This course explores lexical semantics from computational, linguistic and philosophical perspectives. It covers theories of word meaning, differences in semantic structure between languages, and the relationship between word meaning and sentence meaning. Students will learn about primitives of meaning, semantic annotation, ontologies, and automated methods for acquiring semantic knowledge at scale. The course consists of 5 modules taught by different professors on topics like computational lexical semantics, deep semantics, ontologies, linguistic issues, and semantic annotation. There will be 4 assignments worth 25% each.
Latent Topic-semantic Indexing based Automatic Text SummarizationElaheh Barati
Automatic summarization, a difficult but pressing problem in natural language processing, aims at shortening source documents while retaining main information. In recent years, more statistical machine learning methods have been applied to automatic summarization. In this paper, we propose a novel approach for summarization, based on hierarchical Bayesian model of topic-semantic indexing (TSI) and extraction strategy of average log-likelihood. The new method is tested on Brown corpus, and its performance is analyzed by a well-designed blind experiment of one-way ANOVA on human reviews. The experimental results show that TSI model is promising on topic- driven summarization.
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
Latent Topic-semantic Indexing based Automatic Text SummarizationElaheh Barati
Automatic summarization, a difficult but pressing problem in natural language processing, aims at shortening source documents while retaining main information. In recent years, more statistical machine learning methods have been applied to automatic summarization. In this paper, we propose a novel approach for summarization, based on hierarchical Bayesian model of topic-semantic indexing (TSI) and extraction strategy of average log-likelihood. The new method is tested on Brown corpus, and its performance is analyzed by a well-designed blind experiment of one-way ANOVA on human reviews. The experimental results show that TSI model is promising on topic- driven summarization.
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
A \"sentence pattern\" in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent architecture for extracting from sentences more sophisticated patterns than n-grams. In this architecture a \"sentence pattern\" is considered as n-element ordered combination of sentence elements. Experiments showed that the method extracts significantly more frequent patterns than the usual n-gram approach.
PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabili...Rommel Carvalho
Presentation given by Saminda Abeyruwan at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in November 7, 2010.
Paper: PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods
Abstract: Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
review of "Learning to Understand Phrases by Embedding the Dictionary" by Felix Hill, Kyunghyun Cho, Anna Korhonen, Yoshua Bengio
at KTH's Deep Learning reading group:
www.csc.kth.se/cvap/cvg/rg/
Taking into account communities of practice’s specific vocabularies in inform...inscit2006
L. Damas and C. Million-Rousseau
Condillac Group, LISTIC, Université de Savoie. 73370 Le Bourget du Lac, France
Ontologos Corp. 6, route de Nanfray, 74000 Cran-Gevrier, France
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora
resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a
subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora
resolution into many applications like automatic summarization, opinion mining, machine translation,
question answering systems etc. increase their accuracy by 10%. Related work in this field has been done
in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed
for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model
uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass
approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted
on a Punjabi story containing more than 1000 words and result is drawn with the future directions.
PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabili...Rommel Carvalho
Presentation given by Saminda Abeyruwan at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in November 7, 2010.
Paper: PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods
Abstract: Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
review of "Learning to Understand Phrases by Embedding the Dictionary" by Felix Hill, Kyunghyun Cho, Anna Korhonen, Yoshua Bengio
at KTH's Deep Learning reading group:
www.csc.kth.se/cvap/cvg/rg/
Taking into account communities of practice’s specific vocabularies in inform...inscit2006
L. Damas and C. Million-Rousseau
Condillac Group, LISTIC, Université de Savoie. 73370 Le Bourget du Lac, France
Ontologos Corp. 6, route de Nanfray, 74000 Cran-Gevrier, France
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora
resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a
subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora
resolution into many applications like automatic summarization, opinion mining, machine translation,
question answering systems etc. increase their accuracy by 10%. Related work in this field has been done
in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed
for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model
uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass
approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted
on a Punjabi story containing more than 1000 words and result is drawn with the future directions.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP