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.
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
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGICIJCI JOURNAL
In this paper we use Fuzzy logic instead of the classical methods of performance evaluation of the students.
In classical methods mathematical calculations are being used. This performance evaluation is done for the
engineering students mainly. The overall evaluation cannot be just based on the total marks he/she
obtained in various subjects. A complete engineer is the one who is skilled in lab experiments , theory
papers as well as in projects. So Through this paper we put forward a fuzzy method for the same. Even
though this method requires additional software this is very helpful for teachers to evaluate a student. This
method is flexible as they can change the membership function and also its value.
This document discusses various machine learning techniques for transfer learning, including unsupervised domain adaptation (UDA), few-shot learning (FSL), zero-shot learning (ZSL), and hypothesis transfer learning (HTL). For UDA, the author proposes graph matching approaches to minimize domain discrepancy between source and target domains. For FSL, a two-stage approach is used to estimate novel class prototypes and variances. For ZSL, an approach is described that uses relational matching, adaptation, and calibration. For HTL, estimating novel class prototypes from source prototypes and sparse target data is discussed. Experimental results demonstrate the effectiveness of the proposed approaches.
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.
Machine Reading Using Neural Machines (talk at Microsoft Research Faculty Sum...Isabelle Augenstein
The document discusses machine reading using neural machines. It presents goals of fact checking claims and understanding scientific publications. It outlines challenges in tasks like stance detection on tweets and summarizing scientific papers. These include interpreting statements based on the target or headline, handling unseen targets, and the small size of benchmark datasets which makes neural machine reading computationally costly.
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.
GECCO-2014 Learning Classifier Systems: A Gentle IntroductionPier Luca Lanzi
The document provides an introduction to learning classifier systems, including an overview of their history and applications. It discusses the key components of learning classifier systems, such as how they represent knowledge as classifiers, use reinforcement learning to update classifier predictions, and employ a genetic algorithm to discover new classifiers. Examples are also given to illustrate how learning classifier systems work and the types of decisions that must be made when applying them.
Testing of artificial intelligence; AI quality engineering skils - an introdu...Rik Marselis
Testing of AI will require a new skillset related to interpreting a system’s boundaries or tolerances. Indeed, as our paper points out, the complex functioning of an AI system means, amongst other things, that the focus of testing shifts from output to input to verify a robust solution. Also we introduce the 6 angles of quality for Artificial Intelligence and Robotics.
This paper was written by Humayun Shaukat, Toni Gansel and Rik Marselis.
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
OVERALL PERFORMANCE EVALUATION OF ENGINEERING STUDENTS USING FUZZY LOGICIJCI JOURNAL
In this paper we use Fuzzy logic instead of the classical methods of performance evaluation of the students.
In classical methods mathematical calculations are being used. This performance evaluation is done for the
engineering students mainly. The overall evaluation cannot be just based on the total marks he/she
obtained in various subjects. A complete engineer is the one who is skilled in lab experiments , theory
papers as well as in projects. So Through this paper we put forward a fuzzy method for the same. Even
though this method requires additional software this is very helpful for teachers to evaluate a student. This
method is flexible as they can change the membership function and also its value.
This document discusses various machine learning techniques for transfer learning, including unsupervised domain adaptation (UDA), few-shot learning (FSL), zero-shot learning (ZSL), and hypothesis transfer learning (HTL). For UDA, the author proposes graph matching approaches to minimize domain discrepancy between source and target domains. For FSL, a two-stage approach is used to estimate novel class prototypes and variances. For ZSL, an approach is described that uses relational matching, adaptation, and calibration. For HTL, estimating novel class prototypes from source prototypes and sparse target data is discussed. Experimental results demonstrate the effectiveness of the proposed approaches.
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.
Machine Reading Using Neural Machines (talk at Microsoft Research Faculty Sum...Isabelle Augenstein
The document discusses machine reading using neural machines. It presents goals of fact checking claims and understanding scientific publications. It outlines challenges in tasks like stance detection on tweets and summarizing scientific papers. These include interpreting statements based on the target or headline, handling unseen targets, and the small size of benchmark datasets which makes neural machine reading computationally costly.
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.
GECCO-2014 Learning Classifier Systems: A Gentle IntroductionPier Luca Lanzi
The document provides an introduction to learning classifier systems, including an overview of their history and applications. It discusses the key components of learning classifier systems, such as how they represent knowledge as classifiers, use reinforcement learning to update classifier predictions, and employ a genetic algorithm to discover new classifiers. Examples are also given to illustrate how learning classifier systems work and the types of decisions that must be made when applying them.
Testing of artificial intelligence; AI quality engineering skils - an introdu...Rik Marselis
Testing of AI will require a new skillset related to interpreting a system’s boundaries or tolerances. Indeed, as our paper points out, the complex functioning of an AI system means, amongst other things, that the focus of testing shifts from output to input to verify a robust solution. Also we introduce the 6 angles of quality for Artificial Intelligence and Robotics.
This paper was written by Humayun Shaukat, Toni Gansel and Rik Marselis.
PATHS: User Requirements Analysis v1.0pathsproject
This document summarizes user requirements research conducted for the PATHS project, which aims to develop a system to allow users to create and consume personalized paths through cultural heritage collections. The research utilized a mixed-methods approach including desk research, surveys, interviews, and experiments with potential users. The results identified user profiles and requirements in four domains: heritage, education, professional, and leisure users. Key findings include the need for both expert and non-expert path creation, consumption of paths for various activities, and social features to share and discuss paths. The research informs the design of the initial PATHS system prototype to ensure it meets users' needs.
PATHS Functional specification first prototypepathsproject
The document presents a functional specification for the first prototype of the PATHS system, which aims to make exploring cultural heritage content enjoyable and easy for users, detailing functions like user accounts, workspaces, searching, creating paths and nodes, tagging, and different types of users including general, registered, and administrators. The specification is based on analysis of user requirements to implement core necessary functions for the first prototype while leaving more complex aspects for future iterations.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document provides biographical information on Anne Frank, Margot Frank, Otto Frank, and Edith Frank. It describes that Anne Frank was born in Germany but lived most of her life in Amsterdam, where she lost her German citizenship under the Nuremberg Laws. Margot was Anne's older sister who was deported by the Gestapo, hastening the Frank family into hiding. Otto Frank was Anne and Margot's father, a German-born businessman. Edith Frank was Anne's mother and wife of Otto Frank. The document also briefly describes the fate of the eight individuals from the Secret Annex, including their transfers to concentration camps and deaths.
The old exchange environment versus modern exchange environment part 02#36Eyal Doron
The old Exchange environment versus “modern” Exchange environment | Part 02#36
The Autodiscover as a solution for the modern Exchange environment versus, the older Exchange server architecture that did not have the Autodiscover infrastructure.
http://o365info.com/the-old-exchange-environment-versus-modern-exchange-environment-part-02-of-36
Eyal Doron | o365info.com
PATHS Second prototype-functional-specpathsproject
This document provides the functional specifications for the second prototype of the PATHS project. It outlines functions to be implemented based on priorities - functions that resolve critical user interface issues are highest priority. The second prototype will address issues identified in testing the first prototype and provide enhanced functionality such as improved search, user accounts/permissions, and workspace customization. The specifications draw on recommendations from evaluating the first prototype to improve the PATHS system.
1) The document describes a group revisiting the Hanshi Khushi school for mentally challenged children to continue a crafts project from the previous year.
2) This time, the group expanded and divided tasks among making decorations for an upcoming sale, teaching craft techniques to teachers, and helping at the sale.
3) Over several sessions at the school and weekends, the group helped students make painted clay pots, printed notebooks, jute dolls, picture frames, calendars, and other crafts for the sale. It was a proud experience to see the completed products despite challenges with coordination and time.
A presentation about the project given at PATCH 2011 by Paula Goodale, Paul Clough, Nigel Ford and Mark Stevenson, University of Sheffield. 13 February 2011
A digital id or digital certificate consists of a public and private key pair that can be used to encrypt documents and files so that only the intended recipient can read them, and to digitally sign documents to verify the sender and ensure the document hasn't been altered. The document discusses how public and private keys work, with the public key used to encrypt and the private key needed to decrypt. It states that a digital signature guarantees the identity of the sender and confirms the document hasn't been modified during transmission.
My E-mail appears as spam - Troubleshooting path | Part 11#17Eyal Doron
My E-mail appears as spam - Troubleshooting path | Part 11#17
http://o365info.com/my-e-mail-appears-as-spam-troubleshooting-path-part-11-17
Troubleshooting scenario of internal \ outbound spam in Office 365 and Exchange Online environment.
Verifying if our domain name is blacklisted, verifying if the problem is related to E-mail content, verifying if the problem is related to specific organization user E-mail address, Moving the troubleshooting process to the “other side.
Eyal Doron | o365info.com
Cardinal Bessarion and scholar Politian helped spread Greek scholarship in 15th century Italy. Aldus Manutius established a publishing house in Venice to make Greek texts more widely available through printing. He collaborated with scholar Marcus Musurus, who corrected errors in manuscripts and helped publish many Greek works. Erasmus also contributed by working with Aldus to publish collections that helped spread Greek learning.
Las canciones del colegio son importantes para crear un sentido de comunidad y tradición entre los estudiantes y profesores. Canciones como el himno del colegio o canciones coreadas durante eventos deportivos ayudan a unir a todos bajo los colores y valores de la escuela. Estas canciones pasan de generación en generación y ayudan a mantener viva la historia y el espíritu del colegio a través de los años.
Autodiscover flow in active directory based environment part 15#36Eyal Doron
Autodiscover flow in Active Directory based environment | Part 15#36
Reviewing the Autodiscover flow that is implemented by Outlook client on the internal network that enable the client to access the On-Premise Active Directory.
http://o365info.com/autodiscover-flow-active-directory-based-environment-part-15-of-36
Eyal Doron | o365info.com
The girls wanted to teach basic literacy skills to mothers in their locality who were not educated. They held weekly classes where 4 mothers learned to write their names. Through regular practice with slates at home and in class, the mothers gained confidence in their writing. At a celebration party, the girls presented the mothers with framed photos as gifts. Both the students and teachers felt proud of their accomplishments and want to continue classes to learn more.
Exchange In-Place eDiscovery & Hold | Introduction | 5#7Eyal Doron
Exchange In-Place eDiscovery & Hold | Introduction | 5#7
http://o365info.com/exchange-in-place-ediscovery-hold-introduction-part-5-7
The Exchange In-Place Hold & eDiscovery, is a very powerful tool that can help us to accomplish three main tasks.
1. Search for information (mail items) in single or multiple mailboxes
2. Put specific information on “hold” (enable to save the information for an unlimited time period)
3. Recover deleted mail items
In this article, we will review the logic and the concepts of the Exchange In-Place Hold & eDiscovery toll.
In the next article xx, we will demonstrate how to use the Exchange In-Place Hold & eDiscovery toll for recovering deleted mail items.
Eyal Doron | o365info.com
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding D...Jinho Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy. A novel distillation ensemble approach is also proposed that trains a high-efficient student model using multiple teacher models. In our approach, the teacher models play roles only during training such that the student model operates on its own without getting supports from the teacher models during decoding, which makes it eighty times faster and lighter than other typical ensemble methods. All models are evaluated on seven document classification datasets and show significant advantage over the teacher models for most cases. Our analysis depicts insightful transformation of word embeddings from distillation and suggests a future direction to ensemble approaches using neural models.
The Status of ML Algorithms for Structure-property Relationships Using Matb...Anubhav Jain
The document discusses the development of Matbench, a standardized benchmark for evaluating machine learning algorithms for materials property prediction. Matbench includes 13 standardized datasets covering a variety of materials prediction tasks. It employs a nested cross-validation procedure to evaluate algorithms and ranks submissions on an online leaderboard. This allows for reproducible evaluation and comparison of different algorithms. Matbench has provided insights into which algorithm types work best for certain prediction problems and has helped measure overall progress in the field. Future work aims to expand Matbench with more diverse datasets and evaluation procedures to better represent real-world materials design challenges.
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.
Evaluating Chemical Composition and Crystal Structure Representations using t...Anubhav Jain
This document discusses the Matbench testing protocol for evaluating machine learning models for materials property prediction. Matbench contains 13 standardized tasks to compare different models. Several existing models have been tested, including those using composition features and graph neural networks using structural representations. While some tasks have seen significant improvement, others have seen little progress. The document suggests ways to improve Matbench, such as adding new materials classes, properties, and evaluation metrics to further benchmark progress and encourage development of better models.
The document discusses optimizing the performance of Word2Vec on multicore systems through a technique called Context Combining. Some key points:
- Context Combining improves Word2Vec training efficiency by combining related windows that share samples, improving floating point throughput and reducing overhead.
- Experiments on Intel and Intel Knights Landing processors show Context Combining (pSGNScc) achieves up to 1.28x speedup over prior work (pWord2Vec) and maintains comparable accuracy to state-of-the-art implementations.
- Parallel scaling tests show pSGNScc achieves near linear speedup up to 68 cores, utilizing more of the available computational resources than previous techniques.
- Future
PATHS: User Requirements Analysis v1.0pathsproject
This document summarizes user requirements research conducted for the PATHS project, which aims to develop a system to allow users to create and consume personalized paths through cultural heritage collections. The research utilized a mixed-methods approach including desk research, surveys, interviews, and experiments with potential users. The results identified user profiles and requirements in four domains: heritage, education, professional, and leisure users. Key findings include the need for both expert and non-expert path creation, consumption of paths for various activities, and social features to share and discuss paths. The research informs the design of the initial PATHS system prototype to ensure it meets users' needs.
PATHS Functional specification first prototypepathsproject
The document presents a functional specification for the first prototype of the PATHS system, which aims to make exploring cultural heritage content enjoyable and easy for users, detailing functions like user accounts, workspaces, searching, creating paths and nodes, tagging, and different types of users including general, registered, and administrators. The specification is based on analysis of user requirements to implement core necessary functions for the first prototype while leaving more complex aspects for future iterations.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document provides biographical information on Anne Frank, Margot Frank, Otto Frank, and Edith Frank. It describes that Anne Frank was born in Germany but lived most of her life in Amsterdam, where she lost her German citizenship under the Nuremberg Laws. Margot was Anne's older sister who was deported by the Gestapo, hastening the Frank family into hiding. Otto Frank was Anne and Margot's father, a German-born businessman. Edith Frank was Anne's mother and wife of Otto Frank. The document also briefly describes the fate of the eight individuals from the Secret Annex, including their transfers to concentration camps and deaths.
The old exchange environment versus modern exchange environment part 02#36Eyal Doron
The old Exchange environment versus “modern” Exchange environment | Part 02#36
The Autodiscover as a solution for the modern Exchange environment versus, the older Exchange server architecture that did not have the Autodiscover infrastructure.
http://o365info.com/the-old-exchange-environment-versus-modern-exchange-environment-part-02-of-36
Eyal Doron | o365info.com
PATHS Second prototype-functional-specpathsproject
This document provides the functional specifications for the second prototype of the PATHS project. It outlines functions to be implemented based on priorities - functions that resolve critical user interface issues are highest priority. The second prototype will address issues identified in testing the first prototype and provide enhanced functionality such as improved search, user accounts/permissions, and workspace customization. The specifications draw on recommendations from evaluating the first prototype to improve the PATHS system.
1) The document describes a group revisiting the Hanshi Khushi school for mentally challenged children to continue a crafts project from the previous year.
2) This time, the group expanded and divided tasks among making decorations for an upcoming sale, teaching craft techniques to teachers, and helping at the sale.
3) Over several sessions at the school and weekends, the group helped students make painted clay pots, printed notebooks, jute dolls, picture frames, calendars, and other crafts for the sale. It was a proud experience to see the completed products despite challenges with coordination and time.
A presentation about the project given at PATCH 2011 by Paula Goodale, Paul Clough, Nigel Ford and Mark Stevenson, University of Sheffield. 13 February 2011
A digital id or digital certificate consists of a public and private key pair that can be used to encrypt documents and files so that only the intended recipient can read them, and to digitally sign documents to verify the sender and ensure the document hasn't been altered. The document discusses how public and private keys work, with the public key used to encrypt and the private key needed to decrypt. It states that a digital signature guarantees the identity of the sender and confirms the document hasn't been modified during transmission.
My E-mail appears as spam - Troubleshooting path | Part 11#17Eyal Doron
My E-mail appears as spam - Troubleshooting path | Part 11#17
http://o365info.com/my-e-mail-appears-as-spam-troubleshooting-path-part-11-17
Troubleshooting scenario of internal \ outbound spam in Office 365 and Exchange Online environment.
Verifying if our domain name is blacklisted, verifying if the problem is related to E-mail content, verifying if the problem is related to specific organization user E-mail address, Moving the troubleshooting process to the “other side.
Eyal Doron | o365info.com
Cardinal Bessarion and scholar Politian helped spread Greek scholarship in 15th century Italy. Aldus Manutius established a publishing house in Venice to make Greek texts more widely available through printing. He collaborated with scholar Marcus Musurus, who corrected errors in manuscripts and helped publish many Greek works. Erasmus also contributed by working with Aldus to publish collections that helped spread Greek learning.
Las canciones del colegio son importantes para crear un sentido de comunidad y tradición entre los estudiantes y profesores. Canciones como el himno del colegio o canciones coreadas durante eventos deportivos ayudan a unir a todos bajo los colores y valores de la escuela. Estas canciones pasan de generación en generación y ayudan a mantener viva la historia y el espíritu del colegio a través de los años.
Autodiscover flow in active directory based environment part 15#36Eyal Doron
Autodiscover flow in Active Directory based environment | Part 15#36
Reviewing the Autodiscover flow that is implemented by Outlook client on the internal network that enable the client to access the On-Premise Active Directory.
http://o365info.com/autodiscover-flow-active-directory-based-environment-part-15-of-36
Eyal Doron | o365info.com
The girls wanted to teach basic literacy skills to mothers in their locality who were not educated. They held weekly classes where 4 mothers learned to write their names. Through regular practice with slates at home and in class, the mothers gained confidence in their writing. At a celebration party, the girls presented the mothers with framed photos as gifts. Both the students and teachers felt proud of their accomplishments and want to continue classes to learn more.
Exchange In-Place eDiscovery & Hold | Introduction | 5#7Eyal Doron
Exchange In-Place eDiscovery & Hold | Introduction | 5#7
http://o365info.com/exchange-in-place-ediscovery-hold-introduction-part-5-7
The Exchange In-Place Hold & eDiscovery, is a very powerful tool that can help us to accomplish three main tasks.
1. Search for information (mail items) in single or multiple mailboxes
2. Put specific information on “hold” (enable to save the information for an unlimited time period)
3. Recover deleted mail items
In this article, we will review the logic and the concepts of the Exchange In-Place Hold & eDiscovery toll.
In the next article xx, we will demonstrate how to use the Exchange In-Place Hold & eDiscovery toll for recovering deleted mail items.
Eyal Doron | o365info.com
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding D...Jinho Choi
Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy. A novel distillation ensemble approach is also proposed that trains a high-efficient student model using multiple teacher models. In our approach, the teacher models play roles only during training such that the student model operates on its own without getting supports from the teacher models during decoding, which makes it eighty times faster and lighter than other typical ensemble methods. All models are evaluated on seven document classification datasets and show significant advantage over the teacher models for most cases. Our analysis depicts insightful transformation of word embeddings from distillation and suggests a future direction to ensemble approaches using neural models.
The Status of ML Algorithms for Structure-property Relationships Using Matb...Anubhav Jain
The document discusses the development of Matbench, a standardized benchmark for evaluating machine learning algorithms for materials property prediction. Matbench includes 13 standardized datasets covering a variety of materials prediction tasks. It employs a nested cross-validation procedure to evaluate algorithms and ranks submissions on an online leaderboard. This allows for reproducible evaluation and comparison of different algorithms. Matbench has provided insights into which algorithm types work best for certain prediction problems and has helped measure overall progress in the field. Future work aims to expand Matbench with more diverse datasets and evaluation procedures to better represent real-world materials design challenges.
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.
Evaluating Chemical Composition and Crystal Structure Representations using t...Anubhav Jain
This document discusses the Matbench testing protocol for evaluating machine learning models for materials property prediction. Matbench contains 13 standardized tasks to compare different models. Several existing models have been tested, including those using composition features and graph neural networks using structural representations. While some tasks have seen significant improvement, others have seen little progress. The document suggests ways to improve Matbench, such as adding new materials classes, properties, and evaluation metrics to further benchmark progress and encourage development of better models.
The document discusses optimizing the performance of Word2Vec on multicore systems through a technique called Context Combining. Some key points:
- Context Combining improves Word2Vec training efficiency by combining related windows that share samples, improving floating point throughput and reducing overhead.
- Experiments on Intel and Intel Knights Landing processors show Context Combining (pSGNScc) achieves up to 1.28x speedup over prior work (pWord2Vec) and maintains comparable accuracy to state-of-the-art implementations.
- Parallel scaling tests show pSGNScc achieves near linear speedup up to 68 cores, utilizing more of the available computational resources than previous techniques.
- Future
This document summarizes a thesis on automating test routine creation through natural language processing. The author proposes using word embeddings and recommender systems to automatically generate test cases from requirements documents and link them together. The methodology involves representing text as word vectors, calculating similarity between requirements and test blocks, and applying association rule mining on test block sequences. An experiment on a space operations dataset showed the approach improved productivity in test creation and requirements tracing over manual methods. Future work could explore using deep learning models and collecting additional evaluation metrics from users.
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Dissertation defense slides on "Semantic Analysis for Improved Multi-document...Quinsulon Israel
This document outlines Quinsulon Israel's Ph.D. dissertation defense on using semantic analysis to improve multi-document summarization. The dissertation examines using semantic triples clustering and semantic class scoring of sentences to generate summaries. It reviews prior work on statistical, features combination, graph-based, multi-level text relationship, and semantic analysis approaches. The dissertation aims to improve the baseline method and evaluate the effects of semantic analysis on focused multi-document summarization performance.
a deep reinforced model for abstractive summarizationJEE HYUN PARK
The document summarizes a research paper on a neural model for abstractive summarization. The model uses an intra-attention mechanism that separately attends to the input and generated output in the encoder and decoder. It also uses a hybrid learning objective that combines supervised learning with reinforcement learning to generate higher quality summaries. Evaluation shows the model achieves state-of-the-art results on the CNN/Daily Mail dataset and produces summaries rated as higher quality according to human judges.
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...Dr. Haxel Consult
Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.
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1. SemEval 2012 task 6
A pilot on
Semantic Textual Similarity
http://www.cs.york.ac.uk/semeval-2012/task6/
Eneko Agirre (University of the Basque Country)
Daniel Cer (Stanford University)
Mona Diab (Columbia University)
Bill Dolan (Microsoft)
Aitor Gonzalez-Agirre (University of the Basque Country)
2. Outline
Motivation
Description of the task
Source Datasets
Definition of similarity and annotation
Results
Conclusions, open issues
STS task - SemEval 2012 2
3. Motivation
● Word similarity and relatedness highly correlated
with humans
Move to longer text fragments (STS)
– Li et al. (2006) 65 pairs of glosses
– Lee et al. (2005) 50 documents on news
● Paraphrase datasets judge semantic equivalence
between text fragments
● Textual entailment (TE) judges whether one
fragment entails another
Move to graded notion of semantic equivalence (STS)
STS task - SemEval 2012 3
4. Motivation
● STS has been part of the core implementation
of TE and paraphrase systems
● Algorithms for STS have been extensively
applied
● MT, MT evaluation, Summarization, Generation,
Distillation, Machine Reading, Textual Inference,
Deep QA
● Interest from application side confirmed in a
recent STS workshop:
● http://www.cs.columbia.edu/~weiwei/workshop/
STS task - SemEval 2012 4
5. Motivation
● STS as a unified framework to combine and evaluate
semantic (and pragmatic components)
word sense disambiguation and induction
lexical substitution
semantic role labeling
multiword expression detection and handling
anaphora and coreference resolution
time and date resolution
named-entity handling
underspecification
hedging
semantic scoping
discourse analysis
STS task - SemEval 2012 5
6. Motivation
● Start with a pilot task, with the following goals
1.To set a definition of STS as a graded notion which
can be easily communicated to non-expert
annotators beyond the likert-scale
2.To gather a substantial amount of sentence pairs
from diverse datasets, and to annotate them with
high quality
3.To explore evaluation measures for STS
4.To explore the relation of STS to paraphrase and
Machine Translation Evaluation exercises
STS task - SemEval 2012 6
7. Description of the task
● Given two sentences, s1 and s2
● Return a similarity score
and an optional confidence score
● Evaluation
● Correlation (Pearson)
with average of human scores
STS task - SemEval 2012 7
8. Data sources
● MSR paraphrase: train (750), test (750)
● MSR video: train (750), test (750)
● WMT 07–08 (EuroParl): train (734), test (499)
● Surprise datasets
● WMT 2007 news: test (399)
● Ontonotes – WordNet glosses: test (750)
STS task - SemEval 2012 8
10. Annotation
● Pilot with 200 pairs annotated by three authors
● Pairwise (0.84r to 0.87r), with average (0.87r to 0.89r)
● Amazon Mechanical Turk
● 5 annotations per pair, averaged
● Remove turkers with very low correlations with pilot
● Correlation with us 0.90r to 0.94r
● MSR: 2.76 mean, 0.66 sdv.
● SMT: 4.05 mean, 0.66 sdv.
STS task - SemEval 2012 10
11. Results
● Baselines: random, cosine of tokens
● Participation: 120 hours to submit three runs.
● 35 teams, 88 runs
● Evaluation
● Pearson for each dataset
● Concatenate all 5 datasets: ALL
– Some systems doing well in each dataset, low results
● Weighted mean over 5 datasets (micro-average): MEAN
– Statistical significance
● Normalize each dataset and concatenate (least square): ALLnorm
– Corrects errors (random would get 0.59r)
STS task - SemEval 2012 11
14. Results
● Evaluation using confidence scores
● Weighted Pearson correlation
● Some systems improve results (IRIT, TIANTIANZHU7)
– IRIT: 0.48r => 0.55r
● Others did not (UNED)
● Unfortunately only a few teams sent out
confidence scores
● Promising direction, potentially useful in
applications (Watson)
STS task - SemEval 2012 14
15. Tools used
● WordNet, corpora and Wikipedia most used
● Knowledge-based and distributional equally
● Machine learning widely used for combination
and tuning
● Best systems used most resources
● Exception: SOFT-CARDINALITY
STS task - SemEval 2012 15
16. Conclusions
● Pilot worked!
● Define STS as likert scale with definitions
● Produce a wealth of data of high quality (~ 3750)
● Very successful participation
● All data and system outputs are publicly available
● Started to explore evaluation of STS
● Started to explore relation to paraphrase and
MT evaluation
● Planning for STS 2013
STS task - SemEval 2012 16
17. Open issues
● Data sources, alternatives to the opportunistic method
● New pairs of sentences
● Possibly related to specific phenomena, e.g. negation
● Definition of task
● Agreement for definitions
● Compare to Likert scale with no definitions
● Define multiple dimensions of similarity
(polarity, sentiment, modality, relatedness, entailment, etc.)
● Evaluation
● Spearman, Kendall's Tau
● Significance tests over multiple datasets (Bergmann & Hommel, 1989)
● And more!! Join STS-semeval google group
STS task - SemEval 2012 17
18. STS presentations
● Three best systems will be presented in
last session of Semeval today (4:00pm)
● Analysis of runs and some thoughts on
evaluation will be also presented
● Tomorrow in the posters sessions
STS task - SemEval 2012 18
19. Thanks for your attention!
And thanks to all participants, specially all participants, specially
those contributing to the evaluation discussion (Yoan Gutierrez,
Michael Heilman, Sergio Jimenez, Nitin Madnami, Diana
McCarthy and Shrutiranjan Satpathy)
Eneko Agirre was partially funded by the European Community's Seventh Framework Programme
(FP7/2007-2013) under grant agreement no. 270082 (PATHS project) and the Ministry of Economy
under grant TIN2009-14715-C04-01 (KNOW2 project). Daniel Cer gratefully acknowledges the support
of the Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air
Force Research Labora-tory (AFRL) prime contract no. FA8750-09-C-0181 and the support of the
DARPA Broad Operational Language Translation (BOLT) program through IBM. The STS annotations
were funded by an extension to DARPA GALE subcontract to IBM # W0853748 4911021461.0 to Mona
Diab. Any opinions, findings, and conclusion or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect the view of the DARPA, AFRL, or the US government.
STS task - SemEval 2012 19
20. SemEval 2012 task 6
A pilot on
Semantic Textual Similarity
http://www.cs.york.ac.uk/semeval-2012/task6/
Eneko Agirre (University of the Basque Country)
Daniel Cer (Stanford University)
Mona Diab (Columbia University)
Bill Dolan (Microsoft)
Aitor Agirre-Gonzalez (University of the Basque Country)
21. MSR paraphrase corpus
● Widely used to evaluate text similarity algorithms
● Gleaned over a period of 18 months from
thousands of news sources on the web.
● 5801 pairs of sentences
● 70% train, 30% test
● 67% yes, %33 no
– completely unrelated semantically, partially overlapping, to
those that are almost-but-not-quite semantically equivalent.
● IAA 82%-84%
● (Dolan et al. 2004)
STS task - SemEval 2012 21
22. MSR paraphrase corpus
● The Senate Select Committee on Intelligence is preparing a
blistering report on prewar intelligence on Iraq.
● American intelligence leading up to the war on Iraq will be
criticised by a powerful US Congressional committee due to
report soon, officials said today.
● A strong geomagnetic storm was expected to hit Earth today
with the potential to affect electrical grids and satellite
communications.
● A strong geomagnetic storm is expected to hit Earth
sometime %%DAY%% and could knock out electrical grids
and satellite communications.
STS task - SemEval 2012 22
23. MSR paraphrase corpus
● Methodology:
● Rank pairs according to string similarity
– Algorithms for Approximate String Matching", E.
Ukkonen, Information and Control Vol. 64, 1985, pp. 100-
118.
● Five bands (0.8 – 0.4 similarity)
● Sample equal number of pairs from each band
● Repeat for paraphrases / non-paraphrases
● 50% from each
● 750 pairs for train, 750 pairs for test
STS task - SemEval 2012 23
24. MSR Video Description Corpus
● Show a segment of YouTube video
● Ask for one-sentence description of the main
action/event in the video (AMT)
● 120K sentences, 2,000 videos
● Roughly parallel descriptions (not only in English)
● (Chen and Dolan, 2011)
STS task - SemEval 2012 24
25. MSR Video Description Corpus
● A person is slicing a cucumber into
pieces.
● A chef is slicing a vegetable.
● A person is slicing a cucumber.
● A woman is slicing vegetables.
● A woman is slicing a cucumber.
● A person is slicing cucumber with
a knife.
● A person cuts up a piece of
cucumber.
● A man is slicing cucumber.
● A man cutting zucchini.
● Someone is slicing fruit.
STS task - SemEval 2012 25
26. MSR Video Description Corpus
● Methodology:
● All possible pairs from the same video
● 1% of all possible pairs from different videos
● Rank pairs according to string similarity
● Four bands (0.8 – 0.5 similarity)
● Sample equal number of pairs from each band
● Repeat for same video / different video
● 50% from each
● 750 pairs for train, 750 pairs for test
STS task - SemEval 2012 26
27. WMT: MT evaluation
● Pairs of segments (~ sentences) that had been part
of the human evaluation for WMT systems
● a reference translation
● a machine translation submission
● To keep things consistent, we just used French to
English system submissions translation
● Train contains pairs in WMT 2007
● Test contains pairs with less than 16 tokens from
WMT 2008
● Train and test come from Europarl
STS task - SemEval 2012 27
28. WMT: MT evaluation
● The only instance in which no tax is levied is
when the supplier is in a non-EU country and
the recipient is in a Member State of the EU.
● The only case for which no tax is still perceived
"is an example of supply in the European
Community from a third country.
● Thank you very much, Commissioner.
● Thank you very much, Mr Commissioner.
STS task - SemEval 2012 28
29. Surprise datasets
● human ranked fr-en system submissions from
the WMT 2007 news conversation test set,
resulting in 351 unique system reference pairs.
● The second set is radically different as it
comprised 750 pairs of glosses from OntoNotes
4.0 (Hovy et al., 2006) and WordNet 3.1
(Fellbaum, 1998) senses.
STS task - SemEval 2012 29
30. Pilot
● Mona, Dan, Eneko
● ~200 pairs from three datasets
● Pairwise agreement:
● GS:dan SYS:eneko N:188 Pearson: 0.874
● GS:dan SYS:mona N:174 Pearson: 0.845
● GS:eneko SYS:mona N:184 Pearson: 0.863
● Agreement with average of rest of us:
● GS:average SYS:dan N:188 Pearson: 0.885
● GS:average SYS:eneko N:198 Pearson: 0.889
● GS:average SYS:mona N:184 Pearson: 0.875
STS task - SemEval 2012 30
32. Pilot with turkers
● Average turkers with our average:
● N:197 Pearson: 0.959
● Each of us with average of turkers:
● dan N:187 Pearson: 0.937
● eneko N:197 Pearson: 0.919
● mona N:183 Pearson: 0.896
STS task - SemEval 2012 32
33. Working with AMT
● Requirements:
● 95% approval rating for their other HITs on AMT.
● To pass a qualification test with 80% accuracy.
– 6 example pairs
– answers were marked correct if they were within +1/-1 of our
annotations
● Targetting US, but used all origins
● HIT: 5 pairs of sentences, $ 0.20, 5 turkers per HIT
● 114.9 seconds per HIT on the most recent data we
submitted.
STS task - SemEval 2012 33
34. Working with AMT
● Quality control
● Each HIT contained one pair from our pilot
● After the tagging we check correlation of individual
turkers with our scores
● Remove annotations of low correlation turkers
– A2VJKPNDGBSUOK N:100 Pearson: -0.003
● Later realized that we could use correlation with
average of other Turkers
STS task - SemEval 2012 34