSai Mohith
A ppt presentation for technicial seminar on the topic Natural Language processing
References used:
Slideshare.net
wikipedia.org NLP
Stanford NLP website
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning.
For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions.
Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.
Programming languages and concepts by vivek pariharVivek Parihar
This presentation is concerned with the study of programming language paradigms, that is the various systems of ideas that have been used to guide the design of programming languages. These paradigms are realized to a greater or lesser extent in various computer languages, although the design of a given language may reflect the influence of more than one paradigm.
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Contents
❏ What is NLP?
❏ Where is NLP?
❏ Components of NLP
❏ NLP Terminology
❏ Steps in NLP
❏ Developers Usage of NLP
Algorithms?
❏ Practical Examples
❏ NLP Libraries
❏ Difficulties of NLP
❏ Innovations
❏ Research Areas
❏ Expansion domains of NLP
❏ References
CREATED BY AANCHAL CHAURASIA - 2017
3. Natural Language?
Refers to the language spoken by people,
i.e English, Spanish, japanese as opposed
to artificial languages like C++, Java etc.
CREATED BY AANCHAL CHAURASIA - 2017
4. Natural Language Processing?
Natural Language Processing is the science of
teaching Machines how to understand the language
we Human speak and write.
CREATED BY AANCHAL CHAURASIA - 2017
5. Aim of NLP?
To build intelligent system that can interact
With Human Being like a Human Being.
CREATED BY AANCHAL CHAURASIA - 2017
7. NLP is Everywhere even if we
don’t know it. And although NLP
applications can rarely achieve
performances of 100%, still they
are part of our lives and provide
a precious help to all of us.
❏ Machine translation
❏ Automatic summarization
❏ Sentiment analysis
❏ Text classification
❏ Language detection
❏ Information Extraction & lot more
CREATED BY AANCHAL CHAURASIA - 2017
8. Input & Output of NLP
NLP involves making computers to perform useful tasks with the natural
languages humans use. The input and output of an NLP system can be :
❏ Speech
❏ Written Text
CREATED BY AANCHAL CHAURASIA - 2017
10. Natural Language Understanding
NLU involves the following tasks :
❏ Analyzing different aspects of the
language.
❏ Mapping the given input in natural
language into useful representations.
❏ The NLU is harder than NLG.
Natural Language Generation
NLG involves the following tasks :
❏ Text planning : It includes retrieving the
relevant content from knowledge base.
❏ Sentence planning : It includes choosing
required words, forming meaningful
phrases, setting tone of the sentence.
❏ Text Realization : It is mapping sentence
plan into sentence structure.
CREATED BY AANCHAL CHAURASIA - 2017
12. ❏ Phonology : It is study of organizing sound patterns and their meanings.
❏ Morphology : It is a study of construction of words from primitive meaningful
units.
❏ Morpheme : A meaningful morphological unit of a language that cannot be
further divided (e.g. in, come, -ing, forming incoming )
❏ Syntax : It refers to arranging words to make a sentence. It also involves
determining the structural role of words in the sentence and in phrases.
CREATED BY AANCHAL CHAURASIA - 2017
13. ❏ Semantics : It is concerned with the meaning of words and how to combine
words into meaningful phrases and sentences.
❏ Pragmatics : It deals with using and understanding sentences in different
situations and how the interpretation of the sentence is affected. (e.g. Young
men and women)
❏ Discourse : It deals with how the immediately preceding sentence can affect
the interpretation of the next sentence.
❏ World Knowledge : It includes the general knowledge about the world.
CREATED BY AANCHAL CHAURASIA - 2017
17. ❏ Lexical Analysis : Lexical analysis is dividing the whole chunk of text into
paragraphs, sentences, and words.
❏ Syntactic Analysis (Parsing) : It involves analysis of words in the sentence
for grammar and arranging words in a manner that shows the relationship
among the words. i.e, Boy the go the to store
❏ Semantic Analysis : It draws the exact meaning or the dictionary meaning
from the text. The text is checked for meaningfulness. It is done by mapping
syntactic structures. i.e, colourless green idea
CREATED BY AANCHAL CHAURASIA - 2017
18. ❏ Discourse Integration : The meaning of any sentence depends upon the
meaning of the sentence just before it. In addition, it also brings about the
meaning of immediately succeeding sentence.
I.e, Kate wanted it.
❏ Pragmatic Analysis : During this, what was said is re-interpreted on what it
actually meant. It involves deriving those aspects of language which require
real world knowledge.
I.e, Do you know what day it is?
CREATED BY AANCHAL CHAURASIA - 2017
19. Developers Use of NLP Algorithms?
CREATED BY AANCHAL CHAURASIA - 2017
20. ❏ By utilizing NLP, developers can organize and structure knowledge to perform
tasks such as automatic summarization, translation, named entity
recognition, relationship extraction, sentiment analysis, speech recognition
etc.
❏ Algorithms are available with various useful applications on many platforms
using : Ruby, Python, Java, JavaScript, NodeJs, Swift & so on.
❏ Instead of hand-coding large sets of rules, NLP can rely on machine learning
to automatically learn these rules by analyzing a set of examples.
❏ The more data analyzed, the more accurate the model will be.
CREATED BY AANCHAL CHAURASIA - 2017
21. Let us look into
some practical Applications
CREATED BY AANCHAL CHAURASIA - 2017
22. 1. Summarize blocks of Text
Summarizer extracts the most important and central ideas while ignoring
irrelevant information.
❏ INPUTS :
(Required): Large block of text.
(Optional): Number of sentences (default=3)
❏ OUTPUT :
Summarized block of text.
CREATED BY AANCHAL CHAURASIA - 2017
23. 2. Auto tag
Automatically generate keyword tags : A technique that discovers topics
contained within a body of text.
❏ INPUTS :
(Required): Raw text.
❏ OUTPUT :
List of extracted tags.
CREATED BY AANCHAL CHAURASIA - 2017
24. ❏ INPUTS :
{
"document": text
}
❏ OUTPUT :
{
"sentences": List
[{
"detectedEntities": List
[{
"word": String,
"entity": String
}]
}]
}
Identify the type of entity extracted, such as it being a person, place, or
organization using Named Entity Recognition.
3. Named Entity Recognition
CREATED BY AANCHAL CHAURASIA - 2017
25. Identify and extract sentiment in given string. This algorithm takes an input string
and assigns a sentiment rating in the range [-1 t0 1] (very negative to very
positive).
❏ INPUTS :
{
"document": text
}
❏ OUTPUTS :
[
{
"sentiment": 0.474,
"document": "I really like
eating ice cream in the morning!
}
]
4. Sentiment Analysis
CREATED BY AANCHAL CHAURASIA - 2017
26. This is a algorithm for summarizing content in webpages. This algorithm utilizes two
algorithm: util/Html2Text and nlp/Summarizer. It retrieves the html content in a smart way
by using heuristics with Html2Text algorithm, and summarizes the content using the
Summarizer algorithm.
❏ INPUTS :
(Required): String URL.
(Optional): Number of sentences. (default=3)
❏ OUTPUT :
List of extracted tags.
5. Summarize URL
CREATED BY AANCHAL CHAURASIA - 2017
28. ❏ Spelling Correction, Suggests spelling corrections (aka. Autocorrect) for English words.
❏ Detect profanity in text automatically. It is word-based only. It may miss some words,
miss certain misspellings, or double entendres and other material that is offensive in
context.
❏ Create a chat bot using Parsey McParseface, a language parsing deep learning model
made by Google that uses Point-of-Speech tagging.
❏ Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens
using Tokenizer.
CREATED BY AANCHAL CHAURASIA - 2017
30. These libraries provide the algorithmic building blocks of NLP in real-world applications.
❏ Algorithmia provides a free API endpoint for many algorithms.
❏ Apache OpenNLP: a machine learning toolkit that provides tokenizers, sentence
segmentation, part-of-speech tagging, named entity extraction, parsing and more.
❏ Natural Language Toolkit (NLTK): a Python library that provides modules for
processing text, classifying, tokenizing, tagging, parsing, and more.
❏ Standford NLP: a suite of NLP tools that provide part-of-speech tagging, the named
entity recognizer, coreference resolution system, sentiment analysis, and more.
❏ Google SyntaxNet a neural-network framework for analyzing and understanding the
grammatical structure of sentences.
CREATED BY AANCHAL CHAURASIA - 2017
32. Human language is rarely precise, or plainly spoken. To understand human language is
to understand not only the words, but the concepts and how they’re linked together to
create meaning.
Despite language being one of the easiest things for humans to learn, the ambiguity of
language is what makes NLP a difficult problem for computers to master.
❏ Lexical ambiguity : It is at very primitive level such as word-level.
For example, treating the word “board” as noun or verb?
CREATED BY AANCHAL CHAURASIA - 2017
33. ❏ Syntax Level ambiguity : A sentence can be parsed in different ways.
For example, “He lifted the beetle with red cap.” Did he use cap to lift the beetle or he
lifted a beetle that had red cap?
❏ Referential ambiguity : Referring to something using pronouns.
For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired?
❏ One input can mean different meanings.
❏ Many inputs can mean the same thing.
CREATED BY AANCHAL CHAURASIA - 2017
35. ❏ Last year Google had announced the public beta launch of its Cloud Natural Language
API which has, sentimental analysis, syntax detection, entity recognition etc,
i.e the Vision API and the Translate API.
❏ Vision API has features : Label Detection, Face Detection, Explicit Content Detection,
Image Attributes, Logo Detection, Landmark Detection, Optical Character Recognition.
❏ Translate API feature : Text Translation, Language Detection.
❏ Vision API & Translation API pricing is based on usage.
CREATED BY AANCHAL CHAURASIA - 2017
36. ❏ A new foundational machine learning framework used across Apple products, including
Siri, Camera, and QuickType has made available for developers via Core ML.
❏ Core ML delivers fast performance with easy integration using just a few lines of code.
❏ Supported features include face tracking, face detection, landmarks, text detection,
rectangle detection, barcode detection, object tracking, and image registration.
❏ Core ML Tools is a python package.
❏ Core ML Model provides Places205-GoogLeNet, ResNet50, Inception v3, VGG16.
❏ Requirements Xcode 9 and iOS 11
CREATED BY AANCHAL CHAURASIA - 2017
37. ❏ Medical
❏ Forensic Science
❏ Advertisement
❏ Education
❏ Business Development
❏ Marketing & where ever we use language
Expansion Domains of NLP
CREATED BY AANCHAL CHAURASIA - 2017
38. Research Areas
❏ Text Processing
❏ Speech Processing
❏ Text to Speech
❏ Automatic Speech Recognition
❏ Speech to Speech Translation
❏ Commercial machine translation
systems
❏ Search Engines
❏ Advanced text Editors
❏ Information extraction
❏ Fraud detection
❏ Opinion mining
❏ Sentimental analysis
CREATED BY AANCHAL CHAURASIA - 2017
