This document provides an overview of Bayes law, Bayesian networks, and latent Dirichlet allocation (LDA). It begins with an explanation of Bayes law and examples of how it can be used. Next, it defines Bayesian networks as probabilistic graphical models and provides examples. Finally, it introduces LDA as a statistical model for collections of discrete data like text corpora and explains how it can be used for topic modeling. The document includes mathematical notation and diagrams to illustrate key concepts.
ppt on machine learning to deep learning (1).pptxAnweshaGarima
The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
The document discusses natural language processing (NLP), which is a subfield of artificial intelligence that aims to allow computers to understand and interpret human language. It provides an introduction to NLP and its history, describes common areas of NLP research like text processing and machine translation, and discusses potential applications and the future of the field. The document is presented as a slideshow on NLP by an expert in the area.
This document provides an overview of Bayes law, Bayesian networks, and latent Dirichlet allocation (LDA). It begins with an explanation of Bayes law and examples of how it can be used. Next, it defines Bayesian networks as probabilistic graphical models and provides examples. Finally, it introduces LDA as a statistical model for collections of discrete data like text corpora and explains how it can be used for topic modeling. The document includes mathematical notation and diagrams to illustrate key concepts.
ppt on machine learning to deep learning (1).pptxAnweshaGarima
The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
The document discusses natural language processing (NLP), which is a subfield of artificial intelligence that aims to allow computers to understand and interpret human language. It provides an introduction to NLP and its history, describes common areas of NLP research like text processing and machine translation, and discusses potential applications and the future of the field. The document is presented as a slideshow on NLP by an expert in the area.
The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.
The document provides an overview of statistics for data science. It introduces key concepts including descriptive versus inferential statistics, different types of variables and data, probability distributions, and statistical analysis methods. Descriptive statistics are used to describe data through measures of central tendency, variability, and visualization techniques. Inferential statistics enable drawing conclusions about populations from samples using hypothesis testing, confidence intervals, and regression analysis.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
This document discusses cross-lingual information retrieval. It presents approaches for translating queries from other languages to the document language, including using online machine translation systems and developing a statistical machine translation system. It describes experiments on reranking translations to select the one most effective for retrieval and on adapting the reranking model to new languages. Results show the reranking approach improves over baselines and online translation systems. The document also explores document translation and query expansion techniques.
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
Big data - Key Enablers, Drivers & ChallengesShilpi Sharma
Big data is characterized by the 3 Vs - volume, velocity, and variety. The document discusses how big data is growing exponentially due to factors like the internet of things. Key enablers of big data include data storage, computation capacity, and data availability. Addressing big data requires technologies, techniques, and talent across the value chain of aggregating, analyzing, and consuming data to derive value. However, big data also presents management challenges around decision making, change management, technology clashes, and skills shortages. The document provides an example of how big data could help sales professionals better prepare for client meetings.
This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.
This document discusses machine learning and various applications of machine learning. It provides an introduction to machine learning, describing how machine learning programs can automatically improve with experience. It discusses several successful machine learning applications and outlines the goals and multidisciplinary nature of the machine learning field. The document also provides examples of specific machine learning achievements in areas like speech recognition, credit card fraud detection, and game playing.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
The Role of Natural Language Processing in Information RetrievalTony Russell-Rose
The document discusses the role of natural language processing (NLP) in information retrieval. It provides background on NLP, describing some of the fundamental problems in processing text like ambiguity and the contextual nature of language. It then outlines several common NLP tools and techniques used to analyze text at different levels, from part-of-speech tagging to named entity recognition and information extraction. The document concludes that NLP can help address some of the limitations of traditional document retrieval models by identifying implicit meanings and relationships within text.
ICFHR 2014 Competition on Handwritten KeyWord Spotting (H-KWS 2014)Konstantinos Zagoris
H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation- free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation- based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.
The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.
The document provides an overview of statistics for data science. It introduces key concepts including descriptive versus inferential statistics, different types of variables and data, probability distributions, and statistical analysis methods. Descriptive statistics are used to describe data through measures of central tendency, variability, and visualization techniques. Inferential statistics enable drawing conclusions about populations from samples using hypothesis testing, confidence intervals, and regression analysis.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
This document discusses cross-lingual information retrieval. It presents approaches for translating queries from other languages to the document language, including using online machine translation systems and developing a statistical machine translation system. It describes experiments on reranking translations to select the one most effective for retrieval and on adapting the reranking model to new languages. Results show the reranking approach improves over baselines and online translation systems. The document also explores document translation and query expansion techniques.
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
Big data - Key Enablers, Drivers & ChallengesShilpi Sharma
Big data is characterized by the 3 Vs - volume, velocity, and variety. The document discusses how big data is growing exponentially due to factors like the internet of things. Key enablers of big data include data storage, computation capacity, and data availability. Addressing big data requires technologies, techniques, and talent across the value chain of aggregating, analyzing, and consuming data to derive value. However, big data also presents management challenges around decision making, change management, technology clashes, and skills shortages. The document provides an example of how big data could help sales professionals better prepare for client meetings.
This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.
This document discusses machine learning and various applications of machine learning. It provides an introduction to machine learning, describing how machine learning programs can automatically improve with experience. It discusses several successful machine learning applications and outlines the goals and multidisciplinary nature of the machine learning field. The document also provides examples of specific machine learning achievements in areas like speech recognition, credit card fraud detection, and game playing.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
Introduction to Named Entity RecognitionTomer Lieber
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It explains that supervised learning involves learning from labeled examples, unsupervised learning involves categorizing without labels, and reinforcement learning involves learning behaviors to achieve goals through interaction. The document also discusses regression vs classification problems, the learning and testing process, and examples of machine learning applications like customer profiling, face recognition, and handwritten character recognition.
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
The Role of Natural Language Processing in Information RetrievalTony Russell-Rose
The document discusses the role of natural language processing (NLP) in information retrieval. It provides background on NLP, describing some of the fundamental problems in processing text like ambiguity and the contextual nature of language. It then outlines several common NLP tools and techniques used to analyze text at different levels, from part-of-speech tagging to named entity recognition and information extraction. The document concludes that NLP can help address some of the limitations of traditional document retrieval models by identifying implicit meanings and relationships within text.
ICFHR 2014 Competition on Handwritten KeyWord Spotting (H-KWS 2014)Konstantinos Zagoris
H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation- free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation- based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.
Reference Domain Ontologies and Large Medical Language Models.pptxChimezie Ogbuji
Large Language Models (LLMs) have exploded into the modern research and development consciousness and triggered an artificial intelligence revolution. They are well-positioned to have a major impact on Medical Informatics. However, much of the data used to train these revolutionary models are general-purpose and, in some cases, synthetically generated from LLMs. Ontologies are a shared and agreed-upon conceptualization of a domain and facilitate computational reasoning. They have become important tools in biomedicine, supporting critical aspects of healthcare and biomedical research, and are integral to science. In this talk, we will delve into ontologies, their representational and reasoning power, and how terminology systems such as SNOMED-CT, an international master terminology providing comprehensive coverage of the entire domain of medicine, can be used with Controlled Natural Languages (CNL) to advance how LLMs are used and trained.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
The document presents an approach called Convolutional Analysis of code Metrics Evolution (CAME) that uses a convolutional neural network to detect anti-patterns by analyzing the historical evolution of source code metrics at the class level. An evaluation on 7 open-source systems shows that considering longer histories of metrics improves detection performance and that CAME outperforms other machine learning and anti-pattern detection techniques in terms of precision, recall, and F-measure.
Semantics2018 Zhang,Petrak,Maynard: Adapted TextRank for Term Extraction: A G...Johann Petrak
Slides for the talk about the paper:
Ziqi Zhang, Johann Petrak and Diana Maynard, 2018: Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms. Semantics-2018, Vienna, Austria
Utility of topic extraction on customer experience dataKiran Karkera
There exists a vast trove of Customer Experience data in the form of product reviews, forum posts, customer service/customer satisfaction surveys and suchlike. This data is often in unstructured form. Companies that own this data would like to summarize these (often vast) data-sets.
One of the most common methods of text mining (for lack of a better word), is Topic Modeling. Given a large corpus of text, a topic model can assign a probabilistic score for each document-topic pair.
This paper explore the capabilities of topic mining , and suggests areas where the tool is a good fit, as well as situations where topic mining may be unsuitable.
SPSS (Statistical Package for the Social Sciences) is statistical software used for data management and analysis. It allows users to process questionnaires, report data in tables and graphs, and analyze data through various tests like means, chi-square, and regression. Originally called SPSS Inc., it is now owned by IBM and known as IBM SPSS Statistics. The document provides an introduction to SPSS and outlines how to define variables, enter data, select cases, run descriptive statistics like frequencies and crosstabs, and manipulate output files.
AlgoAnalytics is an analytics consultancy that uses advanced mathematical techniques and machine learning to solve business problems for clients across various industries. It has over 30 data scientists with expertise in mathematics, engineering, and cutting-edge methodologies like deep learning. AlgoAnalytics works closely with domain experts to effectively model problems and develop predictive analytics solutions using structured, text, image, sound, and other types of data. Some of its service offerings include contracts management, document decomposition, sentiment analysis, and predictive maintenance. The company is led by CEO and founder Aniruddha Pant, who has over 20 years of experience applying machine learning and analytics to academic and enterprise challenges.
This document discusses multimodal learning analytics (MLA), which examines learning through multiple modalities like video, audio, digital pens, etc. It provides examples of extracting features from these modalities to analyze problem solving, expertise levels, and presentation quality. Key challenges of MLA are integrating different modalities and developing tools to capture real-world learning outside online systems. While current accuracy is limited, MLA is an emerging field that could provide insights beyond traditional learning analytics.
Evidence-based Semantic WebJust a Dream or the Way to Go?Dragan Gasevic
The Semantic Web vision emerged with a promise to collect and interlink semantically relevant data from diverse sources in order to to achieve a full potential of the Web. After more than a decade of diligent research, it is the time to start summing up what has been accomplished and how mature Semantic Web research is, so that plans for the future can be charted. One of the key trails of a mature discipline is to have well-designed research methods allowing researchers to establish evidence about the effectiveness of the research ideas. It is equally important to to have knowledge translation methods that allow for transferring the established evidence to decision makers in practice. In this talk, we will first share some experience and challenges in conducting experiments in the area of the Semantic Web. We will next discuss findings of systematic reviews conducted to estimate the level of quality of the existing research results based on the criteria well-known in medical research and recently adopted in empirical software engineering. We will conclude the talk by discussing the importance and potential milestones for the Semantic Web in order to become an evidence-based discipline (similar to medicine or education) capable of producing strong research evidence transferable to practice.
A pilot on Semantic Textual Similaritypathsproject
This document summarizes the SemEval 2012 task on semantic textual similarity. It describes the motivation for the task as measuring similarity between text fragments on a graded scale. It then outlines the datasets used, including the MSR paraphrase corpus, MSR video corpus, WMT evaluation data, and OntoNotes word sense data. It also discusses the annotation process, which involved a pilot with authors and crowdsourcing through Mechanical Turk. The results showed most systems performed better than baselines and the best systems achieved correlations over 0.8 with human judgments.
This course introduces fundamental concepts of mechanics and electrodynamics. In mechanics, topics covered include Newton's laws of motion, particle dynamics, conservation laws, harmonic oscillators, and rigid body motion. In electrodynamics, the course covers electrostatics, electric fields, Gauss's law, dielectric materials, electrostatic energy, and Maxwell's equations. Students will apply these concepts through hands-on laboratory experiments complementing the theoretical content. The goal is to provide students with a foundation in key principles of physics applied to science and technology.
This course introduces fundamental concepts of mechanics and electrodynamics. In mechanics, topics covered include Newton's laws of motion, particle dynamics, conservation laws, harmonic oscillators, and rigid body motion. In electrodynamics, the course covers electrostatics, electric fields, Gauss's law, dielectric materials, electrostatic energy, and Maxwell's equations. Students will apply these concepts through hands-on laboratory experiments complementing the theoretical content. The goal is to provide students with a foundation in key principles of physics applied to science and technology.
Creating a dataset of peer review in computer science conferences published b...Aliaksandr Birukou
Computer science (CS) as a field is characterised by higher publication numbers and prestige of conference proceedings as opposed to scholarly journal articles. In this presentation we present preliminary results of the extraction and analysis of peer review information from computer science conferences published by Springer in almost 10,000 proceedings volumes. The results will be uploaded to lod.springer.com, with the purpose of creation of the largest dataset of peer review processes in CS conferences.
The Use Of Decision Trees For Adaptive Itembarthriley
This document compares the use of decision tree approaches to IRT-based CAT (computerized adaptive testing) for adaptive item selection and score estimation. Decision trees use predictor variables to partition a sample into increasingly homogeneous subgroups, represented as nodes in a tree structure. The study used decision tree algorithms and IRT modeling to select items and estimate scores on substance abuse scales. It found that decision trees were more efficient initially but that CAT outperformed decision trees in later stages of administration and had higher sensitivity to group differences. The authors conclude that combining decision trees with CAT may provide advantages.
At Elsevier, a lot of effort is focussed on content discovery for users, allowing them to find the most relevant articles for their research. This, at its core, blurs the boundaries of search and recommendation as we are both pushing content to the user and allowing them to search the world’s largest catalogue of scientific research. Apart from using the content as is, we can make new content more discoverable with the help of authors at submission time, for example by getting them to write an executive summary of their paper. However, doing this at submission time means that this additional information is not available for older content. This raises the question of how we can utilise the author’s input on new content to create the same feature retrospectively to the whole Elsevier corpus. Focusing on one use case, we discuss how an extractive summarization model (which is trained on the user-submitted summaries), is used to retrospectively generate executive summaries for articles in the catalogue. Further, we show how extractive summarization is used to highlight the salient points (methods, results and finding) within research articles across the complete corpus. This helps users to identify whether an article is of particular interest for them. As a logical next step, we investigate how these extractions can be used to make the research papers more discoverable through connecting it to other papers which share similar findings, methods or conclusion. In this talk we start from the beginning, understanding what users want from summarization systems. We discuss how the proposed use cases were developed and how this ties into the discovery of new content. We then look in more technical detail at what data is available and which methods can be utilised to implement such a system. Finally, while we are working toward taking this extractive summarization system into production, we need to understand the quality of what is being produced before going live. We discuss how internal annotators were used to confirming the quality of the summaries. Though the monitoring of quality does not stop there, we continually monitor user interaction with the extractive summaries as a proxy for quality and satisfaction.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Social media has changed communication but also raises ethical issues around privacy, misinformation, and mental health. Key debates focus on balancing free speech against harmful content. Filter bubbles and confirmation bias can limit exposure to diverse views through algorithms that amplify aligned content. Disinformation spreads for political and financial gain using bots, fake accounts and doctored content, undermining trust and democracy. Hate speech and cyberbullying on platforms can seriously impact mental health.
The document discusses several issues relating to the future of information ethics, including privacy, surveillance, algorithmic bias, intellectual property, globalization, and augmentation. Specifically, it addresses the need for individuals to have control over their personal data and consent to its use (privacy), the debate around balancing privacy rights with security interests behind surveillance practices, how algorithms can perpetuate inequality if not designed carefully, balancing copyright with open-source licensing, challenges of cross-border data flows, and both current and future ethical challenges relating to new technologies.
Bioethics examines ethical issues arising from advances in biology and medicine. It focuses on topics like abortion, euthanasia, genetic testing, and human experimentation. Bioethics also explores the duties of health professionals and aims to protect vulnerable groups. It provides a framework for complex moral issues in healthcare. Key events in bioethics history include the first test-tube baby in 1978 and gene-edited children in 2018. U.S. laws like HIPAA and the Common Rule aim to protect privacy and informed consent while international guidelines promote human dignity. Autonomy, beneficence, and justice are important principles in bioethical decision making.
This document discusses the ethics of surveillance and security. It covers various ethical approaches, security concepts, types of surveillance, hacking techniques, legal frameworks, privacy concepts, stakeholders, social factors, and ethical dilemmas related to information security and privacy. Key topics include utilitarianism, deontology, virtue ethics, rights-based ethics, social contract theory, encryption, firewalls, intrusion detection, authentication, CCTV, data mining, internet monitoring, wiretapping, facial recognition, penetration testing, social engineering, phishing, vulnerability assessments, GDPR, CFAA, FISMA, the right to be forgotten, consent decrees, anonymity, data minimization, opt-in vs opt-out, information lifec
The document describes 4 different expert systems proposed by different teams to identify the author of an email. Each system analyzes word frequency, keyword frequency, and part-of-speech patterns after keywords in known and questioned texts to determine the author. The systems compare feature vectors representing writing style extracted from the texts to identify or verify the author.
The police collected 4 datasets of emails - 2 from Debbie before and after marriage, 1 from Jamie, and 1 large reference collection. They analyzed the datasets using linguistic analysis of word frequencies, keywords that occur more than expected, and patterns around keywords. Comparing these linguistic features across the datasets could determine if the same person wrote the questioned and known datasets.
The document discusses using an expert system to analyze linguistic patterns in written documents. It describes using an expert system to analyze a threatening email and other emails from a suspect's computer to identify similarities in word frequencies, keywords, and keyword order that could indicate the suspect authored the threatening email. The system would analyze the questioned, known, and reference documents to generate shared word frequency, keyword frequency, and keyword positional lists to help determine authorship.
Barcodes and image recognition technology are examples of machine-readable representations of data. Barcodes use a pattern of bars and spaces that can be read by optical scanners to identify numbers and letters. Image recognition allows computers to identify objects in images through techniques like deep learning, which automatically extracts features from image data. Face recognition is a type of image recognition that extracts features from facial images and compares them to identify individuals, using algorithms like ResNet that represent faces as vectors and compare their Euclidean distances.
There are many sorting algorithms that can sort a list of numbers in ascending or descending order. Some common sorting algorithms include bubble sort, merge sort, and quicksort. Bubble sort has a computational complexity of O(n2) while merge sort and quicksort have better complexities of O(n log n). Stack and queue are abstract data types - stack follows LIFO (Last In First Out) while queue follows FIFO (First In First Out). Stack adds and removes elements from the top of the data structure, while queue adds to the tail and removes from the head.
Edge AI allows devices like self-driving cars to make decisions immediately using on-device processing rather than cloud-based processing, which introduces latency. Edge AI processes data and inferences locally on IoT and sensor devices. This enables applications like self-driving cars using computer vision to detect humans and stop in real-time. While Edge AI provides benefits like lower latency, security, and data privacy, it also faces limitations in processing power and operational complexity compared to cloud-based AI.
The document describes how to generate and review images using machine learning. To generate images, a model learns from original images, searches topics to generate new images on, and iterates generating and scoring images until a threshold is passed. To review generated images, both input and generated images are disassembled into pixels, quantified by color, analyzed for tendencies, which are then compared between input and generated images to find the generated image most similar to the original tendencies.
Computer graphics refers to images and figures created using a computer. There are two main types: 2D computer graphics, which are two-dimensional images, and 3D computer graphics, which create three-dimensional virtual spaces and objects. Creating 3D computer graphics involves several steps: modeling virtual objects, adding textures, rigging for movement, lighting, animation, and rendering to convert the 3D scene to a 2D image. A challenge with creating realistic human figures with 3D computer graphics is the "uncanny valley" effect, where figures that are almost but not perfectly realistic can appear creepy. Advances in technology now allow for highly realistic 3D computer graphics that avoid this effect.
The document discusses information security and protecting personal information. It defines information security as ensuring the confidentiality, integrity, and availability of information. Confidentiality means only authorized individuals can access information. Integrity means information has not been altered or destroyed. Availability means authorized individuals have access to information when needed without interruption. Security measures help protect against potential harm from others by restricting what others can do. However, security has weaknesses that can be exploited.
Gravitational wave detection and denoising algorithm uses machine learning to classify and filter noise from gravitational wave data. There are 22 main types of noise that can be identified using supervised and unsupervised learning algorithms. Unsupervised learning allows the computer to classify noise types on its own by finding inherent groupings or clusters in the data, while supervised learning uses labeled examples to efficiently perform classifications. The proposed method uses a variational autoencoder and invariant information clustering to learn features from spectrogram images of transient noise and then classify the noise types based on the learned features.
This document discusses key considerations for designing real-time embedded systems to ensure predictability and avoid failure. It identifies four main problems: whether the architecture is suitable, if link speeds are adequate, if processing components are powerful enough, and if the operating system is suitable. For each problem, it provides details on how to evaluate them and ensure real-time requirements are met, such as keeping CPU and link utilization below 50% and using an RTOS with preemptive scheduling and low interrupt latency and scheduling variance.
Python is the most widely used programming language in the world due to its simple syntax, wide platform support, and ease of use. It can be learned by both professionals and students. A survey by Stack Overflow found incredible growth in the number of visitors to Python questions on the site. Lisp is one of the oldest high-level programming languages still in use, known for its extensive use of parentheses in code. It was influential in the development of artificial intelligence. R is a programming language and software environment for statistical analysis and graphics. It provides many statistical and graphical techniques and is highly extensible.
Quantum computers can solve problems in hours that would take classical computers over 100 million years by leveraging the quantum state of superposition where multiple states exist simultaneously. Specifically, quantum computers could crack RSA encryption, which normally takes over 100 million years on classical computers, in just a few hours by applying this quantum superposition property.
Neural networks like artificial neural networks (ANNs) and long short-term memory (LSTM) networks are commonly used in machine translation systems. ANNs mimic the human brain by recognizing relationships in vast amounts of data, similar to neural connections in the brain. The field of machine translation began in the 1950s with rule-based machine translation (RBMT) relying on linguistic rules and dictionaries. Statistical machine translation (SMT) developed in the late 1970s and analyzed existing human translations. More recently, neural machine translation (NMT) introduced in the last decade learns from each translation to improve. LSTM is a type of RNN with feedback connections that can process entire sequences of data over time and is well-suited for classifying
SLAM is a technology that uses sensors to allow objects like vehicles to simultaneously map their environment and locate themselves within it. It utilizes sensors such as LiDAR and cameras to create maps without relying on GPS. LiDAR SLAM is particularly accurate and commonly used for automated driving as LiDAR can measure distance and shape using reflected laser time.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
2. Overview
• Background
• Case study
– Annotation of scientific research abstracts
– Strategic decision points
• Findings
– Methodological improvements
– Statistical smoke and rhetorical mirrors
• Conclusions
2
3. Subjectivity in annotation
3
POS tagging,
Phonetic
transcription
etc.
Annotation
guidelines with
discussion of
boundary
cases
Basic
annotation
guidelines
Speaker
intuition, e.g.
discourse
annotation,
pragmatics,
etc.
Problem:
Vagueness and ambiguity in natural languages
Manning (2011) 97.321 / 10021 = 56.28 %
4. Automated and manual
annotation compared
4
Automated annotation Manual annotation
Subjective agent Software developer Annotator
Subjective stage Prior to annotation During annotation
Replicability (near) Perfect Variable
Initial set up cost High (if new software) Low
On-going cost (near) Zero High
Scalable Yes No
Dependent
condition
Availability of training
set
Availability of
annotators (contingent
on time/money)
Speed (near) Instantaneous Variable
Factors
considered
Endogeneric Endo- and exogeneric
Strength Grammatical parsing Semantic parsing
5. Inter-annotator agreement
5
Crucial issue: Are the annotations correct?
We are interested in validity
• Ability to discriminate without error by placing item into appropriate category
But there is no “Ground truth”
• Linguistic categories are determined by human judgement
Implication: We cannot measure correctness directly
So we measure reliability , e.g. reproducibility.
• Intra-annotator reliability
• Inter-annotator reliability
i.e. whether human coders/annotators consistently make same decisions
Assumption 1: lack of reliability rules out validity (text/training issues)
Assumption 2: high reliability implies validity
Terminology credit (Artsein & Poesio, 2008)
Idea adapted from Boldea & Evert (2009) : https://clseslli09.files.wordpress.com/2009/07/02_iaa-
slides2.pdf/
6. Simple example 1
6
(abbreviated for length to increase readability)
Sentence Coder
1
Coder
2
Agreement
We address the problem of …… recognition I P
Our aim is to …recognize [x] from [y]. P P
[A] is set up as prior information, and its pose is
determined by three parameters, which are [j,k and l].
M M
An efficient local gradient-based method is proposed to
…, which is combined into … framework to estimate [V
and W] by iterative evolution
P R
It is shown that the local gradient-based method can
evaluate accurately and efficiently [V and W] .
R R
Observed agreement between 1 and 2 is 60%
7. IAA measures: Kappa coefficient
7
Inter-annotator agreement of 60% in previous example, but
chance agreement figure is 20%. Agreement measures must
be corrected for chance agreement (Carletta, 1996).
Kappa coefficient (Cohen 1960 for 2, Fleiss for 2+)
e.g. Corrected measure: K =
P A −P E
1−𝑃(𝐸)
1 (agreement) 0 (no correlation) -1(disagreement)
Interpretation of Kappa
• Landis and Koch (1977) 0.6-0.79 substantial; 0.8+ perfect
• Krippendorff (1980) 0.67-0.79 tentative; 0.8+ good
• Green (1997) 0.4-0.74 fair/good; 0.75 high
8. IAA measures: Sophisticated
8
e.g. Typical measures used in computational linguistics built
into NLP pipelines, such as NLTK and GATE
Rather than measuring agreement alone, we can measure
both agreement and disagreement, e.g. using Measuring
agreement on set-valued items (MASI) and/or Jaccard
distance. Both MASI (Passonneau, 2006) and Jaccard distance
make use of the union and intersection between sets.
Jaccard formula (Jaccard, 1908 cited in Dunn & Everitt, 2004)
is:
9. Case study overview
• Moves in scientific research abstracts
• Scientific disciplines
• Core corpus specifications
• Example abstract
• Tagset
• Strategic decision points (tag #IAA extraction)
NB: By convention this far-from-linear study is
presented in a linear fashion when in fact there
were numerous forks, dead-ends and iterations.
9
10. Moves in scientific research abstracts
10
Move definition
“a discoursal or rhetorical unit that performs a coherent
communicative function in a written or spoken discourse”.
(Swales, 2004, p.228)
Move sequences
Example (very short) abstract
5-move code Introduction Purpose Method Results Discussion
12. Core 1000 corpus specifications
12
Code Journal name #
abstracts
#
words
1 EC Transactions on Evolutionary Computation 100 17,433
2 KDE Transactions on Knowledge and Data Engineering 100 18,407
3 IP Transactions on Image Processing 100 16,859
4 IT Transactions on Information Theory 100 15,982
5 WC Transactions on Wireless Communications 100 15,971
6 Mat Advanced materials 100 6.078
7 Bot The plant cell 100 19,981
8 Ling App. Ling; Journal of Comm; J of Cog. Neurosc. 100 13,587
9 Eng Transactions on Industrial Electronics 100 14,569
10 Med British Medical Journal 100 29,437
Total 1000 162,232
First 100 abstracts of research articles from top-tier journals published
from Jan 2012.
13. We study the detection error probability associated with a balanced
binary relay tree, where the leaves of the tree correspond to N
identical and independent sensors. The root of the tree represents a
fusion center that makes the overall detection decision. Each of the
other nodes in the tree is a relay node that combines two binary
messages to form a single output binary message. Only the leaves are
sensors. In this way, the information from the sensors is aggregated
into the fusion center via the relay nodes. In this context, we describe
the evolution of the Type I and Type II error probabilities of the binary
data as it propagates from the leaves toward the root. Tight upper and
lower bounds for the total error probability at the fusion center as
functions of N are derived. These characterize how fast the total error
probability converges to 0 with respect to N , even if the individual
sensors have error probabilities that converge to 1/2.
[IT 120616]
Standard abstract (IT)
13
14. Tagset
14
Manual annotation using UAM Corpus Tool 2.X and 3.X (O`Donnell, 2015)
This layer of annotation is for rhetorical moves.
There are 5 choices of moves and 6 choices of submoves.
In short, each ontological unit is assigned to one of 9 choices.
The “uncertain” tag is designed as a temporary label.
15. #IAA theme extraction
Strategic decision points
• Research log was kept using themes, e.g. #meth,
#stats, #IAA
• 142 notes relating to #IAA written between 2012-
2017 were identified.
• The findings presented are the notes that are the
most important and generalizable to other
projects.
15
16. Findings overview:
Three types of strategic decisions affecting IAA
1. Methodological decisions
2. Statistical decisions
3. Rhetorical decisions
16
17. Findings (1)
Methodological choices to enhance IAA
A. Ontological unit
B. Tagset size
C. Tag clarity of demarcation
D. Catch-all tags
E. Detailed coding booklet
F. Pre-selection, training and testing
G. Easy-to-use tools
H. Monitoring, feedback and regular meetings
I. Pilot studies and small trials
17
18. Finding 1a: Ontological unit
18
Fixed ontological units (i.e. what you code), e.g. each
word, each sentence, simplify calculation of IAA and
increase the IAA since boundaries of each unit are
identical.
Variable ontological units provide researchers with
additional choices on how to calculate (manipulate?)
IAA – identical, subsumed, cross-over. How do you
calculate by character (inc. white space?), letter,
word, what unit?
I love you. 8 letters, 3 words, 11 characters
I love him. Agreement ratio 0.62, 0.67, 0.72
19. Finding 1b: Tagset size
The more tags, the less agreement
Rissanen (1989, as cited in Archer, 2012, n.p.) points out the
“mystery of vanishing reliability”
i.e. the statistical unreliability of annotation that is too detailed.
Obvious with hindsight, but researchers tend to develop tags
that will inform their research rather than result in higher IAA.
1 tag = total agreement (but probably no reason to code)
10 tags = less agreement
100 tags = much less agreement
1000 tags = almost no chance of high IAA
19
20. Finding 1c:
Tagset clarity of demarcation
Pilot studies of possible tags and tagsets
Pilot study:
Tagged 100 abstracts using IMRD move and CARS move tags
Difficulty:
1. prevalence of method in IMRD positions
2. demarcation of boundary cases created SOP, codified in
coding booklet
Final selection:
Dropped both sets of tags and selected Hyland (2004, p.67)
IPMPC tagset20
21. Finding 1d: Catch-all tags
21
Tags Description
Fuzzy Used when difficult to assign to tag in
existing tagset
Multiple Used when more than one tag applies
Portmanteau Used when item transcends two tag
domains
Problematic Used when impossible to assign tag
Archer (2012, n.p.) describes four tag types, all of which
increase IAA by providing easy-to-code options for
boundary cases.
My “uncertain” tag is a catch-all. Calculating IAA
including “uncertain” results in higher IAA.
23. Finding 1f:
Training course and test
23
Course based on annotation booklet
• Face-to-face and/or online
Test based on annotation booklet
• Serialist tests
• Holistic tests
Qualification cut-off points
• e.g. 90% can start annotating
• e.g. 61% needs additional training
• e.g. 60% discontinue training
24. Finding 1g:
Easy-to-use annotation tools
24
• Tool and instructions!
• UAM Corpus Tool – help forum in Spanish
• Wrote project-specific instruction booklet for annotators
25. Finding 1h: monitoring,
feedback and regular meetings
25
These three aspects I believe led to greater retention of
annotators and higher accuracy.
• More monitoring in initial stages (real-time is possible in GATE)
– to identify problems early
• Constructive actionable feedback
– to retain annotator and increase accuracy
• Regular meetings
– annotators who cancelled meetings tended to have a
problem (either with annotation or in their life).
I helped with annotation issues.
26. Finding 1i: Pilot studies
26
Various pilot studies and small-scale trials.
Enables researcher to discover issues and proactively avert potential problems
• 136 abstracts SFL annotation of process, participant and circumstance
• 136 abstracts SFL annotation of sub-categories of circumstance
• 10 abstracts Multimethod
• 500 abstracts Lexicogrammatical
• 40 abstracts Specialist vs linguist IMRaD annotation
• 100 abstracts Tagset selection (CARS vs IMRaD)
• 3 people Development of Coding booklet
• 10 abstracts Examples vs. Coding booklet
• 2 people Development of training course
• 500 abstracts Rhetorical moves using coding booklet by self
• 1000 abstracts Rhetorical moves using coding booklet by self & annotators
• 2500 abstracts Rhetorical moves using coding booklet by annotators
27. Findings (2)
Statistical choices to enhance IAA
A. Cherry-picking population-sample size ratio
B. Random vs systematic
C. Dealing with outliers (annotators)
• Omit [+justify?]; replace with mean [?]
D. Sample selection:
• early vs later coding
• pre-discussion vs. post-discussion
E. Granularity (see next slide)
• Reducing granularity by merging units; fewer
categories, higher agreement
27
29. Findings (3)
Rhetorical choices to enhance IAA
Claim high IAA with no further details
+ gold standard with no further details and/or
+ provide a simple ratio or percentage and/or
+ provide details of sample size
Rely on vagueness and ambiguity to allow reader to
infer higher IAA than found or actual high IAA.
29
30. Conclusion
High IAA may be due to
• sound or cogent methodological choices;
but it could also be due to manipulating the
• statistical smoke
(i.e. selecting parameters leading to higher IAA)
and
• rhetorical mirrors.
(i.e. using vagueness/ambiguity to infer IAA is high)
In most publications in applied linguistics, sufficient
detail is not provided.
30
31. Best practice suggestions
• Annotate using tags at one level more finely.
• Create annotation booklet with clear rules,
examples and discussion of boundary cases.
• Develop, trial and require all annotators to
complete a training course.
• Set a benchmark standard.
• Monitor and provide constructive actionable
feedback to annotators.
• Report IAA in sufficient detail to convince
skeptical readers.
31
32. Beware of the
skeleton in the cupboard
• Researchers aim to
portray their work as
sound or cogent.
• Actual IAA may differ
from reported IAA
• Be wary of statistical
smoke and
rhetorical mirrors
32