Slides for my tutorial at the ESWC Summer School 2015, giving an introduction to information extraction with Linked Data and an introduction to one of the applications of information extraction, opinion mining.
Compare and contrast RDF triple stores and NoSQL: are triples stores NoSQL or not?
Talk given 2011-09-08 tot he BigData/NoSQL meetup at Bristol University.
TechTalk #15 Grokking: The data processing journey at AhaMoveGrokking VN
Thuc Nguyen - Lead Operation Engineer, AhaMove
Thuc Nguyen is working at AhaMove - an 1.5 year old instant delivery startup - as Lead Operation Engineer.
The topic is to share data problems which AhaMove had to deal with on early days, and how our engineering culture and philosophy affected our decisions and actions on handling these issues with detailed showcases on geospatial analytics and data pipeline.
Slides for my tutorial at the ESWC Summer School 2015, giving an introduction to information extraction with Linked Data and an introduction to one of the applications of information extraction, opinion mining.
Compare and contrast RDF triple stores and NoSQL: are triples stores NoSQL or not?
Talk given 2011-09-08 tot he BigData/NoSQL meetup at Bristol University.
TechTalk #15 Grokking: The data processing journey at AhaMoveGrokking VN
Thuc Nguyen - Lead Operation Engineer, AhaMove
Thuc Nguyen is working at AhaMove - an 1.5 year old instant delivery startup - as Lead Operation Engineer.
The topic is to share data problems which AhaMove had to deal with on early days, and how our engineering culture and philosophy affected our decisions and actions on handling these issues with detailed showcases on geospatial analytics and data pipeline.
Presentation of work that will be published at EMNLP 2016.
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. emoji2vec: Learning Emoji Representations from their Description. SocialNLP at EMNLP 2016. https://arxiv.org/abs/1609.08359
Georgios Spithourakis, Isabelle Augenstein, Sebastian Riedel. Numerically Grounded Language Models for Semantic Error Correction. EMNLP 2016. https://arxiv.org/abs/1608.04147
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva. Stance Detection with Bidirectional Conditional Encoding. EMNLP 2016. https://arxiv.org/abs/1606.05464
USFD at SemEval-2016 - Stance Detection on Twitter with AutoencodersIsabelle Augenstein
This paper describes the University of Sheffield's submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is to classify the stance of a tweet towards a target as "favor", "against", or "none". In Subtask B, the targets in the test data are different from the targets in the training data, thus rendering the task more challenging but also more realistic.
To address the lack of target-specific training data, we use a large set of unlabelled tweets containing all targets and train a bag-of-words autoencoder to learn how to produce feature representations of tweets. These feature representations are then used to train a logistic regression classifier on labelled tweets, with additional features such as an indicator of whether the target is contained in the tweet. Our submitted run on the test data achieved an F1 of 0.3270.
Paper: http://isabelleaugenstein.github.io/papers/SemEval2016-Stance.pdf
Invited talk in Heriot-Watt University Computer Science seminar series about our EMNLP paper on extracting non-standard relations from the Web with Distant Supervision and Imitation Learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf
View the EMNLP poster here: http://www.slideshare.net/isabelleaugenstein/extracting-relations-between-nonstandard-entities-using-distant-supervision-and-imitation-learning
Relation Extraction from the Web using Distant SupervisionIsabelle Augenstein
Slides of my presentation on "Relation Extraction from the Web using Distant Supervision" at EKAW 2014. Download link for the paper: http://staffwww.dcs.shef.ac.uk/people/I.Augenstein/EKAW2014-Relation.pdf
Seed Selection for Distantly Supervised Web-Based Relation ExtractionIsabelle Augenstein
Slides of my presentation on "Seed Selection for Distantly Supervised Web-Based Relation Extraction" at the Semantic Web and Information Extraction workshop (SWAIE) and COLING 2014
Download link for the paper: http://staffwww.dcs.shef.ac.uk/people/I.Augenstein/SWAIE2014-Seed.pdf
Running Natural Language Queries on MongoDBMongoDB
One of the most sought-after features of any user centric web application is the search functionality. QBurst revamped the search interface by using NLP and integrating with MongoDB. Their solution is designed to identify key components and operators within a natural language query and use it against MongoDB to extract records. This session explains QBurst's technical solution in detail.
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology
Introduction to natural language generation with artificial neural networks (ANNs) and a group poetry writing exercise where humans pretend to be neurons in an ANN.
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Presentation of work that will be published at EMNLP 2016.
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, Sebastian Riedel. emoji2vec: Learning Emoji Representations from their Description. SocialNLP at EMNLP 2016. https://arxiv.org/abs/1609.08359
Georgios Spithourakis, Isabelle Augenstein, Sebastian Riedel. Numerically Grounded Language Models for Semantic Error Correction. EMNLP 2016. https://arxiv.org/abs/1608.04147
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva. Stance Detection with Bidirectional Conditional Encoding. EMNLP 2016. https://arxiv.org/abs/1606.05464
USFD at SemEval-2016 - Stance Detection on Twitter with AutoencodersIsabelle Augenstein
This paper describes the University of Sheffield's submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is to classify the stance of a tweet towards a target as "favor", "against", or "none". In Subtask B, the targets in the test data are different from the targets in the training data, thus rendering the task more challenging but also more realistic.
To address the lack of target-specific training data, we use a large set of unlabelled tweets containing all targets and train a bag-of-words autoencoder to learn how to produce feature representations of tweets. These feature representations are then used to train a logistic regression classifier on labelled tweets, with additional features such as an indicator of whether the target is contained in the tweet. Our submitted run on the test data achieved an F1 of 0.3270.
Paper: http://isabelleaugenstein.github.io/papers/SemEval2016-Stance.pdf
Invited talk in Heriot-Watt University Computer Science seminar series about our EMNLP paper on extracting non-standard relations from the Web with Distant Supervision and Imitation Learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf
View the EMNLP poster here: http://www.slideshare.net/isabelleaugenstein/extracting-relations-between-nonstandard-entities-using-distant-supervision-and-imitation-learning
Relation Extraction from the Web using Distant SupervisionIsabelle Augenstein
Slides of my presentation on "Relation Extraction from the Web using Distant Supervision" at EKAW 2014. Download link for the paper: http://staffwww.dcs.shef.ac.uk/people/I.Augenstein/EKAW2014-Relation.pdf
Seed Selection for Distantly Supervised Web-Based Relation ExtractionIsabelle Augenstein
Slides of my presentation on "Seed Selection for Distantly Supervised Web-Based Relation Extraction" at the Semantic Web and Information Extraction workshop (SWAIE) and COLING 2014
Download link for the paper: http://staffwww.dcs.shef.ac.uk/people/I.Augenstein/SWAIE2014-Seed.pdf
Running Natural Language Queries on MongoDBMongoDB
One of the most sought-after features of any user centric web application is the search functionality. QBurst revamped the search interface by using NLP and integrating with MongoDB. Their solution is designed to identify key components and operators within a natural language query and use it against MongoDB to extract records. This session explains QBurst's technical solution in detail.
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology
Introduction to natural language generation with artificial neural networks (ANNs) and a group poetry writing exercise where humans pretend to be neurons in an ANN.
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Cultivating a Community-Oriented OrganizationTim Falls
A talk I delivered at CMX Summit West 2015 in San Francisco.
CMX is a community of community professionals, and this talk was about community. Community?
Essay Writing Co Uk. Buy Essay Uk Orders, Buy ready essayNoel Brooks
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Bibliotheca Digitalis. Reconstitution of Early Modern Cultural Networks. From Primary Source to Data. DARIAH / Biblissima Summer School, 4-8 July 2017, Le Mans, France.
2nd day, July 5th – Establishing Prosopographical data.
What’s in a Name: Text and Image for indexing Prosopographical data.
Eduard Frunzeanu,
Régis Robineau.
Research engineers, Equipex Biblissima, Campus Condorcet.
Abstract: https://bvh.hypotheses.org/3310#conf-EFrunzeanu-RRobineau
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What’s in a Name? Text and Image for Indexing Prosopographical DataEquipex Biblissima
Presentation by Eduard Frunzeanu and Régis Robineau at the Summer School “Reconstitution of Early Modern Cultural Networks. From Primary Source to Data” (Médiathèque Louis-Aragon, Le Mans, France), organized with the support of Humanities at Scale (DARIAH-EU) and the City of Le Mans, and in partnership with Biblissima and the Centre d’Études Supérieures de la Renaissance (Tours).
Similar to Natural Language Processing for the Semantic Web (14)
Beyond Fact Checking — Modelling Information Change in Scientific CommunicationIsabelle Augenstein
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible — e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. In this talk, I will present some first steps towards addressing these problems, discussing our research on exaggeration detection, scientific fact checking, and on modelling information change in scientific communication more broadly.
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. In this talk, I will present some first steps towards addressing these problems, discussing our research on exaggeration detection of scientific claims and on scientific fact checking.
The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, a knowledge-intensive and complex reasoning task. Most existing fact checking models predict a claim’s veracity with black-box models, which often lack explanations of the reasons behind their predictions and contain hidden vulnerabilities. The lack of transparency in fact checking systems and ML models, in general, has been exacerbated by increased model size and by “the right…to obtain an explanation of the decision reached” enshrined in European law. This talk presents some first solutions to generating explanations for fact checking models. It then examines how to assess the generated explanations using diagnostic properties, and how further optimising for these diagnostic properties can improve the quality of the generating explanations. Finally, the talk examines how to systemically reveal vulnerabilities of black-box fact checking models.
Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -- e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.
Towards Explainable Fact Checking (DIKU Business Club presentation)Isabelle Augenstein
Outline:
- Fact checking – what is it and why do we need it?
- False information online
- Content-based automatic fact checking
- Explainability – what is it and why do we need it?
- Making the right predictions for the right reasons
- Model training pipeline
- Explainable fact checking – some first solutions
- Rationale selection
- Generating free-text explanations
- Wrap-up
Tutorial on 'Explainability for NLP' given at the first ALPS (Advanced Language Processing) winter school: http://lig-alps.imag.fr/index.php/schedule/
The talk introduces the concepts of 'model understanding' as well as 'decision understanding' and provides examples of approaches from the areas of fact checking and text classification.
Exercises to go with the tutorial are available here: https://github.com/copenlu/ALPS_2021
Automatic fact checking is one of the more involved NLP tasks currently researched: not only does it require sentence understanding, but also an understanding of how claims relate to evidence documents and world knowledge. Moreover, there is still no common understanding in the automatic fact checking community of how the subtasks of fact checking — claim check-worthiness detection, evidence retrieval, veracity prediction — should be framed. This is partly owing to the complexity of the task, despite efforts to formalise the task of fact checking through the development of benchmark datasets.
The first part of the talk will be on automatically generating textual explanations for fact checking, thereby exposing some of the reasoning processes these models follow. The second part of the talk will be on re-examining how claim check-worthiness is defined, and how check-worthy claims can be detected; followed by how to automatically generate claims which are hard to fact-check automatically.
Talk on 'Tracking False Information Online' at W-NUT workshop at EMNLP 2019.
=========
Digital media enables fast sharing of information and discussions among users. While this comes with many benefits to today’s society, such as broadening information access, the manner in which information is disseminated also has obvious downsides. Since fast access to information is expected by many users and news outlets are often under financial pressure, speedy access often comes at the expense of accuracy, which leads to misinformation. Moreover, digital media can be misused by campaigns to intentionally spread false information, i.e. disinformation, about events, individuals or governments. In this talk, I will present on different ways false information is spread online, including misinformation and disinformation. I will then report findings from our recent and ongoing work on automatic fact checking, stance detection and framing attitudes.
What can typological knowledge bases and language representations tell us abo...Isabelle Augenstein
One of the core challenges in typology is to record properties of languages in a structured way. As a result of manual efforts, typological knowledge bases have emerged, which contains information about languages’ phonological, morphological and syntactic properties; as well as information about language families. Ideally, such typological knowledge bases would provide useful information for multilingual NLP models to learn how to selectively share parameters.
A related area of research suggests a different way of encoding properties of languages, namely to learn language representation vectors directly from text documents.
In this talk, I will analyse and contrast these two ways of encoding linguistic properties, as well as present research on how the two can benefit one another.
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate ...Isabelle Augenstein
Paper presented at NAACL 2018. Link: https://arxiv.org/abs/1802.09913
Abstract:
============
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for topic-based sentiment analysis.
Learning with limited labelled data in NLP: multi-task learning and beyondIsabelle Augenstein
When labelled training data for certain NLP tasks or languages is not readily available, different approaches exist to leverage other resources for the training of machine learning models. Those are commonly either instances from a related task or unlabelled data.
An approach that has been found to work particularly well when only limited training data is available is multi-task learning.
There, a model learns from examples of multiple related tasks at the same time by sharing hidden layers between tasks, and can therefore benefit from a larger overall number of training instances and extend the models' generalisation performance. In the related paradigm of semi-supervised learning, unlabelled data as well as labelled data for related tasks can be easily utilised by transferring labels from labelled instances to unlabelled ones in order to essentially extend the training dataset.
In this talk, I will present my recent and ongoing work in the space of learning with limited labelled data in NLP, including our NAACL 2018 papers 'Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces [1] and 'From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings’ [2].
[1] https://t.co/A5jHhFWrdw
[2] https://arxiv.org/abs/1802.09375
==========
Bio from my website http://isabelleaugenstein.github.io/index.html:
I am a tenure-track assistant professor at the University of Copenhagen, Department of Computer Science since July 2017, affiliated with the CoAStAL NLP group and work in the general areas of Statistical Natural Language Processing and Machine Learning. My main research interests are weakly supervised and low-resource learning with applications including information extraction, machine reading and fact checking.
Before starting a faculty position, I was a postdoctoral research associate in Sebastian Riedel's UCL Machine Reading group, mainly investigating machine reading from scientific articles. Prior to that, I was a Research Associate in the Sheffield NLP group, a PhD Student in the University of Sheffield Computer Science department, a Research Assistant at AIFB, Karlsruhe Institute of Technology and a Computational Linguistics undergraduate student at the Department of Computational Linguistics, Heidelberg University.
Spreading of mis- and disinformation is growing and is having a big impact on interpersonal communications, politics and even science.
Traditional methods, e.g. manual fact-checking by reporters cannot keep up with the growth of information. On the other hand, there has been much progress in natural language processing recently, partly due to the resurgence of neural methods.
How can natural language processing methods fill this gap and help to automatically check facts?
This talk will explore different ways to frame fact checking and detail our ongoing work on learning to encode documents for automated fact checking, as well as describe future challenges.
SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Sc...Isabelle Augenstein
Shared task summary for SemEval 2017 Task 10: ScienceIE – Extracting Keyphrases and Relations from Scientific Publications
Paper: https://arxiv.org/abs/1704.02853
Abstract:
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.
Extracting Relations between Non-Standard Entities using Distant Supervision ...Isabelle Augenstein
Poster for our EMNLP paper on extracting non-standard relations from the Web with distant supervision and imitation learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
1. Natural Language Processing for the
Semantic Web
Isabelle Augenstein
Department of Computer Science, University of Sheffield, UK
i.augenstein@sheffield.ac.uk
Linked Data for NLP Tutorial, ESWC Summer School 2014
August 24, 2014
3. Why Natural Language Processing? 3
semi-structured information
Isabelle Augenstein
unstructured information
4. Why Natural Language Processing? 4
semi-structured information
Isabelle Augenstein
unstructured information
How to link this information to a
knowledge base automatically?
5. Why Natural Language Processing? 5
semi-structured information
Isabelle Augenstein
unstructured information
How to link this information to a
knowledge base automatically?
Information Extraction!
6. Information Extraction 6
Between AD 1400 and 1450, China was a global superpower run
by one family – the Ming dynasty – who established Beijing as the
capital and built the Forbidden City.
Isabelle Augenstein
7. Named Entities 7
Between AD 1400 and 1450, China was a global superpower run
by one family – the Ming dynasty – who established Beijing as the
capital and built the Forbidden City.
Named Entity Recognition
Isabelle Augenstein
8. Named Entities 8
Between AD 1400 and 1450, China was a global superpower run
by one family – the Ming dynasty – who established Beijing as the
capital and built the Forbidden City.
Named Entity Recognition (NER)
Named Entity Classification (NEC):
China: /location/country
Ming dynasty: /royalty/royal_line
Beijing: /location/city
Forbidden City: /location/city
Isabelle Augenstein
9. Named Entities 9
Between AD 1400 and 1450, China was a global superpower run
by one family – the Ming dynasty – who established Beijing as the
capital and built the Forbidden City.
Named Entity Recognition
Named Entity Classification: Named Entity Linking:
China: /location/country China: /m/0d05w3
Ming dynasty: /royalty/royal_line Ming dynasty: /m/0bw_m
Beijing: /location/city Beijing: /m/01914
Forbidden City: /location/city Forbidden City: /m/0j0b2
Isabelle Augenstein
10. Named Entities: Definition 10
Named Entities: Proper nouns, which refer to real-life entities
Named Entity Recognition: Detecting boundaries of named
entities (NEs)
Named Entity Classification: Assigning classes to NEs, such as
PERSON, LOCATION, ORGANISATION, or fine-grained classes
such as ROYAL LINE
Named Entity Linking / Disambiguation: Linking NEs to
concrete entries in knowledge base, example:
China -> LOCATION: Republic of China, country in East Asia
-> LOCATION: China proper, core region of China during Qing dynasty
-> LOCATION: China, Texas
-> PERSON: China, Brazilian footballer born in 1964
-> MUSIC: China, a 1979 album by Vangelis
-> …
11. Relations 11
Between AD 1400 and 1450, China was a global
superpower run by one family – the Ming dynasty – who
established Beijing as the capital and built the Forbidden City.
Named Entity Recognition
Relation Extraction
Isabelle Augenstein
/royalty/royal_line/kingdom_s_ruled
/location/country/capital /location/location/containedby
12. Relations and Time Expressions 12
Between AD 1400 and 1450, China was a global
1400-XX-XX -- 1450-XX-XX
superpower run by one family – the Ming dynasty – who
established Beijing as the capital and built the Forbidden City.
Named Entity Recognition
Relation Extraction
Temporal Extraction
Isabelle Augenstein
/royalty/royal_line/kingdom_s_ruled
/location/country/capital /location/location/containedby
13. Relations, Time Expressions 13
and Events
Between AD 1400 and 1450, China was a global
1400-XX-XX -- 1450-XX-XX
superpower run by one family – the Ming dynasty – who
established Beijing as the capital and built the Forbidden City.
Named Entity Recognition
Relation Extraction
Temporal Extraction
Isabelle Augenstein
/royalty/royal_line/kingdom_s_ruled
/location/country/capital /location/location/containedby
Event Extraction
Event: /royalty/royal_line: Ming dynasty
/royalty/royal_line/ruled_from: 1400-XX-XX
/royalty/royal_line/ruled_to: 1450-XX-XX
/royalty/royal_line/kingdom_s_ruled: China
14. Relations, Time Expressions 14
and Events: Definition
Relations: Two or more entities which relate to one another in
real life
Relation Extraction: Detecting relations between entities and
assigning relation types to them, such as CAPITAL-OF
Temporal Extraction: Recognising and normalising time
expressions: times (e.g. “3 in the afternoon”), dates (“tomorrow”),
durations (“since yesterday”), and sets (e.g. “twice a month”)
Events: Real-life events that happened at some point in space
and time, e.g. kingdom, assassination, exhibition
Event Extraction: Extracting events consisting of the name and
type of event, time and location
15. Summary: Introduction 15
• Information extraction (IE) methods such as named entity
recognition (NER), named entity classification (NEC), named
entity linking, relation extraction (RE), temporal extraction, and
event extraction can help to add markup to Web pages
• Information extraction approaches can serve two purposes:
• Annotating every single mention of an entity, relation or event,
e.g. to add markup to Web pages
• Aggregating those mentions to populate knowledge bases, e.g.
based on confidence values and majority voting
China LOCATION 0.9
China LOCATION 0.8
China PERSON 0.4
à China LOCATION
Isabelle Augenstein
16. Information Extraction: Methods 16
• Focus of the rest of the tutorial and hands-on session:
Named entity recognition and classification (NERC)
• Possible methodologies
• Rule-based approaches: write manual extraction rules
• Machine learning based approaches
• Supervised learning: manually annotate text, train machine
learning model
• Unsupervised learning: extract language patterns, cluster similar
ones
• Semi-supervised learning: start with a small number of language
patterns, iteratively learn more (bootstrapping)
• Gazetteer-based method: use existing list of named entities
• Combination of the above
Isabelle Augenstein
18. Information Extraction: Methods 18
Developing a NERC involves programming based around APIs..
which can be frustrating at times
Isabelle Augenstein
20. Background: Linguistics 20
Acoustic signal
Phonemes
Phonology,
phonetics
Morphemes
Words
Sentences
Meaning
Lexicon
Morphology
Isabelle Augenstein
-> /l/
Syntax
Semantics,
Discourse
/ʃ/ -> sh
wait + ed -> waited, cat -> cat
wait -> V, cat -> N
NP
NP
The (D) farmer (N) hit (V) the (D) donkey (N).
Every farmer who owns a donkey beats it.
∀x∀y (farmer(x) ∧ donkey(y) ∧ own(x, y)
→ beat(x, y))
21. Background: NLP Tasks 21
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
Speech recognition
Phonology,
phonetics
Morphology
-> /l/
Syntax
Lemmatisation or stemming,
part of speech (POS) tagging
Chunking, parsing
Lexicon
Semantics,
Discourse
Semantic and discourse analysis,
anaphora resolution
22. Background: Linguistics 22
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon
Morphology
-> /l/
Syntax
Semantics,
Discourse
23. Background: Linguistics 23
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/
Morphology
-> /l/
Syntax
Semantics,
Discourse
24. Background: Linguistics 24
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Semantics,
Discourse
25. Background: Linguistics 25
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies like an arrow
Semantics,
Discourse
26. Background: Linguistics 26
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
Semantics,
Discourse
27. Background: Linguistics 27
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars.
Semantics,
Discourse
28. Background: Linguistics 28
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars. -> Who had the binoculars?
Semantics,
Discourse
29. Background: Linguistics 29
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars. -> Who had the binoculars?
Semantics,
Discourse
Somewhere in Britain, some woman
has a child every thirty seconds.
30. Background: Linguistics 30
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars. -> Who had the binoculars?
Semantics,
Discourse
Somewhere in Britain, some woman
has a child every thirty seconds.
-> Same woman or different women?
31. Background: Linguistics 31
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
Ambiguities on every level
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars. -> Who had the binoculars?
Semantics,
Discourse
Somewhere in Britain, some woman
has a child every thirty seconds.
-> Same woman or different women?
32. Background: Linguistics 32
Acoustic signal
Phonemes
Morphemes
Words
Sentences
Meaning
Isabelle Augenstein
Ambiguities on every level
Y U SO
recognize speech – wreck a nice beach
/ˈrek.əәɡ.naɪz/ /spiːtʃ/ – /rek/ /əә/ /naɪs/ /biːtʃ/
Phonology,
phonetics
Lexicon She’d /ʃiːd/ -> she would, she had
Morphology
-> /l/
Syntax
Time flies(V/N) like(V/P) an arrow
The woman saw the man with the
binoculars. -> Who had the binoculars?
Semantics,
Discourse
Somewhere in Britain, some woman
has a child every thirty seconds.
-> Same woman or different women?
AMBIGUOUS?
33. Information Extraction 33
Language is ambiguous..
Can we still build named entity extractors that extract all
entities from unseen text correctly?
Isabelle Augenstein
34. Information Extraction 34
Language is ambiguous..
Can we still build named entity extractors that extract all
entities from unseen text correctly?
Isabelle Augenstein
35. Information Extraction 35
Language is ambiguous..
Can we still build named entity extractors that extract all
entities from unseen text correctly?
However, we can try to extract most of them correctly
using linguistic cues and background knowledge!
Isabelle Augenstein
36. NERC: Features 36
What can help to recognise and/or classify named entities?
• Words:
• Words in window before and after mention
• Sequences
• Bags of words
Between AD 1400 and 1450, China was a global superpower
w: China w-1: , w-2: 1450 w+1: was w+2: a
seq[-]: 1450, seq[+]: was a
bow: China bow[-]: , bow[-]: 1450 bow[+]: was bow[+]: a
Isabelle Augenstein
37. NERC: Features 37
What can help to recognise and/or classify named entities?
• Morphology:
• Capitalisation: is upper case (China), all upper case (IBM), mixed case
(eBay)
• Symbols: contains $, £, €, roman symbols (IV), ..
• Contains period (google.com), apostrophe (Mandy’s), hyphen (speed-o-meter),
ampersand (Fisher & Sons)
• Stem or Lemma (cats -> cat), prefix (disadvantages -> dis),
suffix (cats -> s), interfix (speed-o-meter -> o)
Isabelle Augenstein
38. our learning component.
4.1 Part-of-Speech Tags
Part-Of-Speech (POS) tags have often been considered as
an important discriminative feature for term identification.
Many works on key term identification apply either fixed
or regular expression POS tag patterns to improve their ef-fectiveness.
NERC: Features 38
Nonetheless, POS tags alone cannot produce
What can help to recognise and/or classify named entities?
high-quality results. As can be seen from the overall POS
tag distribution graph extracted from one of our collections
(see Figure 3), many of the most frequent tag patterns (e.g.,
JJNN tagging adjectives and nouns6) are far from yielding
perfect results.
• POS (part of speech) tags
• Most named entities are nouns
• Prokofyev (2014)
Isabelle Augenstein
39. Figure 3: Top 6 most frequent part-of-speech tag
patterns of the SIGIR collection, where JJ stands
comma, while the n-gram“automatic which is indeed a valid the beginning or at the end The contingency tables given this: The +punctuation respectively, the counts of the punctuation mark in any of of the n-grams that have no occurrences. From the tables, of punctuation marks (+punctuation) an n-gram occurs twice as often valid entities compared to the that the absence of punctuation less frequently for the valid ones.
Table 1: Contingency table appearing immediately before Valid +punctuation 1622 punctuation 6523 Totals 8145 Table 2: Contingency table appearing immediately after Valid +punctuation 4887
41. Morphology: Penn Treebank POS tags 40
Nouns
(all start
with N)
Verbs (all
start with V)
Adjectives
(all start
with J)
42. our learning component.
4.1 Part-of-Speech Tags
Part-Of-Speech (POS) tags have often been considered as
an important discriminative feature for term identification.
Many works on key term identification apply either fixed
or regular expression POS tag patterns to improve their ef-fectiveness.
NERC: Features 41
Nonetheless, POS tags alone cannot produce
What can help to recognise and/or classify named entities?
high-quality results. As can be seen from the overall POS
tag distribution graph extracted from one of our collections
(see Figure 3), many of the most frequent tag patterns (e.g.,
JJNN tagging adjectives and nouns6) are far from yielding
perfect results.
• POS (part of speech) tags
• Most named entities are nouns
• Prokofyev (2014)
Isabelle Augenstein
43. Figure 3: Top 6 most frequent part-of-speech tag
patterns of the SIGIR collection, where JJ stands
comma, while the n-gram“automatic which is indeed a valid the beginning or at the end The contingency tables given this: The +punctuation respectively, the counts of the punctuation mark in any of of the n-grams that have no occurrences. From the tables, of punctuation marks (+punctuation) an n-gram occurs twice as often valid entities compared to the that the absence of punctuation less frequently for the valid ones.
Table 1: Contingency table appearing immediately before Valid +punctuation 1622 punctuation 6523 Totals 8145 Table 2: Contingency table appearing immediately after Valid +punctuation 4887
44. NERC: Features 42
What can help to recognise and/or classify named entities?
• Gazetteers
• Retrieved from HTML lists or tables
• Using regular expressions patterns and search engines (e.g.
“Religions such as * ”)
• Retrieved from knowledge bases
Isabelle Augenstein
45. NERC: Training Models 43
Extensive choice of machine learning algorithms for training NERCs
46. NERC: Training Models 44
Extensive choice of machine learning algorithms for training NERCs
47. NERC: Training Models 45
• Unfortunately, there isn’t enough time to explain machine learning
algorithms in detail
• CRFs (conditional random fields) are one of the most widely used
algorithms for NERC
• Graphical models, view NERC as a sequence labelling task
• Named entities consist of a beginning token (B), inside tokens (I),
and outside tokens (O)
China (B-LOC) built (O) the (O) Forbidden (B-LOC) City (I-LOC) . (O)
• For now, we will focus on rule- and gazetteer-based NERC
• It is fairly easy to write manual extraction rules for NEs, can
achieve a high performance when combined with gazetteers
• This can be done with the GATE software (general architecture for
text engineering) and Jape rules
- Hands-on session
Isabelle Augenstein
48. Outlook: NLP ML Software 46
Natural Language Processing:
- GATE (general purpose architecture, includes other NLP and ML
software as plugins)
- Stanford NLP (Java)
- OpenNLP (Java)
- NLTK (Python)
Machine Learning:
- scikit-learn (Python, rich documentation, highly recommended!)
- Mallet (Java)
- WEKA (Java)
- Alchemy (graphical models, Java)
- FACTORIE (graphical models, Scala)
- CRFSuite (efficient implementation of CRFs, Python)
Isabelle Augenstein
49. Outlook: NLP ML Software 47
Ready to use NERC software:
- ANNIE (rule-based, part of GATE)
- Wikifier (based on Wikipedia)
- FIGER (based on Wikipedia, fine-grained Freebase NE classes)
Almost ready to use NERC software:
- CRFSuite (already includes Python implementation for feature extraction,
you just need to feed it with training data, which you can also download)
Ready to use RE software:
- ReVerb (Open IE, extracts patterns for any kind of relation)
- MultiR (Distant supervision, relation extractor trained on Freebase)
Web Content Extraction software:
- Boilerpipe (extract main text content from Web pages)
- Jsoup (traverse elements of Web pages individually, also allows to
extract text)
Isabelle Augenstein
50. Natural Language Processing for the 48
Semantic Web
Thank you for
your attention!
Questions?
Isabelle Augenstein