Distributional semantics models can provide probabilistic information about word meanings based on contextual clues. An agent can use distributional evidence to update its probabilistic information state about unknown words. In experiments, distributional similarity evidence increased the probability that properties of a known word (like "crocodile") also apply to an unknown word ("alligator"). Higher similarities led to more confident inferences. Combining multiple pieces of evidence further increased probabilities, allowing an agent to infer an unknown word refers to an animal based on its similarities to both "crocodile" and "trout".
This document proposes a framework to quantitatively describe relations between ideas using text corpora. It analyzes the co-occurrence, prevalence correlation, and strength of relations between ideas. Key ideas are represented as topics or keywords. The framework is demonstrated on datasets about immigration, terrorism, and machine translation, revealing relations like friendships, arms-races, and head-to-head competitions between ideas over time. The results are consistent with theories of structural balance and show the framework can effectively explore relations between ideas in temporal datasets.
This document provides an overview of word embeddings and the Word2Vec algorithm. It begins by establishing that measuring document similarity is an important natural language processing task, and that representing words as vectors is an effective approach. It then discusses different methods for representing words as vectors, including one-hot encoding and distributed representations. Word2Vec is introduced as a model that learns word embeddings by predicting words in a sentence based on context. The document demonstrates how Word2Vec can be used to find word similarities and analogies. It also references the theoretical justification that words with similar contexts have similar meanings.
Mouse tail of 27 mm
The document discusses the need for metadata and context to properly interpret data. It provides the example of the number "27" and explains that without additional information like the unit of measurement or what the number is associated with, the original data is meaningless. Later it is clarified that "27" refers to the length in millimeters of a mouse tail.
Intelligence tests have found average differences in IQ scores between racial groups, with Blacks and Hispanics scoring lower on average than Whites and Asians. There are several possible explanations for these observed differences:
1. Genetic differences cause racial groups to have different intelligence levels. However, within-group genetic variations do not fully explain between-group differences.
2. Environmental factors like socioeconomic status, stereotype threat, and test bias may impact scores. Studies matching Blacks and Whites on socioeconomic factors find smaller IQ differences.
3. Tests themselves could be racially biased in ways that disadvantage some groups. The causes of observed racial IQ differences remain controversial and complex, with both genetic and environmental factors likely playing
This document provides an overview of lectures for Week 6 on the genetic basis of evolution. The lectures will cover general introductions, defining key terms, genetic drift, and natural selection. Students are advised to read additional material on evolution. The lectures aim to move students away from overly simplistic "pan-selectionist" views and help them understand how genetic drift and natural selection both shape evolution. Genetic drift, the random changes in allele frequencies due to chance events in small populations, is a major factor in evolution and occurs in all populations.
Semantic nets are a knowledge representation scheme that uses nodes and labeled directed arcs to encode knowledge. Nodes represent objects, concepts, and events, while arcs represent relationships between nodes. Frames are a similar representation that uses slots and fillers to represent entities and their attributes. Both semantic nets and frames allow for inheritance of properties along relationships. More expressive description logics were later developed that combine frame-like representations with formal semantics and classification capabilities. Large knowledge bases like CYC have been created using these representations to encode common-sense knowledge.
This document provides an outline and content for a lecture on the genetic basis of evolution. The key points covered include:
- Genetic drift and natural selection both influence evolution but selection does not explain everything, as the "pan-selectionist" view suggests.
- Genetic drift, the random changes in allele frequencies between generations due to chance events, is an important evolutionary process that occurs in all populations. It accounts for genetic differences between individuals, populations, and species.
- Other topics that will be covered include defining terms like genes, loci, alleles, genotypes and phenotypes, and exploring the concepts of genetic drift and natural selection in more detail. The goal is to move beyond a "just-so"
This document proposes a framework to quantitatively describe relations between ideas using text corpora. It analyzes the co-occurrence, prevalence correlation, and strength of relations between ideas. Key ideas are represented as topics or keywords. The framework is demonstrated on datasets about immigration, terrorism, and machine translation, revealing relations like friendships, arms-races, and head-to-head competitions between ideas over time. The results are consistent with theories of structural balance and show the framework can effectively explore relations between ideas in temporal datasets.
This document provides an overview of word embeddings and the Word2Vec algorithm. It begins by establishing that measuring document similarity is an important natural language processing task, and that representing words as vectors is an effective approach. It then discusses different methods for representing words as vectors, including one-hot encoding and distributed representations. Word2Vec is introduced as a model that learns word embeddings by predicting words in a sentence based on context. The document demonstrates how Word2Vec can be used to find word similarities and analogies. It also references the theoretical justification that words with similar contexts have similar meanings.
Mouse tail of 27 mm
The document discusses the need for metadata and context to properly interpret data. It provides the example of the number "27" and explains that without additional information like the unit of measurement or what the number is associated with, the original data is meaningless. Later it is clarified that "27" refers to the length in millimeters of a mouse tail.
Intelligence tests have found average differences in IQ scores between racial groups, with Blacks and Hispanics scoring lower on average than Whites and Asians. There are several possible explanations for these observed differences:
1. Genetic differences cause racial groups to have different intelligence levels. However, within-group genetic variations do not fully explain between-group differences.
2. Environmental factors like socioeconomic status, stereotype threat, and test bias may impact scores. Studies matching Blacks and Whites on socioeconomic factors find smaller IQ differences.
3. Tests themselves could be racially biased in ways that disadvantage some groups. The causes of observed racial IQ differences remain controversial and complex, with both genetic and environmental factors likely playing
This document provides an overview of lectures for Week 6 on the genetic basis of evolution. The lectures will cover general introductions, defining key terms, genetic drift, and natural selection. Students are advised to read additional material on evolution. The lectures aim to move students away from overly simplistic "pan-selectionist" views and help them understand how genetic drift and natural selection both shape evolution. Genetic drift, the random changes in allele frequencies due to chance events in small populations, is a major factor in evolution and occurs in all populations.
Semantic nets are a knowledge representation scheme that uses nodes and labeled directed arcs to encode knowledge. Nodes represent objects, concepts, and events, while arcs represent relationships between nodes. Frames are a similar representation that uses slots and fillers to represent entities and their attributes. Both semantic nets and frames allow for inheritance of properties along relationships. More expressive description logics were later developed that combine frame-like representations with formal semantics and classification capabilities. Large knowledge bases like CYC have been created using these representations to encode common-sense knowledge.
This document provides an outline and content for a lecture on the genetic basis of evolution. The key points covered include:
- Genetic drift and natural selection both influence evolution but selection does not explain everything, as the "pan-selectionist" view suggests.
- Genetic drift, the random changes in allele frequencies between generations due to chance events, is an important evolutionary process that occurs in all populations. It accounts for genetic differences between individuals, populations, and species.
- Other topics that will be covered include defining terms like genes, loci, alleles, genotypes and phenotypes, and exploring the concepts of genetic drift and natural selection in more detail. The goal is to move beyond a "just-so"
Variation in speech tempo: Capt. Kirk, Mr. Spock, and all of us in betweenTyler Schnoebelen
Kendall (2009) shows that speech rate correlates to region, ethnicity, gender, and age. Beyond average rates, acceleration and deceleration matter. Psychologists and musicologists link tempo not only to demographic categories, but to emotions and personality-types, too.
An analysis of five Star Trek episodes shows how differences in tempo match differences in characters, from the highly variable tempos of a passionate Captain Kirk to the measured, burst-free speech of the emotionless Mr. Spock. The actors deploy tempo stylistically, creating emotions and personalities that audiences understand.
To calculate evenness and irregularity in tempo, I adapt measures of burstiness that network traffic engineering uses for packets traveling across the Internet: computing the variance in time between syllable nuclei in an utterance, then dividing the variance by 0.5*the number of syllables. The bigger the ratio, the more it is characterized by clusters.
Kirk’s burstiness differs significantly from his crew: at least four times greater than all the others at their burstiest. Everyone’s bursts correlate to emotional “hot spots”—areas of increased involvement (Çetin and Shriberg 2006).
I demonstrate that meanings of tempo are structured by two themes: (i) arousal (“action readiness”) and (ii) ideologies about time. These emerge not just in the Star Trek data but from building
indexical fields for “fast talk” and “slow talk.” In the spirit of Eckert (2008), the fields begin with proven correlations. I also develop a rapid survey methodology—results from 50 participants chart a constellation of ideological meanings that describe who talks fast/slow and when.
This paper differs from most work on social meaning by focusing on a suprasegmental aspect of speech. It also draws upon psychology, anthropology, musicology, and computer science. Its use of performances distills stylistic tempo from reflexive, cognitive effects, offering insights that assist our understanding of how tempo gets used in naturally-occurring speech.
This document discusses knowledge and truth. It presents different theories of truth, including correspondence theory, coherence theory, and pragmatic theory. It also distinguishes between knowledge and truth, asking if something can be known that is not true or true but not known. The document then discusses different ways of knowing, including reason, sense perception, intuition/imagination, language, emotion, testimony/authority, and imagination. It presents examples of applying different "tests of truthiness" like correspondence, coherence, and pragmatic to evaluate statements. Finally, it discusses using concepts from ways of knowing and tests of truth to apply to an issue like gun control in a blog response.
This document provides an outline for a lecture on the genetic basis of evolution. It begins with introducing key terms like gene, locus, allele, genotype, and phenotype. It then discusses genetic drift and how drift is influenced by population size. Selection is also introduced and defined as a process where individuals with different genotypes have different fitnesses. The document emphasizes that both genetic drift and selection influence evolution, and neither process should be overemphasized. It aims to move people away from only considering selection (pan-selectionism) and highlights the importance of genetic drift.
This presentation contains my one day lectures which introduces fuzzy set theory, operations on fuzzy sets, some engineering control applications using Mamdamn model.
You are-all-crazy-subjectivaly-speaking-uploaded-1224441527362216-8Manuela Pestana
This document contains the transcript from a presentation given by Alexander Johannesen on topic maps and subject-centric approaches to knowledge representation. Some key points discussed include:
- Topic maps help force one to thoughtfully consider the meaning of words, relations, and identity when defining information solutions.
- Subjects can represent anything that assertions may be made about, and topics are used to represent subjects. However, categories and constraints are also important considerations for knowledge representation.
- While topic maps support flexible knowledge modeling, the topic maps community needs to more openly share structures and data, as well as develop technologies and practices, to fully realize the potential of subject-centric approaches.
Dr. Lotfi Ali Asker Zadeh is considered the father of fuzzy logic. In the 1960s and 1970s, he developed the concept of fuzzy sets and fuzzy logic to deal with imprecise data and approximations. Fuzzy logic uses membership values between 0 and 1 rather than binary logic of true and false. It allows partial truth values to model uncertainty. Fuzzy logic has been applied in areas like control systems, decision making, and pattern recognition to handle imprecise concepts.
The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stanceannotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.
Write a short essay on Cat Essay Writing English - YouTube. cat essay writer by writetips - Issuu. My Pet Cat Essay Essay on My Pet Cat for Students and Children in .... Write an essay on cat in english - YouTube. cat essay writer - YouTube. Cat Essay Writer Service. My Pet Cat essay in English English Lessons for Beginners English .... Essay on Cats Essay, Cats, Education. 10 Lines on My Cat in English ll Essay on My Cat ll Essay Writing ll Essay in English ll Handwriting. Essay on Cat in English for Class 1, 2 and 3 Dr Noor Essays - YouTube. my pet cat essay in english Essay on my pet Essay on my cat .... Academic writing with CATS - YouTube. Story Essay Cat. My Pet Cat Essay and Paragraph - YouTube. My Pet Cat Essay for School kids Exams Nation. 11 Fascinating Essays on My Pet Cat For Students Student Essays. Essay on quot; The Catquot; Essay writing English essay English writing .... My Pet Cat Essay For Kids From Classes 3rd to 6th Earth Reminder. MY PET CAT ESSAY 2.docx - MY PET CAT ESSAY 2 300 WORDS Introduction My .... Essay My Pet Cat Essay Learning in English - YouTube. Paragraph on The Cat //Essay on The Cat - YouTube. Cat essay writer accurate court reporting. essays for cats - YouTube. Cat Essay for Class 1,2,3,4,5 - 10 Lines Essay for Kids. Dogs Are Better Than Cats Essay - Free Essay Example - 1011 Words .... The 1709 Blog: A conference in search of speakers: Copyright and .... My pet A short essay or few lines about cat in curvise handwriting .... My Favourite Book Essay For Class 6 In English / My Favourite Hobby My .... Persuasive Essay About Dogs Are Better Than Cats Sitedoct.org. Essay on cat for children 2 models Topics in English. An essay on cat - YouTube. 024 Essay Example Cat 699728 1280 For Thatsnotus. Essay on Cat for Kids and School Students - 10 Lines, 100, 150 to 200 .... Cat narrative essay english ShowMe. My pet cat essay writing Cat Essay Writer Cat Essay Writer
- The document discusses different approaches to defining word meaning, including lexicographic traditions of enumerating senses in dictionaries, ontological approaches using taxonomies of concepts, and distributional approaches using vector representations based on word context.
- It covers challenges with the traditional word sense disambiguation task, such as the skewed distribution of word senses and implicit disambiguation in context. Dimensionality reduction techniques and models like word2vec are discussed as distributional methods to learn word vectors from large corpora that capture semantic relationships.
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Uncertain Knowledge and reasoning by Mr.Abhishek Sharma, Research Scholar from Object Automation.
Iulia Pasov, Sixt. Trends in sentiment analysis. The entire history from rule...IT Arena
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
009 Essay Example Maxr. Online assignment writing service.Angelina Johnson
The document discusses the increasing use of nanomaterials in various fields including civil
engineering, as nanomaterials have unique mechanical, chemical, electronic and optical properties
that can improve materials like concrete, steel and glass. It reviews how nanomaterials like carbon
nanotubes, metal nanoparticles and metal oxide nanoparticles can enhance properties of concrete
like compressive strength, corrosion and abrasion resistance when added. The potential applications
of these nanomaterials in construction are explored to possibly improve building materials.
Scientific Method Lecture 1 WA UAM 1MA/2 sem/2013Barbara Konat
This document outlines the syllabus and structure for a course on scientific method taught by Barbara Konat. The course covers the scientific process, empirical research methods, and how to design a research study. It is divided into three modules: introduction to scientific inquiry, analyzing scientific articles, and developing a research plan. Students will work in groups to design and present their own research project at the end of the course. The document provides contact information for the instructor and notes on course assignments, presentations, and participation requirements.
In this slideshare I briefly review the topic of ergodicity and WEIRDness in Qualitative Research. Disclaimer: Past performance is not indicative of future results.
Essential human sciences in 2 lessons (with extension if required)Kieran Ryan
The document provides an overview of the key concepts in human sciences, including definitions, research methodologies, and approaches. It discusses the differences between human sciences and natural sciences, as well as three main approaches to research in human sciences: positivism, interpretivism, and critical theory. Examples are given to illustrate these approaches. Students are given tasks to match examples to different research approaches and consider reasons for differences between qualitative and quantitative research.
Essential human sciences in 2 lessons (with extension if required)Kieran Ryan
The document provides an overview of the key concepts in human sciences, including definitions, research methodologies, and approaches. It discusses the differences between human sciences and natural sciences, as well as three main approaches to research in human sciences: positivist, interpretivist, and critical theory. Examples are given for each approach. The document also describes some criticisms of human sciences from the perspective of natural sciences and discusses challenges around qualitative versus quantitative research.
A text extraction workshop delivered by Cameron Buckner on Friday, October 18th, 2012 as part of the University of Houston Digital Humanities Initiative.
Cognitive processes such as thinking, problem solving, language, and intelligence involve complex mental activities. Thinking refers to making sense of and changing the world through attention, mental representation, reasoning, judgment, and decision making. Problem solving uses strategies like algorithms, heuristics, analogies, and overcoming biases. Language allows for complex communication and shapes thought and culture. Theories of intelligence propose that it involves multiple abilities and can be analyzed through factors, domains, and problem-solving styles.
The document discusses the importance of thinking and reasoning. It provides three main points:
1. If we do not think for ourselves, others will think for us and control us, enslaving us and taking away our humanity. Therefore, thinking is essential to being human.
2. We are constantly bombarded with reasoning from various sources, so it is important to think critically and not just accept what others say. Studying logic helps improve our ability to think, reason, and evaluate arguments.
3. Political debates involve reasoning and arguing skills. These abilities can be refined through studying logic, which teaches how to identify fallacies and evaluate whether arguments' premises adequately support their conclusions.
This paper contributes a noun phrase-annotated SMS corpus and proposes a weak semi-Markov CRF model for noun phrase chunking in informal text. The weak semi-CRF model improves training speed over linear-CRF and semi-CRF models while maintaining similar accuracy. Experiments on the SMS corpus show the weak semi-CRF achieves F1 scores comparable to other models but trains faster, especially with larger training data sizes.
This document presents a new method for automatically detecting false friends between Spanish and Portuguese using word embeddings. The method builds word vector spaces for each language using word2vec, finds a linear transformation between the spaces, and measures vector distances to classify word pairs as cognates or false friends. In experiments on a dataset of 710 word pairs, the method achieved state-of-the-art accuracy of 77.28% and high coverage of 97.91%, outperforming previous work. Future work will explore using different word embeddings and fine-grained classifications of partial false friends.
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Variation in speech tempo: Capt. Kirk, Mr. Spock, and all of us in betweenTyler Schnoebelen
Kendall (2009) shows that speech rate correlates to region, ethnicity, gender, and age. Beyond average rates, acceleration and deceleration matter. Psychologists and musicologists link tempo not only to demographic categories, but to emotions and personality-types, too.
An analysis of five Star Trek episodes shows how differences in tempo match differences in characters, from the highly variable tempos of a passionate Captain Kirk to the measured, burst-free speech of the emotionless Mr. Spock. The actors deploy tempo stylistically, creating emotions and personalities that audiences understand.
To calculate evenness and irregularity in tempo, I adapt measures of burstiness that network traffic engineering uses for packets traveling across the Internet: computing the variance in time between syllable nuclei in an utterance, then dividing the variance by 0.5*the number of syllables. The bigger the ratio, the more it is characterized by clusters.
Kirk’s burstiness differs significantly from his crew: at least four times greater than all the others at their burstiest. Everyone’s bursts correlate to emotional “hot spots”—areas of increased involvement (Çetin and Shriberg 2006).
I demonstrate that meanings of tempo are structured by two themes: (i) arousal (“action readiness”) and (ii) ideologies about time. These emerge not just in the Star Trek data but from building
indexical fields for “fast talk” and “slow talk.” In the spirit of Eckert (2008), the fields begin with proven correlations. I also develop a rapid survey methodology—results from 50 participants chart a constellation of ideological meanings that describe who talks fast/slow and when.
This paper differs from most work on social meaning by focusing on a suprasegmental aspect of speech. It also draws upon psychology, anthropology, musicology, and computer science. Its use of performances distills stylistic tempo from reflexive, cognitive effects, offering insights that assist our understanding of how tempo gets used in naturally-occurring speech.
This document discusses knowledge and truth. It presents different theories of truth, including correspondence theory, coherence theory, and pragmatic theory. It also distinguishes between knowledge and truth, asking if something can be known that is not true or true but not known. The document then discusses different ways of knowing, including reason, sense perception, intuition/imagination, language, emotion, testimony/authority, and imagination. It presents examples of applying different "tests of truthiness" like correspondence, coherence, and pragmatic to evaluate statements. Finally, it discusses using concepts from ways of knowing and tests of truth to apply to an issue like gun control in a blog response.
This document provides an outline for a lecture on the genetic basis of evolution. It begins with introducing key terms like gene, locus, allele, genotype, and phenotype. It then discusses genetic drift and how drift is influenced by population size. Selection is also introduced and defined as a process where individuals with different genotypes have different fitnesses. The document emphasizes that both genetic drift and selection influence evolution, and neither process should be overemphasized. It aims to move people away from only considering selection (pan-selectionism) and highlights the importance of genetic drift.
This presentation contains my one day lectures which introduces fuzzy set theory, operations on fuzzy sets, some engineering control applications using Mamdamn model.
You are-all-crazy-subjectivaly-speaking-uploaded-1224441527362216-8Manuela Pestana
This document contains the transcript from a presentation given by Alexander Johannesen on topic maps and subject-centric approaches to knowledge representation. Some key points discussed include:
- Topic maps help force one to thoughtfully consider the meaning of words, relations, and identity when defining information solutions.
- Subjects can represent anything that assertions may be made about, and topics are used to represent subjects. However, categories and constraints are also important considerations for knowledge representation.
- While topic maps support flexible knowledge modeling, the topic maps community needs to more openly share structures and data, as well as develop technologies and practices, to fully realize the potential of subject-centric approaches.
Dr. Lotfi Ali Asker Zadeh is considered the father of fuzzy logic. In the 1960s and 1970s, he developed the concept of fuzzy sets and fuzzy logic to deal with imprecise data and approximations. Fuzzy logic uses membership values between 0 and 1 rather than binary logic of true and false. It allows partial truth values to model uncertainty. Fuzzy logic has been applied in areas like control systems, decision making, and pattern recognition to handle imprecise concepts.
The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stanceannotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.
Write a short essay on Cat Essay Writing English - YouTube. cat essay writer by writetips - Issuu. My Pet Cat Essay Essay on My Pet Cat for Students and Children in .... Write an essay on cat in english - YouTube. cat essay writer - YouTube. Cat Essay Writer Service. My Pet Cat essay in English English Lessons for Beginners English .... Essay on Cats Essay, Cats, Education. 10 Lines on My Cat in English ll Essay on My Cat ll Essay Writing ll Essay in English ll Handwriting. Essay on Cat in English for Class 1, 2 and 3 Dr Noor Essays - YouTube. my pet cat essay in english Essay on my pet Essay on my cat .... Academic writing with CATS - YouTube. Story Essay Cat. My Pet Cat Essay and Paragraph - YouTube. My Pet Cat Essay for School kids Exams Nation. 11 Fascinating Essays on My Pet Cat For Students Student Essays. Essay on quot; The Catquot; Essay writing English essay English writing .... My Pet Cat Essay For Kids From Classes 3rd to 6th Earth Reminder. MY PET CAT ESSAY 2.docx - MY PET CAT ESSAY 2 300 WORDS Introduction My .... Essay My Pet Cat Essay Learning in English - YouTube. Paragraph on The Cat //Essay on The Cat - YouTube. Cat essay writer accurate court reporting. essays for cats - YouTube. Cat Essay for Class 1,2,3,4,5 - 10 Lines Essay for Kids. Dogs Are Better Than Cats Essay - Free Essay Example - 1011 Words .... The 1709 Blog: A conference in search of speakers: Copyright and .... My pet A short essay or few lines about cat in curvise handwriting .... My Favourite Book Essay For Class 6 In English / My Favourite Hobby My .... Persuasive Essay About Dogs Are Better Than Cats Sitedoct.org. Essay on cat for children 2 models Topics in English. An essay on cat - YouTube. 024 Essay Example Cat 699728 1280 For Thatsnotus. Essay on Cat for Kids and School Students - 10 Lines, 100, 150 to 200 .... Cat narrative essay english ShowMe. My pet cat essay writing Cat Essay Writer Cat Essay Writer
- The document discusses different approaches to defining word meaning, including lexicographic traditions of enumerating senses in dictionaries, ontological approaches using taxonomies of concepts, and distributional approaches using vector representations based on word context.
- It covers challenges with the traditional word sense disambiguation task, such as the skewed distribution of word senses and implicit disambiguation in context. Dimensionality reduction techniques and models like word2vec are discussed as distributional methods to learn word vectors from large corpora that capture semantic relationships.
Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Uncertain Knowledge and reasoning by Mr.Abhishek Sharma, Research Scholar from Object Automation.
Iulia Pasov, Sixt. Trends in sentiment analysis. The entire history from rule...IT Arena
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
009 Essay Example Maxr. Online assignment writing service.Angelina Johnson
The document discusses the increasing use of nanomaterials in various fields including civil
engineering, as nanomaterials have unique mechanical, chemical, electronic and optical properties
that can improve materials like concrete, steel and glass. It reviews how nanomaterials like carbon
nanotubes, metal nanoparticles and metal oxide nanoparticles can enhance properties of concrete
like compressive strength, corrosion and abrasion resistance when added. The potential applications
of these nanomaterials in construction are explored to possibly improve building materials.
Scientific Method Lecture 1 WA UAM 1MA/2 sem/2013Barbara Konat
This document outlines the syllabus and structure for a course on scientific method taught by Barbara Konat. The course covers the scientific process, empirical research methods, and how to design a research study. It is divided into three modules: introduction to scientific inquiry, analyzing scientific articles, and developing a research plan. Students will work in groups to design and present their own research project at the end of the course. The document provides contact information for the instructor and notes on course assignments, presentations, and participation requirements.
In this slideshare I briefly review the topic of ergodicity and WEIRDness in Qualitative Research. Disclaimer: Past performance is not indicative of future results.
Essential human sciences in 2 lessons (with extension if required)Kieran Ryan
The document provides an overview of the key concepts in human sciences, including definitions, research methodologies, and approaches. It discusses the differences between human sciences and natural sciences, as well as three main approaches to research in human sciences: positivism, interpretivism, and critical theory. Examples are given to illustrate these approaches. Students are given tasks to match examples to different research approaches and consider reasons for differences between qualitative and quantitative research.
Essential human sciences in 2 lessons (with extension if required)Kieran Ryan
The document provides an overview of the key concepts in human sciences, including definitions, research methodologies, and approaches. It discusses the differences between human sciences and natural sciences, as well as three main approaches to research in human sciences: positivist, interpretivist, and critical theory. Examples are given for each approach. The document also describes some criticisms of human sciences from the perspective of natural sciences and discusses challenges around qualitative versus quantitative research.
A text extraction workshop delivered by Cameron Buckner on Friday, October 18th, 2012 as part of the University of Houston Digital Humanities Initiative.
Cognitive processes such as thinking, problem solving, language, and intelligence involve complex mental activities. Thinking refers to making sense of and changing the world through attention, mental representation, reasoning, judgment, and decision making. Problem solving uses strategies like algorithms, heuristics, analogies, and overcoming biases. Language allows for complex communication and shapes thought and culture. Theories of intelligence propose that it involves multiple abilities and can be analyzed through factors, domains, and problem-solving styles.
The document discusses the importance of thinking and reasoning. It provides three main points:
1. If we do not think for ourselves, others will think for us and control us, enslaving us and taking away our humanity. Therefore, thinking is essential to being human.
2. We are constantly bombarded with reasoning from various sources, so it is important to think critically and not just accept what others say. Studying logic helps improve our ability to think, reason, and evaluate arguments.
3. Political debates involve reasoning and arguing skills. These abilities can be refined through studying logic, which teaches how to identify fallacies and evaluate whether arguments' premises adequately support their conclusions.
Similar to Katrin Erk - 2017 - What do you know about an alligator when you know the company it keeps? (20)
This paper contributes a noun phrase-annotated SMS corpus and proposes a weak semi-Markov CRF model for noun phrase chunking in informal text. The weak semi-CRF model improves training speed over linear-CRF and semi-CRF models while maintaining similar accuracy. Experiments on the SMS corpus show the weak semi-CRF achieves F1 scores comparable to other models but trains faster, especially with larger training data sizes.
This document presents a new method for automatically detecting false friends between Spanish and Portuguese using word embeddings. The method builds word vector spaces for each language using word2vec, finds a linear transformation between the spaces, and measures vector distances to classify word pairs as cognates or false friends. In experiments on a dataset of 710 word pairs, the method achieved state-of-the-art accuracy of 77.28% and high coverage of 97.91%, outperforming previous work. Future work will explore using different word embeddings and fine-grained classifications of partial false friends.
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Katrin Erk - 2017 - What do you know about an alligator when you know the company it keeps?
1. What do you know about
an alligator when you know
the company it keeps?
Katrin Erk
University of Texas at Austin
STARSEM 2017
2. Distributional semantics and
you
• Distributional models/Embeddings: An incredible
success story in computational linguistics
• Do you make use of distributional information, too?
• Landauer & Dumais, 1997: “A solution to Plato’s problem”
• How do humans acquire such a gigantic vocabulary in such a
short time?
• Much debate in psychology,
experimental support: McDonald&Ramscar, 2001,
Lazaridou et al, 2016
• But how about the linguistic side of the story?
3. “A solution to Plato’s problem”
“Many well-read adults know that Buddha sat long
under a banyan tree (whatever that is) and Tahitian
natives lived idyllically on breadfruit and poi (whatever
those are). More or less correct usage often precedes
referential knowledge” (Landauer&Dumais, 1997)
4. “A solution to Plato’s problem”
“Many well-read adults know that Buddha sat long
under a banyan tree (whatever that is) and Tahitian
natives lived idyllically on breadfruit and poi (whatever
those are). More or less correct usage often precedes
referential knowledge” ” (Landauer&Dumais, 1997)
But wait: How can you use the word “banyan” more or
less correctly when you are not aware of its reference?
When you couldn’t point out a banyan in a yard?
5. Learning about word meaning
from textual context
• Main aim: insight
• What information is present in distributional
representations, and why?
• Assuming a learner with grounded concepts:
How can distributional information contribute?
6. Learning about meaning from
textual context
Suppose you do not know what an alligator is. What
do these sentence tell you about alligators?
• On our last evening, the boatman killed an alligator as
it crawled past our camp-fire to go hunting in the reeds
beyond.
• A study done by Edwin Colbert and his colleagues
showed that a tiny 50 gramme (1.76 oz) alligator heated
up 1◦C every minute and a half from the Sun[…]
• The throne was occupied by a pipe-smoking alligator.
7. Learning about word meaning
from textual context
• Setting: adult learner
• What kind of information can you get from text?
• How does it enable you to use “alligator” more or less
correctly?
• Why can you learn anything from text?
• Textual clues are rarely 100% reliable
• “An alligator was lying at the bottom of a pool”
• Could be an animal, a pool-cleaning implement…
8. The story in a nutshell
• How can I successfully use the word “alligator”
when I don’t know what it refers to?
• I know some properties of alligators: they are
animals, dangerous, …
• So then I use “alligator” in animal-like textual
contexts
9. The story in a nutshell
• How does distributional information help?
• It lets me infer properties of words:
• Suppose I don’t know what an alligator is
• But it appears in similar contexts as “crocodile”
• So it must be something like a crocodile:
• That is, it must share properties with a crocodile
• So it may be an animal, it may be dangerous…
10. The story in a nutshell
• But distributional information can never yield
certain knowledge
• Instead uncertain, probabilistic information
• Formal semantics framework
• Probabilistic semantics:
• Probability distribution over worlds that could be the
current one
• Probability of a world influenced by distributional
information
11. Plan
• What can an agent learn from distributional context?
• A probabilistic information state
• Influencing a probabilistic information state with
distributional information
• A toy experiment
12. What is in an embedding?
• What information can be encoded in an embedding
computed from text data?
• Lots of things, given the right objective function
• But:
• What objective function can we assume a human agent
to use?
• What individual linguistic phenomena have been
shown to be encoded?
• So, restrict ourselves to simple model
13. What is in an embedding?
• Count-based models of textual context
• (and neural models like word2vec,
see Levy&Goldberg 2015)
• Long-time criticism in psychology, eg. Murphy (2002):
only a vague notion of “similarity”
• But in fact distributional models can distinguish between
semantic relations
• by choice of what “context” means
• through relation-specific classifiers (Fu et al, 2014; Levy et al,
2015; Shwartz et al, 2016; Roller& Erk, 2016, …)
14. The effect of context window size
• Peirsman 2008 (Dutch):
• Narrow context window: high ratings to “similar” words
• Particularly to co-hyponyms
• Syntactic context even more so
• Wide context window: high ratings to “related” words
• Baroni/Lenci 2011 (English):
• Narrow context window: highest ratings to co-hyponyms
• Wide context window: ratings equal across many relations
15. What is narrow-window
similarity?
• High ratings for co-hyponyms, also synonyms, some
hypernyms, antonyms (well-known bug)
• What semantic relation is that?
• Co-hyponymy is an odd relation
• dictionary-specific
• can be incompatible (cat/dog) or compatible
(hotel/restaurant)
• Proposal: property overlap
• Alligator, crocodile have many properties in common:
animal, reptile, scaly, dangerous, …
16. Why does narrow-window
similarity do this?
• Focus on noun targets
• Narrow window, syntactic context contain:
• Modifiers
• Verbs that take target as argument
• Selectional constraints
• Traditionally formulated in terms of taxonomic
properties
• subject of “crawl”: animate
17. But wait, where do the
probabilities come from?
• Frequency in text is not frequency in real life
• Reporting bias: Almost no one says “Bananas are
yellow” (Bruni et al, 2012)
• Genre bias: “captive” and “westerner” respective
nearest neighbors in Lin 1998
• Then how can counts in text lead us to probabilities
relevant to grounded concepts?
18. But wait, where do the
probabilities come from?
• Two tricks in this study
1. Only consider properties that apply to all members of
a category (like “being an animal”)
2. Use distributional context only indirectly: Learn
correlation between distributional context and real-
world properties
• More recent work: trick 2 without trick 1
• I think we can use distributional context directly
and properly to get probabilities – more later
19. Learning properties from
distributional data
• Concrete noun concepts
• To learn: properties of a concept
• Focus on properties applying to all members of a
category (like taxonomic properties)
• Broad definition of a property: can be expressed as an
adjective, can be a hypernym, …
20. Property overlap
• Percentage of properties that are joint
• Jaccard coefficient on sets
• A, B, sets of properties:
• Degrees of property overlap
• Idea: The more properties in common, the higher the
distributional similarity
Jac(A, B) =
|A B|
|A [ B|
jac = 2 / 6 = 0.33
21. Plan
• What can an agent learn from distributional context?
• A probabilistic information state
• Influencing a probabilistic information state with
distributional information
• A toy experiment
22. Information states
• Information state of Agent: set of worlds that the agent
considers possibilities
• Agent not omniscient
• As far as Agent is concerned, any of these worlds could be
the actual world
• Update semantics: Information state updated through
communication (Veltman 1996)
• Probabilistic information state: probability distribution
over worlds (van Benthem et al. 2009, Zeevat 2013)
• Not all worlds equally likely to be the actual world
23. Probabilistic logics
• Uncertainty about the world we are in
• Probability distribution over worlds
• Nilsson 1986
• Probability that a sentence is true depends on the
probabilities of the worlds in which it is true
P(') =
X
w:||'||w=t
P(w)
24. Generating a probability
distribution over worlds
• Text understanding as a generative process
• Agent mentally simulates (i.e., probabilistically
generates) the situation described in the text
• Goodman et al, 2015; Goodman and Lassiter, 2016
• To generate a person:
• draw gender: flip a fair coin
• draw height from the normal distribution of heights for
that gender.
25. Properties in a probabilistic
information state
• Property applies in a particular world: extension of
predicate included in extension of property in that
world
• Focus here: Properties that the agent is certain
about: apply in all worlds that have non-zero
probability
26. Plan
• What can an agent learn from distributional context?
• A probabilistic information state
• Influencing a probabilistic information state with
distributional information
• A toy experiment
27. Bayesian update on the probability
distribution over worlds
• Prior distribution over worlds P0
• Then we see distributional evidence Edist
• e.g.: Distributional similarity of “crocodile” and
“alligator” is 0.93
• Posterior distribution P1 given Edist
• How do we determine the likelihood?
P1(w) = P(w|Edist) =
P(Edist|w)P0(w)
P(Edist)
28. Interpreting distributional data
• Speaker observes words with known properties,
and their
distributional
similarity
Property overlap from McRae feature norms (McRae et al 2005).
Similarities from a narrow-context model computed on UKWaC+
Wikipedia+BNC
word 1 word 2 ovl sim
peacock raven 0.29 0.70
mixer toaster 0.19 0.72
crocodile frog 0.17 0.86
bagpipe banjo 0.10 0.72
scissors typewriter 0.04 0.62
crocodile lime 0.03 0.33
coconut porcupine 0.03 0.42
29. Observing regularities: high property overlap
goes with high distributional similarity
word 1 word 2 ovl sim
peacock raven 0.29 0.70
mixer toaster 0.19 0.72
crocodile frog 0.17 0.86
bagpipe banjo 0.10 0.72
scissors typewriter 0.04 0.62
crocodile lime 0.03 0.33
coconut porcupine 0.03 0.42
0.05 0.10 0.15 0.20 0.25 0.30
0.20.61.0
Property overlap versus
similarity (artificial data)
property overlap
dist.sim.
In the simplest case:
linear regression.
30. Given the regularities I observed, and the
distributional evidence, what do I now
think of world w?
• World w:
• property overlap of crocodile and alligator is o = 0.1
• Predicted similarity:
• Distributional evidence: sim(crocodile, alligator) = 0.93
• How likely are we to observe a distributional
similarity of 0.93 if the predicted similarity is 0.53?
• Standard move in hypothesis testing: How likely to
see an observed value this high or higher
given the predicted distribution?
0 + 1o = 0.53
31. Likelihood of the distributional
evidence in this world
• What distribution?
• Equivalent view of linear regression:
Observed similarity = predicted similarity + normally
distributed error
• Normal distribution with mean
f(o) = 0 + 1o
0.00.10.20.30.4
dist.rating
prob.density
f(o)
0.00.10.20.30.4
prob.density
32. Likelihood of the distributional
evidence in this world
• Distributional similarity s = sim(crocodile, alligator)
• Hypothesis testing: How likely to see similarity value
as high as s or higher given property overlap o?
0.00.10.20.30.4
prob.density
f(o)
0.00.10.20.30.4
prob.density
f(o) s
33. Computing posterior probabilities in
a probabilistic generative framework
• Probabilistically generate worlds:
• “To generate a person, flip a fair coin to determine their
gender…”
• Approximately determine probability distribution
over worlds: Sample n probabilistically generated
worlds
• Sample from posterior:
• Rejection sampling
• Formulate likelihood as a sampling condition
34. Computing posterior probabilities in
a probabilistic generative framework
• Property overlap o between crocodiles and alligators
in world w
• Distributional similarity s = sim(crocodile, alligator)
• Keep w if similarity as high as s or higher is likely
given o
• Sample s’ from the normal
distribution with mean f(o)
• Keep world w if s’ >= s
0.00.10.20.30.4
prob.density
f(o) 0.00.10.20.30.4
prob.density
f(o) s
35. Plan
• What can an agent learn from distributional context?
• A probabilistic information state
• Influencing a probabilistic information state with
distributional information
• A toy experiment
36. Toy experiments
• Property collection: McRae et al., 2005
• Human-generated definitional features for concrete noun
properties
• Distributional model: narrow context, UKWaC + Wikipedia +
BNC
• Hold out alligator as unknown word
• Given distributional evidence, how likely are we to believe…
1. All alligators are dangerous
2. All alligators are edible
3. All alligators are animals
37. Toy experiments
• All alligators are dangerous:
• Known word: crocodile. sim(alligator, crocodile) = 0.93
• Crocodiles are animals, dangerous, scaly, and crocodiles
• All alligators are edible:
• Known word: trout. sim(alligator, trout) = 0.68
• Trouts are animals, aquatic, edible, and trouts
• Probability should be lower because similarity is lower
• All alligators are animals:
• Known words: crocodile, trout.
• Can evidence accumulate with multiple similarity ratings?
38. Generative story for the
prior probability
• Fix domain size to 10
• For each entity in the domain:
• Flip a fair coin to determine if it is a crocodile. Likewise for
alligator.
• For each entity in the domain:
• If it is a crocodile, it is also an animal, dangerous, and scaly.
• Otherwise, flip a fair coin to see if it is an animal (dangerous,
scaly).
Implemented in Church.
39. Results: All alligators are…
Sentence words sim prior posterior
. . . dangerous alligator,
crocodile
0.93 0.26 0.47
. . . edible alligator, trout 0.68 0.26 0.38
• Aim: Significant increase in probability
• Absolute probabilities depend on domain size,
problem formulation
• Higher similarities lead to significantly more confident inferences
• “Crocodile” much more similar to “alligator” than “trout”:
Agent more confidently ascribes crocodile properties to alligators
40. Probability of property
overlap: prior versus posterior
0 0.2 0.4 0.6 0.8 1
no dist. evidence
with dist. evidence
Property overlap of 'alligator' and 'crocodile'
prop. overlap
num.worlds
0200400600800
0 0.2 0.4 0.6 0.8 1
no dist. evidence
with dist. evidence
Property overlap of 'alligator' and trout'
prop. overlap
num.worlds
0200400600800
Alligator vs crocodile Alligator vs trout
prior
posterior
41. Accumulating evidence:
“All alligators are animals”
sim of alligator to. . . prior posterior
crocodile: 0.93 0.53 0.68
trout: 0.68 0.53 0.63
crocodile: 0.93,
trout: 0.68
0.53 0.80
• Does distributional evidence accumulate?
• Both crocodiles and trouts are known to be animals
• Posterior significantly higher
when two pieces of evidence present
42. Summary
• How can people use a word whose reference they don’t
know?
• Suppose we don’t know what an alligator is, can we still
infer from context clues that it’s an animal?
• Proposal:
• (Narrow-window) distributional evidence is property overlap
evidence
• Distributional evidence affects probabilistic information state
• Can be described in probabilistic generative framework
43. Next questions
• Learning from a single sentence only
• On our last evening, the boatman killed an alligator as it
crawled past our camp-fire to go hunting in the reeds beyond.
• Distributional one-shot learning
• Doable: same setup, learn McRae et al. definitional features
using selectional constraints of neighboring predicates
• Properties that do not apply to all members of a category
• Some but not all crocodiles are dangerous
• Learn probability of generating a property for “alligator”
44. Next questions
• Here: Learn from context only indirectly,
from correlation with grounded properties
• Can we learn from what is said in the text?
• On our last evening, the boatman killed an alligator as it
crawled past our camp-fire to go hunting in the reeds beyond.
• Alligators are entities that generally crawl, hunt, and are
found in reeds
• P(q is a generic property of alligators that would be
mentioned by people)
• Relevant to “human experience of alligators”
(Thill/Padó/Ziemke 2014)
45. Thanks
Gemma Boleda, Louise McNally, Judith Tonhauser
(best editor on earth!), Nicholas Asher, Marco Baroni,
David Beaver, John Beavers, Ann Copestake, Ido
Dagan, Aurélie Herbelot, Hans Kamp, Alexander
Koller, Alessandro Lenci, Sebastian Löbner, Julian
Michael, Ray Mooney, Sebastian Padó, Manfred
Pinkal, Stephen Roller, Hinrich Schütze, Jan van Eijck,
Leah Velleman, Steve Wechsler, Roberto Zamparelli,
and the Foundations of Semantic Spaces reading group