Natural language processing (NLP) is a subfield of artificial intelligence that studies how to process and understand human language, with the ultimate goal of enabling natural communication between humans and computers; it is an interdisciplinary field that draws from computer science, linguistics, psychology and other areas to allow computers to understand, generate and translate between different human languages. NLP techniques include morphology, lexicography, syntax, semantics and discourse analysis to analyze words, sentences and full conversations at different levels of meaning.
Managing Reputation in the Digital Age - Magnus CarterMentor Digital
Magnus welcomes everyone and sets the scene for the valuable and informative talks to follow by talking about how reputation within public sector organisations and charities is very much shaped in three ways; their websites, the media and social media. It is important to have a dialogue with the public, to share information and ensure their support and confidence through a positive image. Magnus demystifies some of the issues you can face and gives some valuable pointers for the future.
NLP
Machine learning
is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Managing Reputation in the Digital Age - Magnus CarterMentor Digital
Magnus welcomes everyone and sets the scene for the valuable and informative talks to follow by talking about how reputation within public sector organisations and charities is very much shaped in three ways; their websites, the media and social media. It is important to have a dialogue with the public, to share information and ensure their support and confidence through a positive image. Magnus demystifies some of the issues you can face and gives some valuable pointers for the future.
NLP
Machine learning
is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Talk by Cheng Niu, Principal NLP Engineer, WeChat Team, Tencent. As one of the biggest social network in the world, WeChat is innovating the way how people acquire the needed information, knowledge and services. In this talk, Cheng Niu presents the chatbot development effort made by WeChat AI team.
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
Provides a basic introduction to Natural Language Processing (NLP), its properties, and some common techniques such as stemming, tokenization, bag-of-words, stripping, and n-grams
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Talk by Cheng Niu, Principal NLP Engineer, WeChat Team, Tencent. As one of the biggest social network in the world, WeChat is innovating the way how people acquire the needed information, knowledge and services. In this talk, Cheng Niu presents the chatbot development effort made by WeChat AI team.
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
Provides a basic introduction to Natural Language Processing (NLP), its properties, and some common techniques such as stemming, tokenization, bag-of-words, stripping, and n-grams
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
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.
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.
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
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.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2. What is NLP?
• “Natural” languages
– English, Mandarin, French, Swahili, Arabic, Nahuatl, ….
– NOT Java, C++, Perl, …
• Ultimate goal: Natural human-to-computer communication
• Sub-field of Artificial Intelligence, but very interdisciplinary
– Computer science, human-computer interaction (HCI), linguistics,
cognitive psychology, speech signal processing (EE), …
• Shall we play a game? (1983)
4. How does NLP work…
• Morphology: What is a word?
• 奧林匹克運動會（希臘語：Ολυμπιακοί Αγώνες，簡稱奧運會或
奧運）是國際奧林匹克委員會主辦的包含多種體育運動項目的國際
性運動會，每四年舉行一次。
• ك
بيوت
ها = “to her houses”
• Lexicography: What does each word mean?
– He plays bass guitar.
– That bass was delicious!
• Syntax: How do the words relate to each other?
– The dog bit the man. ≠ The man bit the dog.
– But in Russian: человек собаку съел = человек съел собаку
5. How does NLP work…
• Semantics: How can we infer meaning from sentences?
– I saw the man on the hill with the telescope.
– The ipod is so small!
– The monitor is so small!
• Discourse: How about across many sentences?
– President Bush met with President-Elect Obama today at the
White House. He welcomed him, and showed him around.
– Who is “he”? Who is “him”? How would a computer figure that
out?
7. Spoken Language Processing
• Speech Recognition
– Automatic dictation, assistance for blind people, indexing
youtube videos, automatic 411, …
• Related things we study…
– How does intonation affect semantic meaning?
– Detecting uncertainty and emotions
– Detecting deception!
• Why is this hard?
– Each speaker has a different voice (male vs female, child
versus older person)
– Many different accents (Scottish, American, non-native
speakers) and ways of speaking
– Conversation: turn taking, interruptions, …
Examples from Prof. Julia Hirschberg’s slides
8. Spoken Language Processing
• Text-to-Speech / Spoken dialog systems
– Call response centers, tutoring systems, …
• Related things we study…
– Making computer voices sound more human
– Making computer speech acts more human-like
10. Machine Translation
• About $10 billion spent annually on human translation
• Hotels in Beijing, China
– 昨天我打电话订的时候艺龙信誓旦旦的保证说是四星级的酒店,住进去
以后一看没,我靠,这在80年代可能算得上是四星的,我要的是368的大床
房,房间只有一个0.5米*1米的小窗户,打开一看,我靠, ...
– Yesterday, I called out when Art Long vowed to ensure that the four-
star hotel, to live in. I see no future, I rely on it in the 80s may be
regarded as a four-star, and I want the big 368-bed Room, the room
is only one 0.5 m * 1-meter small windows, what we can see, I rely
on, ...
– "本人刚从酒店回来，很想发表一下自己的看法。总体印象：位置很好
，价格也不错，但是服务一般或是太一般了，前台接待的水平和效
率 ..."
– "I came back from the hotel, would like to express my own views. The
overall impression: a good location, good prices, but services in
general or too general, the level of the front reception and efficiency
..."
11. Why is machine translation hard?
• Requires both understanding the “from” language and
generating the “to” language.
• How can we teach a computer a “second language”
when it doesn’t even really have a first language?
• Can we do machine translation without solving natural
language understanding and natural language
generation first?
Que hambre tengo yo
What hunger have I
I've got that hunger
I am so hungry
She let the cat out of the bag. Ella deja que el gato fuera de la bolsa
12.
13. Rosetta Stone (not the product)
• Example of “parallel text”: same text in two or more
languages
– Hieroglyphic Egyptian, Demotic Egyptian and classical Greek
• Used to understand hieroglyphic writing system
14. Statistical Machine Translation
• Lots and lots of parallel text
– Learn word-for-word translations
– Learn phrase-for-phrase translations
– Learn syntax and grammar rules?
Taken from Prof. Chris Manning’s slides
15. NLP: Conclusions
• NLP is already used in many systems today
– Indexing words on the web: Segmenting Chinese, tokenizing
English, de-compoundizing German, …
– Calling centers (“Welcome to AT&T…”)
• Many technologies are in use, and still improving
– Machine translation used by soldiers in Iraq (speech to speech
translation?)
– Dictation used by doctors, many professionals
• Lots of awesome research to work on!
– Detecting deception in speech?
– Tracking social networks via documents?
– Can a computer get an 800 on the verbal SAT? (not yet!)
16. NLP @ Columbia
• CS4705 Natural Language Processing
• CS4706 Spoken Language Processing
• CS6998 Search Engine Technology, CS6870 Speech Recognition,
CS6998 Computational Approaches to Emotional Speech, …
• Related to the Artificial Intelligence track
• Professor Kathleen McKeown
• Professor Julia Hirschberg
• Researchers Owen Rambow,
Nizar Habash, Mona Diab,
Rebecca Passonneau (@ CCLS)
• Opportunities for undergrad
research
19. Why is this customer confused?
• A: And, what day in May did you want to travel?
• C: OK, uh, I need to be there for a meeting that’s from the
12th to the 15th.
• Note that client did not answer question.
• Meaning of client’s sentence:
– Meeting
• Start-of-meeting: 12th
• End-of-meeting: 15th
– Doesn’t say anything about flying!!!!!
• How does agent infer client is informing him/her of travel dates?
Examples from Prof. Julia Hirschberg’s slides
20. Question Answering
• How old is Julia Roberts?
• When did the Berlin Wall fall?
• What about something more open-ended?
– Why did the US enter WWII?
– How does the Electoral College work?
• May want to ask questions about non-English, non-text
documents… and get responses back in English text.
