The document describes the key steps in natural language processing including morphological analysis, part-of-speech tagging, lexical processing, syntactic processing, semantic analysis, knowledge representation, discourse analysis, and applications such as machine translation. It outlines techniques for analyzing words, assigning parts of speech, determining word meanings, parsing sentences into syntactic structures, assigning semantic meanings, representing knowledge, analyzing discourse, and translating between languages.
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
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
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 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...
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
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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 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...
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
UNIT V MOBILE PLATFORMS AND APPLICATIONS
Mobile Device Operating Systems – Special Constrains & Requirements – Commercial Mobile Operating Systems – Software Development Kit: iOS, Android, BlackBerry, Windows Phone – M-Commerce – Structure – Pros & Cons – Mobile Payment System – Security Issues.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Sanskrit in Natural Language ProcessingHitesh Joshi
As Sanskrit is most unambiguous language as compare to other natural languages. As stated by Rick Briggs, NASA it is the most suitable language for the computer in natural language processing.
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.
Jarrar: Introduction to Natural Language ProcessingMustafa Jarrar
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
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
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
3. 13.3 Natural Language Generation
The steps in natural language generation are as follows.
Meaning representation
Utterance Planning
Meaning representations for sentences
Sentence Planning and Lexical Choice
Syntactic structures of sentences with lexical choices
Sentence Generation
Morphologically analyzed words
Morphological Generation
Words
13.4 Steps in Language Understanding and Generation
13.4.1 Morphological Analysis
• Analyzing words into their linguistic components (morphemes).
• Morphemes are the smallest meaningful units of language.
cars car+PLU
giving give+PROG
geliyordum gel+PROG+PAST+1SG - I was coming
• Ambiguity: More than one alternatives
flies flyVERB+PROG
flyNOUN+PLU
adam adam+ACC - the man (accusative)
adam+P1SG - my man
ada+P1SG+ACC - my island (accusative)
Version 2 CSE IIT, Kharagpur
4. 13.4.2 Parts-of-Speech (POS) Tagging
• Each word has a part-of-speech tag to describe its category.
• Part-of-speech tag of a word is one of major word groups
(or its subgroups).
– open classes -- noun, verb, adjective, adverb
– closed classes -- prepositions, determiners, conjuctions, pronouns,
particples
• POS Taggers try to find POS tags for the words.
• duck is a verb or noun? (morphological analyzer cannot make decision).
• A POS tagger may make that decision by looking the surrounding words.
– Duck! (verb)
– Duck is delicious for dinner. (noun)
13.4.3 Lexical Processing
• The purpose of lexical processing is to determine meanings of individual words.
• Basic methods is to lookup in a database of meanings – lexicon
• We should also identify non-words such as punctuation marks.
• Word-level ambiguity -- words may have several meanings, and the correct one
cannot be chosen based solely on the word itself.
– bank in English
• Solution -- resolve the ambiguity on the spot by POS tagging (if possible) or pass-
on the ambiguity to the other levels.
13.4.4 Syntactic Processing
• Parsing -- converting a flat input sentence into a hierarchical structure that
corresponds to the units of meaning in the sentence.
• There are different parsing formalisms and algorithms.
• Most formalisms have two main components:
– grammar -- a declarative representation describing the syntactic structure
of sentences in the language.
– parser -- an algorithm that analyzes the input and outputs its structural
representation (its parse) consistent with the grammar specification.
Version 2 CSE IIT, Kharagpur
5. • CFGs are in the center of many of the parsing mechanisms. But they are
complemented by some additional features that make the formalism more suitable
to handle natural languages.
13.4.5 Semantic Analysis
• Assigning meanings to the structures created by syntactic analysis.
• Mapping words and structures to particular domain objects in way consistent with
our knowledge of the world.
• Semantic can play an import role in selecting among competing syntactic analyses
and discarding illogical analyses.
– I robbed the bank -- bank is a river bank or a financial institution
• We have to decide the formalisms which will be used in the meaning
representation.
13.5 Knowledge Representation for NLP
• Which knowledge representation will be used depends on the application --
Machine Translation, Database Query System.
• Requires the choice of representational framework, as well as the specific
meaning vocabulary (what are concepts and relationship between these concepts
-- ontology)
• Must be computationally effective.
• Common representational formalisms:
– first order predicate logic
– conceptual dependency graphs
– semantic networks
– Frame-based representations
13.6 Discourse
• Discourses are collection of coherent sentences (not arbitrary set of sentences)
• Discourses have also hierarchical structures (similar to sentences)
• anaphora resolution -- to resolve referring expression
– Mary bought a book for Kelly. She didn’t like it.
• She refers to Mary or Kelly. -- possibly Kelly
• It refers to what -- book.
– Mary had to lie for Kelly. She didn’t like it.
Version 2 CSE IIT, Kharagpur
6. • Discourse structure may depend on application.
– Monologue
– Dialogue
– Human-Computer Interaction
13.7 Applications of Natural Language Processing
• Machine Translation – Translation between two natural languages.
– See the Babel Fish translations system on Alta Vista.
• Information Retrieval – Web search (uni-lingual or multi-lingual).
• Query Answering/Dialogue – Natural language interface with a database system,
or a dialogue system.
• Report Generation – Generation of reports such as weather reports.
• Some Small Applications –
– Grammar Checking, Spell Checking, Spell Corrector
13.8 Machine Translation
• Machine Translation refers to converting a text in language A into the
corresponding text in language B (or speech).
• Different Machine Translation architectures are:
– interlingua based systems
– transfer based systems
• Challenges are to acquire the required knowledge resources such as mapping rules
and bi-lingual dictionary? By hand or acquire them automatically from corpora.
• Example Based Machine Translation acquires the required knowledge (some of it
or all of it) from corpora.
Version 2 CSE IIT, Kharagpur
7. Questions
1. Consider the following short story:
John went to the diner to eat lunch. He ordered a hamburger. But John wasn't very
hungry so he didn't _nish it. John told the waiter that he wanted a doggy bag. John gave
the waiter a tip. John then went to the hardware store and home.
Each inference below is based on a plausible interpretation of the story. For each
inference, briefly explain whether that inference was primarily based on syntactic,
semantic, pragmatic, discourse, or world knowledge. (Do not answer world knowledge
unless none of the other categories are appropriate.)
(a) John is the person who ordered a hamburger.
(b) John wasn't just stating a fact that he desired a doggy bag, but was requesting that the
waiter bring him a doggy bag.
(c) John went to the hardware store and then went to his house. (As opposed to going to
a hardware store and a hardware home.)
(d) John gave the waiter some money as a gratuity. (As opposed to giving him a
suggestion or hint.)
(e) John was wearing clothes.
2. Identify the thematic role associated with each noun phrase in the sentence below:
Mary went from Utah to Colorado with John by bicycle.
Solutions
1.a. Discourse knowledge. The inference comes from coreference resolution between
John” and “He” in the first and second sentences.
1.b. Pragmatics. Most people would assume that John was making a request of the waiter
and not merely stating a fact, which is a pragmatic issue because it reects the purpose of
John's statement.
1.c. Syntactic knowledge. This inference reflects one syntactic parse: ((hardware store)
and (home)), as opposed to an alternative parse: (hardware (store and home)).
1.d Semantic knowledge. Most people would assume that “tip” means gratuity, as
opposed to other meanings of the word “tip”, such as suggestion or hint.
Version 2 CSE IIT, Kharagpur
8. 1.e. World Knowledge. There is nothing stated in the story that mentions clothes, but in
our culture people virtually always wear clothes when they leave their house. So we
make this assumption.
2. The roles are
agent = Mary
source (from-loc) = Utah
destination (to-loc) = Colorado
co-agent = John
instrument = bicycle
Version 2 CSE IIT, Kharagpur