Powerpoint Presentation for the short story assignment.
Master's In Data Science at San Jose State University
CMPE-258
Image references: https://www.einfochips.com/
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
LEPOR: an augmented machine translation evaluation metric - Thesis PPT Lifeng (Aaron) Han
Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicateand apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks.
In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages. Firstly, we design augmented factors to yield highly accurate evaluation.Secondly, we design a tunable evaluation model where weighting of factors can be optimized according to the characteristics of languages. Thirdly, in the enhanced version of our methods, we design concise linguistic feature using POS to show that our methods can yield even higher performance when using some external linguistic resources. Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.
Machine Learning Techniques with Ontology for Subjective Answer Evaluationijnlc
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy.
Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
LEPOR: an augmented machine translation evaluation metric - Thesis PPT Lifeng (Aaron) Han
Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicateand apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks.
In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages. Firstly, we design augmented factors to yield highly accurate evaluation.Secondly, we design a tunable evaluation model where weighting of factors can be optimized according to the characteristics of languages. Thirdly, in the enhanced version of our methods, we design concise linguistic feature using POS to show that our methods can yield even higher performance when using some external linguistic resources. Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.
This is a poster I did to show my internship work for a computing symposium. The content is about medical document classification using neural network.
Meta-evaluation of machine translation evaluation methodsLifeng (Aaron) Han
Cite: Lifeng Han. 2021. Meta-evaluation of machine translation evaluation methods. In Metrics2021 Tutorial Track/type: Workshop on Informetric and Scientometric Research (SIG-MET), ASIS&T. October 23–24.
THE EFFECTS OF THE LDA TOPIC MODEL ON SENTIMENT CLASSIFICATIONijscai
Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features.
The methods of exploratory testing has gained significant attention in industry and research in the last years. However, as many “buzzword" technologies, the introduction and application of exploratory testing is not straightforward. Exploratory testing it is not only black or white - scripted or exploratory - but also all shades of grey in between. Within the EASE industrial excellence center, we have executed an industrial workshop on exploratory testing, that helps providing understanding of how to choose feasible levels of exploration in exploratory testing. We will present the concepts of levels of exploration in exploratory testing, the outcomes of the workshop, along with relevant empirical research findings on exploratory testing.
Natural Language Processing in Artificial Intelligence - Codeup #5 - PayU Artivatic.ai
This is workshop presentation for usages for NLP in Artificial Intelligence.
This is prepared by Artivatic Data Labs.
For more info for the detailed product, visit at www.artivatic.com
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
[DSC MENA 24] Nada_GabAllah_-_Advancement_in_NLP_and_Text_Analytics.pptxDataScienceConferenc1
In recent years, NLP and text analytics have witnessed remarkable progress, transforming the way we interact with language data. From sentiment analysis to named entity recognition, these techniques play a pivotal role in understanding and extracting valuable insights from vast amounts of unstructured text. In this session, we’ll delve into the latest advancements, explore state-of-the-art models, and discuss practical applications across domains such as healthcare, finance, and customer service. Join us to unravel the intricacies of NLP and discover how it empowers organizations to unlock the hidden potential of textual information.
This is a poster I did to show my internship work for a computing symposium. The content is about medical document classification using neural network.
Meta-evaluation of machine translation evaluation methodsLifeng (Aaron) Han
Cite: Lifeng Han. 2021. Meta-evaluation of machine translation evaluation methods. In Metrics2021 Tutorial Track/type: Workshop on Informetric and Scientometric Research (SIG-MET), ASIS&T. October 23–24.
THE EFFECTS OF THE LDA TOPIC MODEL ON SENTIMENT CLASSIFICATIONijscai
Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features.
The methods of exploratory testing has gained significant attention in industry and research in the last years. However, as many “buzzword" technologies, the introduction and application of exploratory testing is not straightforward. Exploratory testing it is not only black or white - scripted or exploratory - but also all shades of grey in between. Within the EASE industrial excellence center, we have executed an industrial workshop on exploratory testing, that helps providing understanding of how to choose feasible levels of exploration in exploratory testing. We will present the concepts of levels of exploration in exploratory testing, the outcomes of the workshop, along with relevant empirical research findings on exploratory testing.
Natural Language Processing in Artificial Intelligence - Codeup #5 - PayU Artivatic.ai
This is workshop presentation for usages for NLP in Artificial Intelligence.
This is prepared by Artivatic Data Labs.
For more info for the detailed product, visit at www.artivatic.com
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
[DSC MENA 24] Nada_GabAllah_-_Advancement_in_NLP_and_Text_Analytics.pptxDataScienceConferenc1
In recent years, NLP and text analytics have witnessed remarkable progress, transforming the way we interact with language data. From sentiment analysis to named entity recognition, these techniques play a pivotal role in understanding and extracting valuable insights from vast amounts of unstructured text. In this session, we’ll delve into the latest advancements, explore state-of-the-art models, and discuss practical applications across domains such as healthcare, finance, and customer service. Join us to unravel the intricacies of NLP and discover how it empowers organizations to unlock the hidden potential of textual information.
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses a range of techniques and technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful.
https://hiretopwriters.com/
INTRODUCTION TO Natural language processingsocarem879
Natural language processing (NLP) is a machine learning technology that gives computers the ability to
interpret, manipulate, and comprehend human language.
•Ex: Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers
• We have large volumes of voice and text data from various communication channels like emails, text
messages, social media newsfeeds, video, audio, and more.
• They use NLP software to automatically process this data, analyze the intent or sentiment in the
message, and respond in real time to human communication
• When text mining and machine learning are combined, automated text analysis becomes possible
PREPROCESSING STEPS IN NLP
• Data preprocessing involves preparing and cleaning text data so that machines can analyze it. This
can be done in following:
• Tokenization. It substitutes sensitive information with nonsensitive information, or a token.
Tokenization is often used in payment transactions to protect credit card data.
• Stop word removal. Common words are removed from the text, so unique words that offer the most
information about the text remain.
• Lemmatization and stemming. Lemmatization groups together different inflected versions of the
same word. For example, the word "walking" would be reduced to its root form, or stem, "walk" to
process.
• Part-of-speech tagging. Words are tagged based on which part of speech they correspond to -- such
as nouns, verbs or adjectives
As a member of TAUS, Lingosail provides language technologies for LSPs, especially in the intellectual property industry. Aiming at Human-Machine Cooperation we have developed and deployed MT systems. Currently we use MT mainly in three scenarios: post-editing production, terminology extraction and cross-language information retrieval. This presentation will show some of our experiences in these use cases, and some of our expectations for MT's feature utilities.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
NLP and its application in Insurance -Short story presentation
1. Natural Language Processing and its applications in Insurance :
A Survey
Stuti Agarwal
Student ID: 015229994
email: stuti.agarwal@sjsu.edu
2. Data forms
Data is present in various forms :
• Text data
• Tabular data
• Visual Data
Textual data is most abundantly available
data and is most unstructured.
3. NLP(Natural Language Processing) and Text Mining
• NLP can be considered as mimicking a normal human text
reading process that uses text mining and also captures all the
language complexities and intricacies and processes it into a
machine learnable form
• Text mining mainly refers to text manipulation techniques or
algorithms
4. NLP in Insurance industry
• Marketing: It is also beneficial to extract public feedback to extract the
expected trends. So doing sentiment analysis on comments or feedback
tweets is a common practice
• Underwriting: When re-insurance happens the underwriters have to analyze
every aspect of the policy at renewal periods and digitization of contracts has
definitely helped the text mining applications to easily keep track of any
changes, thus providing more transparency to the company and policyholder
on policy coverage areas
• Claims: With NLP we can classify the claim type and route the claim to the
respective department.
5. NLP in Insurance industry
• Reserving: NLP helps in easing the process of assessing the expert reports
that are generated few times annually which helps in anticipating the
development of the claim and estimate the expected costs better.
• Prevention: NLP is extremely useful in the field of medical diagnostics. If
diseases are mapped correctly to their symptoms it increases the chances of
patient survival and early treatment.
7. NLP in practice from pre-processing to production
• Pre-Processing: Steps involved
• Conversion to lower case to remove ambiguity
• Stopwords removal: helps in focussing words that really matter
• Processing of special characters
• Tokenization
8. NLP in practice from pre-processing to production
word level processing
9. NLP in practice from pre-processing to production
• Text Embedding:
• Machines understand and learn through numbers . Method of
converting words into numerical vectors is called text embedding
• From the built dictionary of available vocabulary, the rank for each
pulled word or sentence is referenced called tokens
• After tokenization of words into dictionary indexes, context and
semantics are taken into consideration.
• Word2Vec remains the most popular text embedding lib
11. NLP in practice from pre-processing to production
Tokenization
12. NLP in practice from pre-processing to production
• Feature Engineering:
• Creation of new features called derived features
• Technique of data augmentation
• Character interpretation: like symbols and emojis
13. NLP in practice from pre-processing to production
• Modelling:
• As soon as the numerical vectors are ready we can use the data for training the model
• BERT can be used for both data preparation and modelling both
• Test and model evaluation :
• A separate dataset is used to test the model with a similar feature set. And to quantify the
model various metrics are used depending on the task achieved
14. Other approaches in word dependency
• Attention:
• To draw the relationships between the
elements of a sequence an attention matrix
is drawn to show how much influence one
element has over the other one. These are
neural network layers.
15. Other approaches in word dependency
• Transformer Neural Network:
• This has an encoder-decoder architecture.
It uses attention and feed-forward neural
networks on top of encoder and decoder to
improve the efficiency and calculation time
for sequential problems
16. Other approaches in word dependency
• BERT:(Bidirectional Encoder
Representation for
Transformers):
• There are two tasks in the BERT model
training pre-training and fine-tuning and
they use Transformers Neural Networks.
17. Challenges:
Although Textual data is hugely available it also
corruptable and gathering this data from all possible
resources is a challenge
18. Conclusion:
• Digitization accessing text data has become easier
due to the presence of underlying databases,
websites, APIs, RSS feeds, etc.
• Tremendous research has been happening in the
NLP areas in the past decade and numerous
techniques and models have been revolutionizing the
text mining process.
• BERT remains a state-of-the-art model when it comes
to NLP models in these times for its ability to carry
multiple NLP tasks.
19. Referenced Research paper:
• Antoine Ly, Benno Uthayasooriyar & Tingting Wang: A SURVEY ON
NATURAL LANGUAGE PROCESSING (NLP) & APPLICATIONS IN
INSURANCE