This document summarizes a seminar on unsupervised deep learning in natural language processing given by Amir Hadifar. The seminar covered topics such as the advantages of learned features from deep learning models over manually designed features, recent progress and applications of combining deep learning and NLP, including word representation models like Word2Vec. It also discussed future work, such as GloVe and models that learn multi-word representations. The seminar evaluated word embedding models on similarity tasks and corpora from different languages.
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Class-oriented programming, as supported by Java, C++ and C#, helps you develop classes for your customer. Object-oriented programming, on the other hand, lets you focus on networks of cooperating objects that work together to create business value.
This talk describes the trygve open-source programming language and its support for real object-oriented programming the way it was envisioned by those who shaped it in its early days. Learn about trygve and maybe even join the community to help evolve it. And if you’re a working developer, some of the ideas carry over into C# and C++.
More on the philosophy and so forth:
* User manual (the intro might help)
* Original “white paper”
* More academic paper
* fulloo web site
* Past version of a similar talk
About the speaker
Jim Coplien is a Certified Scrum Trainer in Denmark and best-selling author, lecturer, and consultant in the areas of software design, object-oriented programming, lean software development process, and agile development. His earlier work was one of the foundations of Scrum and of XP and he is one of the founders of the software pattern discipline. He helps enterprises solve architectural and organisational problems together and challenges people to question practices they do out of habit or popularity, exhorting people to establish empirical and otherwise provable justifications for their practices.
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.
Introduction to NLP with some practical exercises (tokenization, keyword extraction, topic modelling) using Python libraries like NLTK, Gensim and TextBlob, plus a general overview of the field.
In this talk, we will go through a practical approach for remote pair programming adopted for high-latency situations. We will demonstrate remote pair programming with a live example and we will discuss the advantages and usages of the approach.
What Does Conversational Information Access Exactly Mean and How to Evaluate It?krisztianbalog
This talk discusses a set of specific tasks and scenarios related to information access within the vast space that is casually referred to as conversational AI. While most of these problems have been identified in the literature for quite some time now, progress has been limited. Apart from the inherently challenging nature of these problems, the lack of progress, in large part, can be attributed to the shortage of appropriate evaluation methodology and resources. This talk presents some recent work towards filling this gap.
In one line of research, we investigate the presentation of tabular search results in a conversational setting. Instead of generating a static summary of a result table, we complement brief summaries with clues that invite further exploration, thereby taking advantage of the conversational paradigm. One of the main contributions of this study is the development of a test collection using crowdsourcing.
Another line of work focuses on large-scale evaluation of conversational recommender systems via simulated users. Building on the well-established agenda-based simulation framework from dialogue systems research, we develop interaction and preference models specific to the item recommendation scenario. For evaluation, we compare three existing conversational movie recommender systems with both real and simulated users, and observe high correlation between the two means of evaluation.
This talk has been given at the CIIR talk series at the University of Massachusetts Amherst in Jan 2021 as well as at the IR seminar series at the University of Glasgow in March 2021.
English Language Lab enhances the overall English knowledge of the student. Digital Language lab is not a “Spoken English software” Speaking English is one of the components of Language lab software. English Language Lab enhances the overall English knowledge of the student.
A Strategic Approach: GenAI in EducationPeter 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.
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Class-oriented programming, as supported by Java, C++ and C#, helps you develop classes for your customer. Object-oriented programming, on the other hand, lets you focus on networks of cooperating objects that work together to create business value.
This talk describes the trygve open-source programming language and its support for real object-oriented programming the way it was envisioned by those who shaped it in its early days. Learn about trygve and maybe even join the community to help evolve it. And if you’re a working developer, some of the ideas carry over into C# and C++.
More on the philosophy and so forth:
* User manual (the intro might help)
* Original “white paper”
* More academic paper
* fulloo web site
* Past version of a similar talk
About the speaker
Jim Coplien is a Certified Scrum Trainer in Denmark and best-selling author, lecturer, and consultant in the areas of software design, object-oriented programming, lean software development process, and agile development. His earlier work was one of the foundations of Scrum and of XP and he is one of the founders of the software pattern discipline. He helps enterprises solve architectural and organisational problems together and challenges people to question practices they do out of habit or popularity, exhorting people to establish empirical and otherwise provable justifications for their practices.
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.
Introduction to NLP with some practical exercises (tokenization, keyword extraction, topic modelling) using Python libraries like NLTK, Gensim and TextBlob, plus a general overview of the field.
In this talk, we will go through a practical approach for remote pair programming adopted for high-latency situations. We will demonstrate remote pair programming with a live example and we will discuss the advantages and usages of the approach.
What Does Conversational Information Access Exactly Mean and How to Evaluate It?krisztianbalog
This talk discusses a set of specific tasks and scenarios related to information access within the vast space that is casually referred to as conversational AI. While most of these problems have been identified in the literature for quite some time now, progress has been limited. Apart from the inherently challenging nature of these problems, the lack of progress, in large part, can be attributed to the shortage of appropriate evaluation methodology and resources. This talk presents some recent work towards filling this gap.
In one line of research, we investigate the presentation of tabular search results in a conversational setting. Instead of generating a static summary of a result table, we complement brief summaries with clues that invite further exploration, thereby taking advantage of the conversational paradigm. One of the main contributions of this study is the development of a test collection using crowdsourcing.
Another line of work focuses on large-scale evaluation of conversational recommender systems via simulated users. Building on the well-established agenda-based simulation framework from dialogue systems research, we develop interaction and preference models specific to the item recommendation scenario. For evaluation, we compare three existing conversational movie recommender systems with both real and simulated users, and observe high correlation between the two means of evaluation.
This talk has been given at the CIIR talk series at the University of Massachusetts Amherst in Jan 2021 as well as at the IR seminar series at the University of Glasgow in March 2021.
English Language Lab enhances the overall English knowledge of the student. Digital Language lab is not a “Spoken English software” Speaking English is one of the components of Language lab software. English Language Lab enhances the overall English knowledge of the student.
A Strategic Approach: GenAI in EducationPeter 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.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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.
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!
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.
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.
4. مقدمه
•چراااا؟
•“The next step for Deep Learning is natural language understanding…”
•Yann LeCun (Facebook AI Director, Professor in university of New-York)
•“I think that the most exciting areas over the next five years will be really
understanding text and videos…”
•Geoffry Hinton (Google researcher, Professor in university of Toronto)
•Yoshua Bengio
•Michael Jordan
[D.Manning, 2015]
40/4
15. زبانی مدل
P (Dog | I saw a…) // general
P (Dog| a) // bigram
P (Dog| a) = #count(an Dog) /
# count(an)
مشکالت؟؟؟
1) The cat is walking in the bedroom.
2) A dog is running in a room.
40/13
50. منابع
[1] Y. LeCun, Y. Bengio, and G. Hinton “Deep learning.” Nature, 2015.
[2] R. Socher “Deep learning for Natural Language Processing.”. 2016. Presentation.
[3] C. D. Manning “Last Words.” ACL, 2015.
[4] L. Hank, E. McDermott, and A. Senior. "Large scale deep neural network acoustic modeling
with semi-supervised training data for YouTube video transcription." ASRU, 2013.
[5] L. Thang, R. Socher, and C. D. Manning. "Better word representations with recursive neural
networks for morphology." CoNLL, 2013.
[6] S. Bowman, C. Potts, and C. D. Manning. "Recursive neural networks can learn logical
semantics." arXiv, 2014.
[7] O. Levy, Y. Goldberg, “Neural word embedding as implicit Matrix factorization”, NIPS, 2014
40/39
51. منابع
[8] T. Mikolov, et al. "Efficient estimation of word representations in vector space." arXiv, 2013.
[9] T. Mikolov, et al. "Distributed representations of words and phrases and their
compositionality." NIPS, 2013.
[10] J. Pennington, et al. "Glove: Global Vectors for Word Representation." EMNLP, 2014.
[11] R. Kiros, et al. "Skip-thought vectors." NIPS, 2015.
[12] Y. Wu, et.al, “Google ’ s Neural Machine Translation System : Bridging the Gap between
Human and Machine Translation,” arXiv, 2016.
[13] Q. Lee, T.Mikolov, et.al, “Distributed Representations of Sentences and Documents,” arXiv,
2014.
[14] O. Levy, Y. Goldberg “Dependency-based word embeddings" ACL, 2014.
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