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 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.
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 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.
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(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
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
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.
This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
For more topics stay tuned with Learnbay.
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(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
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
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.
This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
For more topics stay tuned with Learnbay.
Natural Language Processing: State of The Art, Current Trends and Challengesantonellarose
Diksha Khurana1
, Aditya Koli1
, Kiran Khatter1,2 and Sukhdev Singh1,2
1Department of Computer Science and Engineering
Manav Rachna International University, Faridabad-121004, India
2Accendere Knowledge Management Services Pvt. Ltd., India
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.
The presentation explains topics on study of language, applications on natural language processing, levels of language analysis, representation and understanding, linguistic background and elements of a simple noun phrase
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Design principles and common security related programming principles, principle of least privilege,principle of least common mechanism, trust in the system
socio economic dimensions of Nepal, population of Nepal and its projection, population density of Nepal , Age and sex structure in Nepal, Employee trends in Nepal,Labour Market issues
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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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/
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/
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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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.
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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!
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Natural language processing
1. Natural language Processing
Natural Language Processing (NLP) refers to AI method of
communicating with an intelligent systems using a natural language
such as English.
Processing of Natural Language is required when you want an
intelligent system like robot to perform as per your instructions,
when you want to hear decision from a dialogue based clinical expert
system, etc.
The field of 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
2. Components of NLP
There are two components of NLP as given :
A) Natural Language Understanding ( NLU)
With natural language understanding (NLU), computers can deduce what a
speaker actually means, and not just the words they say.
Understanding involves the following tasks
(1)Mapping the given input in natural language into useful representations.
(2)Analyzing different aspects of the language.
For example, Apple's Siri or Amazon's Alexa performs natural language
understanding work in the context of hearing and deciphering user inputs.
3. B) Natural Language Generation (NLG)
It is the process of producing meaningful phrases and sentences in
the form of natural language from some internal representation.
It involves :
(1) Text planning − It includes retrieving the relevant content from
knowledge base.
(2) Sentence planning − It includes choosing required words, forming
meaningful phrases, setting tone of the sentence.
(3) Text Realization − It is mapping sentence plan into sentence
structure.
NLU is harder than NLG.
4. Difficulties in NLU
NL has an extremely rich form and structure.
It is very ambiguous. There can be different levels of ambiguity −
(1)Lexical ambiguity − It is at very primitive level such as word-level.
For example, “Book”
(2)Syntax Level ambiguity − A sentence can be parsed in different
ways.
For example, “Call me a taxi?”
(3)Referential ambiguity − Referring to something using pronouns. For
example, Rima went to Gauri. She said, “I am tired.” − Exactly who is
tired?
5. NLP Terminology
• Phonology – concerns how words are related to the sounds that
realize them.
• Morphology – concerns how words are constructed from more
basic meaning units called morphemes. A morpheme is the primitive
unit of meaning in a language.
• Syntax – concerns how can be put together to form correct sentences
and determines what structural role each word plays in the sentence
and what phrases are subparts of other phrases.
• Semantics – concerns what words mean and how these meaning
combine in sentences to form sentence meaning. The study of
context-independent meaning.
6. • Pragmatics – concerns how sentences are used in different situations
and how use affects the interpretation of the sentence.
• Discourse – concerns how the immediately preceding sentences
affect the interpretation of the next sentence. For example,
interpreting pronouns and interpreting the temporal aspects of the
information.
• World Knowledge – includes general knowledge about the world.
What each language user must know about the other’s beliefs and
goals.
7. Steps in NLP
• Lexical Analysis − It involves identifying and analyzing the structure of
words. Lexicon of a language means the collection of words and
phrases in a language. Lexical analysis is dividing the whole chunk of
txt 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. The sentence such as “The school
goes to boy” is rejected by English syntactic analyzer.
• 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 and objects in the task domain.
8. • 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.
• 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.