The document summarizes Elisavet Palogiannidi's thesis presentation on affective analysis and modeling of spoken dialogue transcripts. The presentation includes an introduction to affective models, experiments conducted, and results. It discusses contributions such as creating the first Greek Affective Lexicon and extending the Semantic Affective Model to multiple languages. The Semantic Affective Model is described as mapping semantic similarity to affective similarity using a small annotated lexicon. Compositional and sentence-level affective models are also presented.
Extraction of Socio-Semantic Data from Chat Conversations in Collaborative Le...Traian Rebedea
The document summarizes research on extracting socio-semantic data from chat conversations in collaborative learning communities. The goals are to automatically determine relationships between utterances, assess learners' competencies, and visualize the conversation graph. Key techniques include detecting topics, discovering implicit references between utterances, and representing the conversation as a directed acyclic graph to identify important utterances and discussion threads. The work integrates ideas from sociocultural learning theory, natural language processing, and machine learning.
This document outlines the process of adapting tests for use in different languages and cultures. It involves translation and back translation with several steps: 1) forward translation to the target language, 2) review by an expert panel, 3) back translation to the original language, 4) pre-testing and cognitive interviews, 5) creating the final version, and 6) documentation. The goal is to produce versions of the test that are conceptually equivalent across languages and cultures while maintaining validity. An example is given of adapting the PISA test for use in many different countries.
Sarcasm & Thwarting in Sentiment Analysis [IIT-Bombay]Sagar Ahire
1) The document discusses various linguistic phenomena including irony, sarcasm, and thwarting. It presents algorithms for detecting sarcasm and thwarting in text.
2) For sarcasm detection, a semi-supervised algorithm uses pattern-based and punctuation-based features to classify sentences, achieving up to 81% accuracy.
3) Thwarting detection compares sentiment across levels of a domain ontology, using either rule-based or machine learning approaches, with the latter approach achieving up to 81% accuracy.
Words can have more than one distinct meaning and many words can be interpreted in multiple ways
depending on the context in which they occur. The process of automatically identifying the meaning of
a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This
phenomenon poses challenges to Natural Language Processing systems. There have been many efforts
on word sense disambiguation for English; however, the amount of efforts for Amharic is very little.
Many natural language processing applications, such as Machine Translation, Information Retrieval,
Question Answering, and Information Extraction, require this task, which occurs at the semantic level.
In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet
is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations
among words and senses. The proposed system consists of preprocessing, morphological analysis and
disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the
input sentence for morphological analysis and morphological analysis is used to reduce various forms
of a word to a single root or stem word. Amharic WordNet contains words along with its different
meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is
used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a
sentence using Amharic WordNet by using sense overlap and related words.
We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic
WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic
WordNet with and without morphological analyzer and the second one is determining an optimal
windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic
WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%,
respectively. In the second experiment, we have found that two-word window on each side of the
ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD
methods have performed better than previous Amharic WSD methods.
Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation,
Knowledge Based Approach, Lesk Algorithm
Human Evaluation: Why do we need it? - Dr. Sheila CastilhoSebastian Ruder
Talk at the 8th NLP Dublin meetup (https://www.meetup.com/NLP-Dublin/events/241198412/) by Dr. Sheila Castilho, postdoc at ADAPT Centre, Dublin City University.
The document presents the results of an experiment that studied how different task design factors impact the quality and diversity of crowdsourced paraphrases. It found that priming workers by providing examples increased correctness and diversity, while prompts containing jargon or requiring multiple paraphrases from a single source decreased quality. The study aims to help understand how to better design crowdsourcing tasks for collecting paraphrases.
This document summarizes an English Education course on theories of translating offered at a university in Indonesia. The course aims to provide students with theoretical and practical knowledge of translation processes, methods, and ethics. Over the course of 16 weeks, topics will include an overview of translation theories, techniques for translating different text types, cultural adaptation, and the use of computer-assisted tools. Assessment will include class participation, translation assignments, a midterm exam, and a final exam. Students are expected to develop their skills in translating a variety of texts from English to Indonesian and vice versa.
The Presentation contains about Word Sense Diassambiguation. I had tried to explain about the Word Sense in terms of Python language. But it can be also done using nltk.
Extraction of Socio-Semantic Data from Chat Conversations in Collaborative Le...Traian Rebedea
The document summarizes research on extracting socio-semantic data from chat conversations in collaborative learning communities. The goals are to automatically determine relationships between utterances, assess learners' competencies, and visualize the conversation graph. Key techniques include detecting topics, discovering implicit references between utterances, and representing the conversation as a directed acyclic graph to identify important utterances and discussion threads. The work integrates ideas from sociocultural learning theory, natural language processing, and machine learning.
This document outlines the process of adapting tests for use in different languages and cultures. It involves translation and back translation with several steps: 1) forward translation to the target language, 2) review by an expert panel, 3) back translation to the original language, 4) pre-testing and cognitive interviews, 5) creating the final version, and 6) documentation. The goal is to produce versions of the test that are conceptually equivalent across languages and cultures while maintaining validity. An example is given of adapting the PISA test for use in many different countries.
Sarcasm & Thwarting in Sentiment Analysis [IIT-Bombay]Sagar Ahire
1) The document discusses various linguistic phenomena including irony, sarcasm, and thwarting. It presents algorithms for detecting sarcasm and thwarting in text.
2) For sarcasm detection, a semi-supervised algorithm uses pattern-based and punctuation-based features to classify sentences, achieving up to 81% accuracy.
3) Thwarting detection compares sentiment across levels of a domain ontology, using either rule-based or machine learning approaches, with the latter approach achieving up to 81% accuracy.
Words can have more than one distinct meaning and many words can be interpreted in multiple ways
depending on the context in which they occur. The process of automatically identifying the meaning of
a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This
phenomenon poses challenges to Natural Language Processing systems. There have been many efforts
on word sense disambiguation for English; however, the amount of efforts for Amharic is very little.
Many natural language processing applications, such as Machine Translation, Information Retrieval,
Question Answering, and Information Extraction, require this task, which occurs at the semantic level.
In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet
is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations
among words and senses. The proposed system consists of preprocessing, morphological analysis and
disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the
input sentence for morphological analysis and morphological analysis is used to reduce various forms
of a word to a single root or stem word. Amharic WordNet contains words along with its different
meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is
used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a
sentence using Amharic WordNet by using sense overlap and related words.
We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic
WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic
WordNet with and without morphological analyzer and the second one is determining an optimal
windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic
WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%,
respectively. In the second experiment, we have found that two-word window on each side of the
ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD
methods have performed better than previous Amharic WSD methods.
Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation,
Knowledge Based Approach, Lesk Algorithm
Human Evaluation: Why do we need it? - Dr. Sheila CastilhoSebastian Ruder
Talk at the 8th NLP Dublin meetup (https://www.meetup.com/NLP-Dublin/events/241198412/) by Dr. Sheila Castilho, postdoc at ADAPT Centre, Dublin City University.
The document presents the results of an experiment that studied how different task design factors impact the quality and diversity of crowdsourced paraphrases. It found that priming workers by providing examples increased correctness and diversity, while prompts containing jargon or requiring multiple paraphrases from a single source decreased quality. The study aims to help understand how to better design crowdsourcing tasks for collecting paraphrases.
This document summarizes an English Education course on theories of translating offered at a university in Indonesia. The course aims to provide students with theoretical and practical knowledge of translation processes, methods, and ethics. Over the course of 16 weeks, topics will include an overview of translation theories, techniques for translating different text types, cultural adaptation, and the use of computer-assisted tools. Assessment will include class participation, translation assignments, a midterm exam, and a final exam. Students are expected to develop their skills in translating a variety of texts from English to Indonesian and vice versa.
The Presentation contains about Word Sense Diassambiguation. I had tried to explain about the Word Sense in terms of Python language. But it can be also done using nltk.
This document outlines the agenda and instructions for an upcoming group presentation assignment. Students are instructed to form groups of 4-5 members to collaborate on a 7-10 minute problem/solution presentation about challenges International Engineering Cooperative Program (IECP) students may face transitioning to an American university and possible solutions. The presentation should be supported by evidence from each group member's observations of Penn State classes and interviews with Penn State students. Guidelines are provided on brainstorming challenges and solutions, preparing the introduction, explanations, proposed solutions, counter objections, and conclusion. The presentation will be evaluated on content, structure, delivery, language usage, and visual aids.
Partial Models: Towards Modeling and Reasoning with UncertaintyMichalis Famelis
This document discusses modeling and reasoning with uncertainty. The authors propose encoding uncertainty using partial models, which represent sets of conventional models. They describe checking properties of partial models by encoding them in propositional logic and using a SAT solver. The authors also discuss giving feedback to facilitate diagnosing properties. They aim to evaluate reasoning with partial models versus reasoning with sets of conventional models.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
Sentiment Analysis in Twitter with Lightweight Discourse AnalysisSubhabrata Mukherjee
Sentiment Analysis in Twitter with Lightweight Discourse Analysis, Subhabrata Mukherjee and Pushpak Bhattacharyya, In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), IIT Bombay, Mumbai, Dec 8 - Dec 15, 2012 (http://www.cse.iitb.ac.in/~pb/papers/coling12-discourse-sa.pdf)
This document discusses introducing personality to argumentative agents. It begins by providing background on argumentation dialogues and current frameworks used to model these, including Dung's abstract framework and ASPIC+. It then discusses modeling personality based on the five factor model (FFM), which describes personality along five traits. A personality model is proposed that assigns strengths to relevant FFM facets and defines how these map to preferences over speech acts and attitudes in argumentation. The goal is to allow agents to reason according to their personality configuration. Research questions are posed around implementing this model in agents and modeling opponents' personalities.
The central issue in test translations and adaptations is producing instruments that adequately measure target constructs across cultures. There are two main perspectives on equivalence - linguistic equivalence focuses on similarity of linguistic features, while psychological equivalence focuses on similarity of meaning and scores. A good translation combines high levels of construct, cultural, linguistic, and measurement equivalence. There is no single best approach, as the optimal method depends on the specific case. Multiple procedures can be used together to evaluate translation accuracy.
The document describes guidelines for translating and adapting tests published by the International Test Commission (ITC) in 2005. It acknowledges contributions from several international organizations in developing the guidelines over several years. The guidelines are structured in four categories and consist of 22 statements to provide a framework for translating, adapting, administering, and interpreting tests across languages and cultures. The guidelines have been referenced in several publications and aim to standardize best practices in test translation and adaptation.
The document discusses recent advances in natural language processing (NLP). It begins with an introduction to the presenter and their background and credentials working in NLP, machine learning, and deep learning. It then provides a brief definition of NLP, describing it as programming computers to process large amounts of natural language at the intersection of computer science, artificial intelligence, and computational linguistics. The document goes on to provide several examples of recent NLP applications, technologies, and research topics, such as sentiment analysis, spell checking, machine translation, story generation from images and text, and using word embeddings and document vectors for visualization. It closes by acknowledging that while recent successes exist, general human-level NLP remains a significant challenge that will require
PRONOUN DISAMBIGUATION: WITH APPLICATION TO THE WINOGRAD SCHEMA CHALLENGEkevig
A value-based approach to Natural Language Understanding, in particular, the disambiguation of
pronouns, is illustrated with a solution to a typical example from the Winograd Schema Challenge. The
worked example uses a language engine, Enguage, to support the articulation of the advocation and
fearing of violence. The example illustrates the indexical nature of pronouns, and how their values, their
referent objects, change because they are set by contextual data. It must be noted that Enguage is not a
suitable candidate for addressing the Winograd Schema Challenge as it is an interactive tool, whereas
the Challenge requires a preconfigured, unattended program.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introductions to the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques. The workshop is divided into four modules: word vectors, sentence/paragraph/document vectors, and character vectors. The document provides background on why text representation is important for NLP, and discusses older techniques like one-hot encoding, bag-of-words, n-grams, and TF-IDF. It also introduces newer distributed representation techniques like word2vec's skip-gram and CBOW models, GloVe, and the use of neural networks for language modeling.
Recent advances in technology have caused a proliferation of data and knowledge sources on a global scale. The ability to access and integrate these knowledge sources is crucial for critical decision making, and to facilitate this, knowledge-based intelligent applications (agents) need to resolve the differences between their knowledge models (ontologies).
We present preliminary work that allows two agents to jointly determine a single correspondence between two concepts in their respective ontologies, without the need for prior joint knowledge. The agents engage in a dialogue that permits the participants to exchange information about the concepts to support the assertion or rejection of a correspondence.
This paper was presented at the 15th Workshop on Computational Models of Natural Argument, 2015.
More details can be found at http://www.csc.liv.ac.uk/~trp/Knowledge-Based-Agents.html
This document outlines the stages of translating and adapting instruments across cultures and languages. It discusses:
1) Having documents translated independently by 2 translators and synthesizing the translations.
2) Evaluating the synthesized version with experts and the target population for comprehension.
3) Conducting back translations to check for consistency with the original.
4) Pilot testing the adapted instrument.
5) Validating the adapted instrument through statistical analyses like confirmatory factor analysis to ensure it measures the same constructs as reliably as the original. Cross-cultural validation is important for meaningful comparisons between groups.
This document discusses deploying a private Docker registry using Docker registry. It provides instructions on installing Docker registry using both Python and Go versions, configuring it as a service, setting up authentication and SSL, connecting Docker hosts to the registry, and running the Docker registry frontend tool.
End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Lan...Yun-Nung (Vivian) Chen
The document describes an end-to-end memory network model for multi-turn spoken language understanding. The model encodes context from previous utterances using an attention mechanism over the memory of past utterances. It then performs slot tagging on the current utterance incorporating the contextual knowledge. Experiments on a Cortana dataset show the model outperforms alternatives, achieving 67.1% accuracy by encoding both history and current utterances with the memory network.
The document discusses statistical learning from dialogues for intelligent assistants. It describes how spoken dialogue systems process user requests through steps like speech recognition, language understanding, dialogue management and response generation. It highlights current challenges like requiring hand-crafted domain knowledge and labeled data. The author's contributions include methods for automated knowledge acquisition from unlabeled dialogues and semantic decoding and intent prediction for dialogue understanding without supervision.
Cascon 2016 Keynote: Disrupting Developer Productivity One Bot at a TimeMargaret-Anne Storey
Conversational bots have become a popular addition to many mainstream platforms and software engineering has adopted them at an almost dizzying pace across every phase of the development life cycle. Bots reportedly help developers become more productive by automating tedious tasks, by bringing awareness of important project or community activities, and by reducing interruptions. Developers "talk to" and "listen to" these bots in the same conversational channels they use to collaborate with and monitor each other. However, the actual impact these bots have on developer productivity and project quality is still unclear. In this talk, I will give an overview of how bots play a prominent role in software development and discuss the benefits and challenges that can arise from relying on these "new virtual team members". I will also explore how bots may influence other knowledge work domains and propose a number of future directions for practitioners and researchers to consider.
Harm van Seijen, Research Scientist, Maluuba at MLconf SF 2016MLconf
1. The document discusses using deep reinforcement learning for dialogue systems. Deep reinforcement learning combines reinforcement learning with deep learning and can be applied to large, complex problems like dialogue systems.
2. A key challenge in training dialogue managers is the huge number of samples needed; this is addressed through using a user simulator trained on offline data. Deep reinforcement learning can learn directly from the belief state space used by dialogue systems.
3. Pre-training the deep reinforcement learning model on offline data makes the training more sample efficient for learning good dialogue policies.
End-to-End Joint Learning of Natural Language Understanding and Dialogue ManagerYun-Nung (Vivian) Chen
This document summarizes a research paper on end-to-end joint learning of natural language understanding and dialogue management. The paper proposes an end-to-end deep hierarchical model that leverages multi-task learning using three supervised tasks: user intent classification, slot tagging, and system action prediction. The model outperforms previous pipelined models by accessing contextual dialogue history and allowing the dialogue management signals to refine the natural language understanding through backpropagation. Evaluation on a dialogue state tracking dataset shows the joint model achieves better dialogue management performance compared to baselines and also improves natural language understanding.
This document outlines the agenda and instructions for an upcoming group presentation assignment. Students are instructed to form groups of 4-5 members to collaborate on a 7-10 minute problem/solution presentation about challenges International Engineering Cooperative Program (IECP) students may face transitioning to an American university and possible solutions. The presentation should be supported by evidence from each group member's observations of Penn State classes and interviews with Penn State students. Guidelines are provided on brainstorming challenges and solutions, preparing the introduction, explanations, proposed solutions, counter objections, and conclusion. The presentation will be evaluated on content, structure, delivery, language usage, and visual aids.
Partial Models: Towards Modeling and Reasoning with UncertaintyMichalis Famelis
This document discusses modeling and reasoning with uncertainty. The authors propose encoding uncertainty using partial models, which represent sets of conventional models. They describe checking properties of partial models by encoding them in propositional logic and using a SAT solver. The authors also discuss giving feedback to facilitate diagnosing properties. They aim to evaluate reasoning with partial models versus reasoning with sets of conventional models.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
Sentiment Analysis in Twitter with Lightweight Discourse AnalysisSubhabrata Mukherjee
Sentiment Analysis in Twitter with Lightweight Discourse Analysis, Subhabrata Mukherjee and Pushpak Bhattacharyya, In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), IIT Bombay, Mumbai, Dec 8 - Dec 15, 2012 (http://www.cse.iitb.ac.in/~pb/papers/coling12-discourse-sa.pdf)
This document discusses introducing personality to argumentative agents. It begins by providing background on argumentation dialogues and current frameworks used to model these, including Dung's abstract framework and ASPIC+. It then discusses modeling personality based on the five factor model (FFM), which describes personality along five traits. A personality model is proposed that assigns strengths to relevant FFM facets and defines how these map to preferences over speech acts and attitudes in argumentation. The goal is to allow agents to reason according to their personality configuration. Research questions are posed around implementing this model in agents and modeling opponents' personalities.
The central issue in test translations and adaptations is producing instruments that adequately measure target constructs across cultures. There are two main perspectives on equivalence - linguistic equivalence focuses on similarity of linguistic features, while psychological equivalence focuses on similarity of meaning and scores. A good translation combines high levels of construct, cultural, linguistic, and measurement equivalence. There is no single best approach, as the optimal method depends on the specific case. Multiple procedures can be used together to evaluate translation accuracy.
The document describes guidelines for translating and adapting tests published by the International Test Commission (ITC) in 2005. It acknowledges contributions from several international organizations in developing the guidelines over several years. The guidelines are structured in four categories and consist of 22 statements to provide a framework for translating, adapting, administering, and interpreting tests across languages and cultures. The guidelines have been referenced in several publications and aim to standardize best practices in test translation and adaptation.
The document discusses recent advances in natural language processing (NLP). It begins with an introduction to the presenter and their background and credentials working in NLP, machine learning, and deep learning. It then provides a brief definition of NLP, describing it as programming computers to process large amounts of natural language at the intersection of computer science, artificial intelligence, and computational linguistics. The document goes on to provide several examples of recent NLP applications, technologies, and research topics, such as sentiment analysis, spell checking, machine translation, story generation from images and text, and using word embeddings and document vectors for visualization. It closes by acknowledging that while recent successes exist, general human-level NLP remains a significant challenge that will require
PRONOUN DISAMBIGUATION: WITH APPLICATION TO THE WINOGRAD SCHEMA CHALLENGEkevig
A value-based approach to Natural Language Understanding, in particular, the disambiguation of
pronouns, is illustrated with a solution to a typical example from the Winograd Schema Challenge. The
worked example uses a language engine, Enguage, to support the articulation of the advocation and
fearing of violence. The example illustrates the indexical nature of pronouns, and how their values, their
referent objects, change because they are set by contextual data. It must be noted that Enguage is not a
suitable candidate for addressing the Winograd Schema Challenge as it is an interactive tool, whereas
the Challenge requires a preconfigured, unattended program.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introductions to the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques. The workshop is divided into four modules: word vectors, sentence/paragraph/document vectors, and character vectors. The document provides background on why text representation is important for NLP, and discusses older techniques like one-hot encoding, bag-of-words, n-grams, and TF-IDF. It also introduces newer distributed representation techniques like word2vec's skip-gram and CBOW models, GloVe, and the use of neural networks for language modeling.
Recent advances in technology have caused a proliferation of data and knowledge sources on a global scale. The ability to access and integrate these knowledge sources is crucial for critical decision making, and to facilitate this, knowledge-based intelligent applications (agents) need to resolve the differences between their knowledge models (ontologies).
We present preliminary work that allows two agents to jointly determine a single correspondence between two concepts in their respective ontologies, without the need for prior joint knowledge. The agents engage in a dialogue that permits the participants to exchange information about the concepts to support the assertion or rejection of a correspondence.
This paper was presented at the 15th Workshop on Computational Models of Natural Argument, 2015.
More details can be found at http://www.csc.liv.ac.uk/~trp/Knowledge-Based-Agents.html
This document outlines the stages of translating and adapting instruments across cultures and languages. It discusses:
1) Having documents translated independently by 2 translators and synthesizing the translations.
2) Evaluating the synthesized version with experts and the target population for comprehension.
3) Conducting back translations to check for consistency with the original.
4) Pilot testing the adapted instrument.
5) Validating the adapted instrument through statistical analyses like confirmatory factor analysis to ensure it measures the same constructs as reliably as the original. Cross-cultural validation is important for meaningful comparisons between groups.
This document discusses deploying a private Docker registry using Docker registry. It provides instructions on installing Docker registry using both Python and Go versions, configuring it as a service, setting up authentication and SSL, connecting Docker hosts to the registry, and running the Docker registry frontend tool.
End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Lan...Yun-Nung (Vivian) Chen
The document describes an end-to-end memory network model for multi-turn spoken language understanding. The model encodes context from previous utterances using an attention mechanism over the memory of past utterances. It then performs slot tagging on the current utterance incorporating the contextual knowledge. Experiments on a Cortana dataset show the model outperforms alternatives, achieving 67.1% accuracy by encoding both history and current utterances with the memory network.
The document discusses statistical learning from dialogues for intelligent assistants. It describes how spoken dialogue systems process user requests through steps like speech recognition, language understanding, dialogue management and response generation. It highlights current challenges like requiring hand-crafted domain knowledge and labeled data. The author's contributions include methods for automated knowledge acquisition from unlabeled dialogues and semantic decoding and intent prediction for dialogue understanding without supervision.
Cascon 2016 Keynote: Disrupting Developer Productivity One Bot at a TimeMargaret-Anne Storey
Conversational bots have become a popular addition to many mainstream platforms and software engineering has adopted them at an almost dizzying pace across every phase of the development life cycle. Bots reportedly help developers become more productive by automating tedious tasks, by bringing awareness of important project or community activities, and by reducing interruptions. Developers "talk to" and "listen to" these bots in the same conversational channels they use to collaborate with and monitor each other. However, the actual impact these bots have on developer productivity and project quality is still unclear. In this talk, I will give an overview of how bots play a prominent role in software development and discuss the benefits and challenges that can arise from relying on these "new virtual team members". I will also explore how bots may influence other knowledge work domains and propose a number of future directions for practitioners and researchers to consider.
Harm van Seijen, Research Scientist, Maluuba at MLconf SF 2016MLconf
1. The document discusses using deep reinforcement learning for dialogue systems. Deep reinforcement learning combines reinforcement learning with deep learning and can be applied to large, complex problems like dialogue systems.
2. A key challenge in training dialogue managers is the huge number of samples needed; this is addressed through using a user simulator trained on offline data. Deep reinforcement learning can learn directly from the belief state space used by dialogue systems.
3. Pre-training the deep reinforcement learning model on offline data makes the training more sample efficient for learning good dialogue policies.
End-to-End Joint Learning of Natural Language Understanding and Dialogue ManagerYun-Nung (Vivian) Chen
This document summarizes a research paper on end-to-end joint learning of natural language understanding and dialogue management. The paper proposes an end-to-end deep hierarchical model that leverages multi-task learning using three supervised tasks: user intent classification, slot tagging, and system action prediction. The model outperforms previous pipelined models by accessing contextual dialogue history and allowing the dialogue management signals to refine the natural language understanding through backpropagation. Evaluation on a dialogue state tracking dataset shows the joint model achieves better dialogue management performance compared to baselines and also improves natural language understanding.
This document summarizes a professional development workshop for language teachers. It includes an agenda with topics on interpersonal speaking standards, strategies to facilitate interpersonal speaking in the classroom, a role play activity to practice communication strategies, and a discussion of common assessments for interpersonal speaking. Video samples of student interpersonal speaking performances were also shared for teachers to evaluate using a provided rubric. The workshop aimed to help teachers develop instructional strategies and assessments for interpersonal speaking.
we can look at a clean copy of the text with analytic eyes
First task with text will be to look generally at five points:
audience
theme & intention
methods of development
tone
emotion
The document provides a step-by-step guide for analyzing the style and techniques used in a non-fiction text. It outlines key areas to examine such as the audience, theme, tone, emotion, diction, syntax, organization, perspective and more. Examples are given for each category to illustrate what to look for and how different writing choices can impact the overall style.
Tracking Learning: Using Corpus Linguistics to Assess Language DevelopmentCALPER
This document summarizes an academic presentation about using corpus linguistics to assess language development. It discusses traditional and alternative approaches to language assessment, and introduces corpus-informed assessment using large databases of authentic language samples. The presentation provides examples of assessing advanced English learners' academic language skills and German modal particles using corpus analysis techniques.
The document discusses the VL3 Virtual Language Learning Laboratory and its focus on developing dialogue system technology. It provides background on relevant fields like linguistic theory, computer science, and second language pedagogy. It also defines natural language processing as the field of manipulating natural language through computational platforms, covering areas like linguistic structure, logic, brain modeling, and cognitive science. The VL3 aims to utilize dialogue system technology, using the Sintagma platform to allow flexible natural language comprehension and dialogue management. It outlines the review process undertaken so far and plans for an upcoming first trial.
The document discusses the VL3 Virtual Language Learning Laboratory and its focus on developing dialogue system technology using natural language processing techniques. It provides background on NLP and its areas of research like natural language understanding, generation, speech recognition, and machine translation. The VL3 project aims to build dialogue systems using Sintagma, a technological platform that allows for flexible natural language comprehension and dialogue management. It is currently in the initial review stage to inform its first trial.
The document discusses the VL3 Virtual Language Learning Laboratory and its focus on developing dialogue system technology using natural language processing techniques. It provides background on NLP and its areas of research like natural language understanding, generation, speech recognition, and machine translation. The VL3 project aims to build dialogue systems using Sintagma, a technological platform that allows for flexible natural language comprehension and dialogue management. It is currently in the initial review stage to inform its first trial.
The document discusses the VL3 Virtual Language Learning Laboratory and its focus on developing dialogue system technology using natural language processing techniques. It provides background on NLP and its areas of research like natural language understanding, generation, speech recognition, and machine translation. The VL3 project aims to build dialogue systems using Sintagma, a technological platform that allows for flexible natural language comprehension and dialogue management. It is currently in the initial review stage to inform its first trial.
5810 oral lang anly transcr wkshp (fall 2014) pdf SVTaylor123
This document provides guidance for analyzing the oral language of a learner as part of a case study assignment. It includes an overview of the assignment requirements and rubric. Students will analyze a transcript of the learner's oral language to identify patterns in their use of language functions and language systems. Examples of language functions include instrumental, regulatory, interactional, and others. Language systems include phonology, syntax, semantics, and others. Charts are provided to record examples from the transcript. The presentation provides guidance on completing the analysis, including how to code the transcript for language functions and systems.
Natural language processing (NLP) refers to technologies that allow computers to understand, interpret and generate human language. NLP aims to allow non-programmers to obtain information from or give commands to computers using natural human languages. NLP involves analyzing text at morphological, syntactic, semantic and pragmatic levels to determine meaning. It is used for applications like search engines, voice assistants, summarization and translation. While progress has been made, NLP still faces challenges like ambiguity, idioms and connecting language to perception. The future of NLP is linked to advances in artificial intelligence to develop more human-like language abilities in machines.
Improving Communications With Soft Skill And Dialogue SimulationsEnspire Learning
The document discusses using simulations and soft skills to improve communication. It provides examples of simulations that leverage learning theories like self-direction, learning in context, practice with feedback. Simulations allow practicing skills in realistic scenarios and receiving immediate feedback to improve communication abilities. The document advocates for using branching simulations when skills need application in combinations or when modeling conversations.
Liberty university coms 101 quiz 4 complete solutions correct answers keySong Love
Liberty University COMS 101 quiz 4 complete solutions correct answers key
4 different versions
https://www.coursemerit.com/solution-details/26203/COMS-101-quiz-4-complete-solutions-correct-answers-key
Gadgets pwn us? A pattern language for CALLLawrie Hunter
The document discusses creating a pattern language for computer-assisted language learning (CALL). It explores the concept of a pattern language as defined by Christopher Alexander and proposes a framework for creating a CALL pattern language in the era of web 2.0. The paper seeks to rework concepts from other fields, like "formal learning design expression" and "task arc," and have participants brainstorm elements to include through graphical challenges. The overall goal is to establish foundational patterns for CALL work.
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
This document provides guidance on analyzing persuasive language use in a text. It discusses identifying the contention, planning a response by outlining the background, contention, reasons, writer, tone, and audience. It also analyzes persuasive techniques including logic, emotion, tone, audience, annotated text examples, and outlines the structure of an analysis including an introduction, body paragraphs, visual analysis, and conclusion.
Experiments on Pattern-based Ontology Designevabl444
The document describes an experiment on using ontology design patterns (ODPs) to construct ontologies from textual requirements. It found that:
- Most participants perceived ODPs as useful for building better quality ontologies, though it did not necessarily speed up development.
- Ontologies constructed using ODPs showed improved coverage of tasks and modeling quality compared to those built without patterns.
- However, more support is still needed for easily finding, selecting, and relating relevant patterns during ontology engineering.
This document discusses oral assessment and role play activities. It covers types of interaction that can be assessed orally, such as between examiner-candidate or examiner-candidate-candidate. It also discusses the difficulty of oral assessment, including how a candidate's personality can influence their performance. It outlines different types of rating scales that can be used, such as analytic or holistic scales, and what specific skills should be assessed. The document provides information on the role of the rater, their influence on the assessment process, and examples of test formats and activities that can be used for oral assessment.
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This document provides an overview of the topics and activities to be covered in Class Session #3 of the workshop LCRT 5810: Workshop in Language Development & Acquisition. The session will focus on using linguistic tools to observe and analyze language in the classroom. Activities include reconnecting with classmates, examining how one's own language varies in different contexts, and collecting and transcribing oral language samples from a case study learner. The document outlines the linguistic areas to be covered, such as phonology, morphology, syntax and pragmatics, and how they can be applied to analyze language samples. Requirements for upcoming assignments on analyzing oral language and collecting additional language samples are also provided.
1. Affective Analysis and Modeling of Spoken
Dialogue Transcripts
Thesis presentation
Elisavet Palogiannidi
Committee
Alexandros Potamianos (supervisor)
Polychronis Koutsakis (co-supervisor)
Aikaterini Mania
School of Electronic and Computer Engineering
Technical University of Crete
Chania, Crete
11 July 2016
2. Introduction Affective models Experiments and Results Q&A Conclusions
What if there was no emotion?
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3. Introduction Affective models Experiments and Results Q&A Conclusions
What if there was no emotion?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 3/49
4. Introduction Affective models Experiments and Results Q&A Conclusions
What if there was no emotion?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 4/49
5. Introduction Affective models Experiments and Results Q&A Conclusions
What if there was no emotion?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 5/49
6. Introduction Affective models Experiments and Results Q&A Conclusions
What if there was no emotion?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 6/49
7. Introduction Affective models Experiments and Results Q&A Conclusions
What if there were no computers?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 7/49
8. Introduction Affective models Experiments and Results Q&A Conclusions
What if there were no computers?
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 8/49
9. Introduction Affective models Experiments and Results Q&A Conclusions
What is the relationship between computers and emotions?
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10. Introduction Affective models Experiments and Results Q&A Conclusions
What is all about?
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11. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
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12. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
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13. Introduction Affective models Experiments and Results Q&A Conclusions
Motivation
Emotion detection from text
“Emotion is perceived in text and it can be elicited by its
content and form”
Goal:Assign continuous high quatlity affective scores on
various granularity lexical tokens, using semantic and affective
features, for multiple languages
Motivation: “Semantic similarity implies affective similarity”
Affective text labelling at the core of many applications
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14. Introduction Affective models Experiments and Results Q&A Conclusions
Motivation
Applications
Affective text applications
Sentiment analysis of Social Media, news, product reviews
Emotion detection on spoken dialogue
Multimodal applications
Semantic affective model (SAM) [Malandrakis et al. 2013]
Has been applied to tweets, sms and news headlines
Is applicable to words or n-grams and numerous dimensions
Valence, Arousal, Dominance, Concreteness, Imagability,
Familiarity, Gender Ladenness
We focus on the prediction of Valence, Arousal, Dominance
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15. Introduction Affective models Experiments and Results Q&A Conclusions
Emotion
Continuous Affective space
Introduction
• Goals: 1) Create an emotional resource for the Greek language
2) Use it to automatically estimate affective ratings of words
• Manually created resources have low language coverage (about 1K words)
• Computational models are used to expand manually created affective lexica
Affective (Emotional) Dimensions
Valence Arousal Dominance
Negative to positive Calming to exciting Controlled to controller
Valence-Arousal Distributions Across Languages
• Valence-Arousal distributions for different languages affective lexica
Greek affective lexicon ratings V-shape across languages
0.25
0.5
0.75
1
Arousal
flirtation
treasure
friend
happy
laugh
victory
poster
slave
sadness
pillow
syphilis
anger
commit suicide
failure
−1 −0.5 0 0.5 1
0.25
0.5
0.75
1
0.5
0.75
1
usal
L
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16. Introduction Affective models Experiments and Results Q&A Conclusions
Contributions
Annotated Resources: Greek ANEW
We created the first Greek Affective Lexicon
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17. Introduction Affective models Experiments and Results Q&A Conclusions
Contributions
Models for multiple languages
We extended SAM to multiple languages
We improved the mapping from semantic to affective space
We tried various contextual features and weighting schemes
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18. Introduction Affective models Experiments and Results Q&A Conclusions
Contributions
Compositional Affective models
The meaning of complex lexical structures p is composed by
the meaning of the constituent words α, β
Compositional approaches in vector-based semantics:
Composition of semantic representation of the phrase’s
constituent words
Combine by addition and multiplication [Mitchell and Lapata.,
2008; Mitchell and Lapata, 2010]
.[Baroni and Zamparelli.,2010] compositional approach based
on POS tags
We assume that composition occurs in the affective space,
Combine affective ratings and not semantic representation of
constituent words
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19. Introduction Affective models Experiments and Results Q&A Conclusions
Contributions
Sentiment Analysis in Twitter
We achieved state of the art performance winning a word wide
competition
...................
Semantic Affective system (Baseline)
.
• Tools: POS-tagging, multiword expression, hashtag expan
– Semantic similarity implies affective similarity: SAM “Distr
Semantic Models for Affective Text Analysis, Malandrakis et al. 2013”
• Goal:estimate the affect of word pairs mor
curately than the non-compositional models
• Compositionality: the meaning of the w
is constructed form the meaning of the part
• Novelty: Applied on affective space
• Adopt modifier-head structure: p = m.h
• E.g., : p=“green parrot” and p=“dead par
– m : green/dead & h : parrot
– m modifies the affect of h
Continuous Affective spaces
• Valence - Arousal - Dominance
Semantic Affective Model (SAM
Semantic similarity implies affective similarity
tributional Semantic Models for Affective Text Analysis, Malandrakis et a
ˆυ(tj) = a0 +
N∑
i=1
aiυ(wi)S(tj, wi)
• ˆυ(tj): the affective rating of the unknown t
tj, w1..N: the seeds, υ(wi) and ai: the affe
rating and the weight of wi, a0: the bias,
semantic similarity between tokens
Each modifie
unique bahavi
Applied on
words &
word pairs!
number of seedsaffective rating
of the unknown token
bias
weights
assigned to seeds
Semantic similarity
between tokens
affective ratings
of seeds
• Two step feature selection, Naive Bayes (NB) tree classifi
....
Topic Modeling - based System (TM)
.
• Adapt semantic space on each tweet
• LDA → detect topics (16)→ split corpus →
..............................
In Subtask
is used as f
.
Subtask B
at SemEval 2016 Task 4
Sentiment Analysis in Twitter
using Semantic-Affective Model Ad
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20. Introduction Affective models Experiments and Results Q&A Conclusions
Contributions
Publications
1 Elisavet Palogiannidi, Elias Iosif, Polychronis Koutsakis and Alexandros Potamianos, “Valence, Arousal
and Dominance Estimation for English, German, Greek, Portuguese and Spanish Lexica using Semantic
Models”, in Proceedings of Interspeech, September 2015.
2 Elisavet Palogiannidi, Elias Iosif, Polychronis Koutsakis and Alexandros Potamianos “Affective lexicon
creation for the Greek language”, in Proceedings of the 10th edition of the Language Resources and
Evaluation Conference (LREC) 2016.
3 Elisavet Palogiannidi, Polychronis Koutsakis and Alexandros Potamianos, “A semantic-affective
compositional approach for the affective labelling of adjective-noun and noun-noun pairs”, in Proceedings
of WASSA 2016.
4 Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos
Malandrakis, Harris Papageorgiou , Shrikanth Narayanan and Alexandros Potamianos, “Tweester:
Sentiment analysis in twitter using semantic-affective model adaptation”, in Proceedings of the 10th
International Workshop on Semantic Evaluation (SemEval) 2016.
5 Jose Lopes, Arodami Chorianopoulou, Elisavet Palogiannidi, Helena Moniz, Alberto Abad, Katerina Louka,
Elias Iosif and Aleandros Potamianos “The SpeDial Datasets: Datasets for Spoken Dialogue Systems
Analytics”, in Proceedings of the 10th edition of the Language Resources and Evaluation Conference
(LREC) 2016.
6 Spiros Georgiladakis, Georgia Athanasopoulou, Raveesh Meena, Jose Lopes, Arodami Chorianopoulou,
Elisavet Palogiannidi, Elias Iosif, Gabriel Skantze and Alexandros Potamianos “Root Cause Analysis of
Miscommunication Hotspots in Spoken Dialogue Systems”, in Proceedings of Interspeech 2016 (to appear).
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21. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
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22. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic Affective Model
Semantic models
Building block for machine learning in NLP
Corpus based approach: Distributional Semantic Models
(DSM)
Semantic information extracted from word frequencies
(co-occurence counts, context vectors)
Context based semantic similarities
“Similarity of context implies similarity of meaning” [Harris ’54]
Contextual windows that contain words or character n-grams
Binary or PPMI weighting scheme
Semantic similarity between two words: cosine of their
contextual feature vectors
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23. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic Affective Model
From Semantic to Affective Space
Affective model: Extension of [Turney and Littman, 2002],
proposed by [Malandrakis et al. 2013b]
The semantic model is built,
based on the corpus
Training phase for the semantic
to the affective mapping
Affective lexica are used for the
training, e.g., ANEW [Bradley
and Lang 1999]
[Malandrakis et al. 2014]
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24. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic Affective Model
Affective model [Malandrakis et al. ’13]
Requires a small, manually annotated affective lexicon
Assumption: The affective score of a word can be expressed
as a linear combination of the affective ratings of seed words
weighted by semantic similarity and trainable weights αi
ˆυ(wj ) = α0 +
N
i=1
αi υ(wi )S(wj , wi ) (1)
ˆυ(wj ): estimated affective rating of the unknown word wj
w1..N : seed words
υ(wi ): affective rating of wi (valence, arousal or dominance)
αi : weight assigned to wi (α0: bias)
S(·): semantic similarity between wj and wi
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25. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic Affective Model
Semantic - affective mapping
Not all seeds are equally salient
Weights estimation (α0 · · · αN) through supervised learning
1 S(w1, w1)υ(w1) · · · S(w1, wN )υ(wN )
1
...
...
...
1 S(wK , w1)υ(w1) · · · S(wK , wN )υ(wN )
·
α0
...
αN
=
1
υ(w1)
.
..
υ(wK )
(2)
A system of K linear equations with N + 1 (N < K) unknown
variables is solved using
Least Squares Estimation (LSE)
Ridge Regression (RR)
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26. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Compositionality
The meaning of the whole is constructed by the meaning of
the parts
New idea: Applied on affective instead of semantic space
Adopt a modifier-head (m − h) structure for word pairs
Assumption: each modifier has unique behavior that can be
learnt in a distributional approach
e.g., green parrot Vs. dead parrot
modifiers m modify the affective content of h
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27. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Compositional model (1/2)
The meaning of more complex lexical structures is composed
by the meaning of the constituent words
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28. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Compositional model (2/2)
The affective content of the word pair is the modified affective
content of the head
ˆυc(p) = β + W ˆυ(h)
β, W are modifier’s bahavior
ˆυ(h) is the affective content of the head
Applied on 1D (W , β are scalars ) and 3D (W ∈ IR3×3
,
β ∈ IR3
) affective spaces
Compositionality measure: Mean Squared Error over training
pairs
Measured between compositional and bigram SAM
High MSE → low compositional model appropriateness
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29. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Fusion of Compositional and non compositional models
Each word pair has different compositionality degree
Non-compositional models
1 Unigram SAM (U-SAM): average of words’ affective ratings
2 Bigram (B-SAM): apply SAM directly on word pair
Fusion schemes
Average (Avg) and Weighted average
MSE-based :
Estimate λ (pj ) = 0.5
1+e
−MSE(pj ) for each training pair
Average all λ (pj ) to learn the parameter λ(p) of the test pair
Weight compositional (C) and non-compositional (nC) models
based on λ(p), i.e., υφ(p) = λ(p)nC + (1 − λ(p))C
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30. Introduction Affective models Experiments and Results Q&A Conclusions
Sentence level Affective Models
Fusion of words’ affective ratings
Sentence level affective rating approaches
1 Aggregation of the constituent words’ affective ratings
Average
Weighted Average
Maximum absolute affective rating
2 Classification based on affective features
Statistics of words’ affective ratings
POS-tag grouping
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31. Introduction Affective models Experiments and Results Q&A Conclusions
Sentence level Affective Models
Tweester: Semantic affective model system
Two - step feature selection
Naive Bayes tree classifier
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32. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
Elisavet Palogiannidi TUC Affective Analysis and Modeling of Spoken Dialogue Transcripts 32/49
33. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Experimental Procedure
Goal
Estimate Valence, Arousal and Dominance scores of words in
multiple languages (English, German, Greek, Portuguese, Spanish)
Semantic similarity computation
Words (W) and character n-grams contextual features
Binary (B) and PPMI weighting schemes
Fusion: combine different types of contextual feature vectors
Evaluation datasets
The affective lexica of each language
10-fold cross validation: 90% train and 10% test
Evaluation Metrics: Pearson Correlation, Binary classification
accuracy (positive vs. negative values)
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34. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Valence performance as a function of the seeds
Valence correlation and classification accuracy
Performance as a function of the seeds
Valence evaluation of five languages
0 100 200 300 400 500 600
0.65
0.7
0.75
0.8
0.85
0.9
Number of seeds
Correlation
0 100 200 300 400 500 600
0.7
0.75
0.8
0.85
0.9
Number of seeds
ClassificationAccuracy
English Greek German Portuguese Spanish
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35. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Comparison of affective dimensions
Valence (a), Arousal (b), Dominance (c) clas. accuracy
0 100 200 300 400 500 600
0.7
0.75
0.8
0.85
0.9
Number of seeds
ClassificationAccuracy
English Greek German Portuguese Spanish
(a)
0 100 200 300 400 500 600
0.65
0.7
0.75
0.8
0.85
0.9
Number of seeds
ClassificationAccuracy
(b)
0 100 200 300 400 500 600
0.65
0.7
0.75
0.8
0.85
0.9
Number of seeds
ClassificationAccuracy
(c)
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36. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Comparison of RR and LSE
10 200 400 600 900
0.2
0.4
0.6
0.7
0.8
Number of seeds
Correlation
Arousal
10 200 400 600 900
0.65
0.7
0.75
0.8
Number of seeds
ClassificationAccuracy
Arousal
Spanish − RR Spanish − LSE Greek − LSE Greek − RR
Using RR with the appropriate λ
Performance stays robust for a large number of seeds
RR improves performance of Greek and Spanish on Arousal
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37. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Valence classification accuracy for 600 seeds
PPMI works better than binary
Sem. Similarity English Greek Spanish Portuguese German
W-B 86.9 84.3 85.9 89.3 77.1
W-PPMI 90.9 87.6 85.3 90.8 85.2
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38. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Valence classification accuracy for 600 seeds
PPMI works better than binary
Character n-grams work equally well with words
Sem. Similarity English Greek Spanish Portuguese German
W-B 86.9 84.3 85.9 89.3 77.1
W-PPMI 90.9 87.6 85.3 90.8 85.2
4gram-PPMI 89.8 87.5 87.7 87.4 82.6
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39. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Valence classification accuracy for 600 seeds
PPMI works better than binary
Character n-grams work equally well with words
Concatenating different contextual vectors does not improve
the performance
Sem. Similarity English Greek Spanish Portuguese German
W-B 86.9 84.3 85.9 89.3 77.1
W-PPMI 90.9 87.6 85.3 90.8 85.2
4gram-PPMI 89.8 87.5 87.7 87.4 82.6
W/4gram-PPMI 90.5 87.2 87.9 89.3 83.0
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40. Introduction Affective models Experiments and Results Q&A Conclusions
Semantic - Affective model
Valence classification accuracy for 600 seeds
PPMI works better than binary
Character n-grams work equally well with words
Concatenating different contextual vectors does not improve
the performance
Sem. Similarity English Greek Spanish Portuguese German
W-B 86.9 84.3 85.9 89.3 77.1
W-PPMI 90.9 87.6 85.3 90.8 85.2
4gram-PPMI 89.8 87.5 87.7 87.4 82.6
W/4gram-PPMI 90.5 87.2 87.9 89.3 83.0
Weighting scheme is the most important parameter
English achieves highest performance
German achieves highest performance increase
Char. 4-gram-PPMI works almost always better than W-B
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41. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Experimental procedure
Goal
Estimate Valence scores of word pairs employing compositional
phenomena
Movie domain word pairs
1009 Adjective Noun (AN) and 357 Noun Noun (NN)
Training corpus: 116M web snippets
Extra training on fusion schemes for weights estimation
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42. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Classification Accuracy for AN and NN word pairs
U−SAMB−SAM 1D 3D Avg W.Avg MSE−Based
74
76
80
84
86
88
Affective models
ClassificationAccuracy(%)
NN AN Chance − NN Chance − AN
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43. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Classification Accuracy for AN and NN word pairs
U−SAMB−SAM 1D 3D Avg W.Avg MSE−Based
74
76
80
84
86
88
Affective models
ClassificationAccuracy(%)
NN AN Chance − NN Chance − AN
Compositional models work better than B-SAMs but worse
than U-SAMs
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44. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Classification Accuracy for AN and NN word pairs
U−SAMB−SAM 1D 3D Avg W.Avg MSE−Based
74
76
80
84
86
88
Affective models
ClassificationAccuracy(%)
NN AN Chance − NN Chance − AN
Compositional models work better than B-SAMs but worse
than U-SAMs
Highest performance achieved for fusion of compositional and
non-compositional models
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45. Introduction Affective models Experiments and Results Q&A Conclusions
Compositional Affective Model
Classification Accuracy for AN and NN word pairs
U−SAMB−SAM 1D 3D Avg W.Avg MSE−Based
74
76
80
84
86
88
Affective models
ClassificationAccuracy(%)
NN AN Chance − NN Chance − AN
Compositional models work better than B-SAMs but worse
than U-SAMs
Highest performance achieved for fusion of compositional and
non-compositional models
Small differences between 1D and 3D models
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46. Introduction Affective models Experiments and Results Q&A Conclusions
Sentence level affective models
Evaluation on News Headlines
Valence estimation of 1000 news headlines aggregating
affective ratings
Affective Model Classification Accuracy (%)
Chance 52.6
Content Words All words
Average 72.4 70.9
Weighted Average 71.6 73.1
Maximum absolute valence 67 66.4
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47. Introduction Affective models Experiments and Results Q&A Conclusions
Sentence level affective models
Evaluation on Movie Subtitles
Valence estimation of movie subtitles from 12 movies
Annotate subtitles on Valence through Crowdsourcing
Leave-one-movie-out scheme
Average performance for all the movies as a function of the
seeds
10 50 100 200 300 400 500 600 700 800
0.5
0.55
0.6
0.65
0.7
Movies subtitles Dataset
Seeds
ClassificationAccuracy
10 50 100 200 300 400 500 600 700 800
0
0.1
0.2
0.3
0.4
Movies subtitles Dataset
Seeds
Correlation
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48. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
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49. Introduction Affective models Experiments and Results Q&A Conclusions
How the sentence level models perform on real data?
Twitter (written text)
Polarity detection task (positive vs. negative tweets)
Classifier with affective features trained on tweets
Evaluation metric: average recall of positive, negative class ρ
System ρ
Baseline 0.821
LYS (Spain 0.791
Amazon 0.784
Spoken Dialogue (transcriptions of speech)
The same utterance can be expressed with different emotion
Affective text models usually don’t work for short utterances
Moderate performance is reached for larger utterances of real
dialogues
Performance improves when fusing with speech system
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50. Introduction Affective models Experiments and Results Q&A Conclusions
How the sentence level models perform on real data?
Twitter (written text)
Polarity detection task (positive vs. negative tweets)
Classifier with affective features trained on tweets
Evaluation metric: average recall of positive, negative class ρ
System ρ
Baseline 0.821
LYS (Spain 0.791
Amazon 0.784
Spoken Dialogue (transcriptions of speech)
The same utterance can be expressed with different emotion
Affective text models usually don’t work for short utterances
Moderate performance is reached for larger utterances of real
dialogues
Performance improves when fusing with speech system
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51. Introduction Affective models Experiments and Results Q&A Conclusions
How the sentence level models perform on real data?
Twitter (written text)
Polarity detection task (positive vs. negative tweets)
Classifier with affective features trained on tweets
Evaluation metric: average recall of positive, negative class ρ
System ρ
Baseline 0.821
LYS (Spain 0.791
Amazon 0.784
Spoken Dialogue (transcriptions of speech)
The same utterance can be expressed with different emotion
Affective text models usually don’t work for short utterances
Moderate performance is reached for larger utterances of real
dialogues
Performance improves when fusing with speech system
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52. Introduction Affective models Experiments and Results Q&A Conclusions
Can SAM be applied on a language with no affective
lexicon lexicon?
1 Create a new affective lexicon
2 Use cross-language modeling
Translate the words of an already existing affective lexicon
Use the other language’s affective ratings
0 100 200 300 400 500 600
0.75
0.8
0.85
0.9
0.95
Seeds
ClassificationAccuracy
S: Greek, T: Portuguese
S: English, T: Portuguese
S: Spanish, T: Portuguese
Portuguese
S: Greek, English, Spanish
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53. Introduction Affective models Experiments and Results Q&A Conclusions
Outline
1 Introduction
Motivation
Emotion
Contributions
2 Affective models
Semantic Affective Model
Compositional Affective Model
Sentence level Affective Models
3 Experiments and Results
Semantic - Affective model
Compositional Affective Model
Sentence level affective models
4 Q&A
5 Conclusions
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54. Introduction Affective models Experiments and Results Q&A Conclusions
Conclusions
Affective models for emotion detection of various granularity
lexical units
We showed that SAM for words
Is language and affective dimension independent
Performance depends on the weights estimation method
We showed that Cross language SAM performs equally well
Compositional models can be applied on affective space
The nature of the written data determines the performance of
the sentence level model
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55. Introduction Affective models Experiments and Results Q&A Conclusions
Future work
Identify parameters that define compositionality
Employ compositional semantics on compositional model
Ambiguous interaction between the words of the word pair
Incorporate morphological information in different languages’
SAMs
Compositional models for sentences
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56. Introduction Affective models Experiments and Results Q&A Conclusions
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57. Introduction Affective models Experiments and Results Q&A Conclusions
References
Malandrakis et al. 2013 N. Malandrakis, A. Potamianos, E. Iosif and S. Narayanan. 2013. “Distributional
Semantic Models for Affective Text Analysis”. IEEE Transactions on Audio, Speech and Language Processing. 2013
Malandrakis et al. 2014 N.Malandrakis, A. Potamianos, K. J. Hsu , K. N. Babeva, M. C. Feng , G. C. Davison , S.
Narayanan, 2014 “Affective Language Model Adaptation Via Corpus Selection”, ICASSP 2014
Turney and Littman 2002 P. Turney and M. L. Littman, “Unsupervised Learning of Semantic Orientation from a
Hundred-Billion-Word Corpus. Technical report ERC-1094 (NRC 44929),” National Research. Council of Canada,
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Mitchell, J., and Lapata 2008 J. Mitchell and M. Lapata. Vector-based models of semantic composition. In Proc.
of (ACL), pages 236244. 2008
Mitchell, J., and Lapata 2010 Mitchell, J., and Lapata, M. Composition in distributional models of semantics.
Cognitive science 34, 8 (2010)
Baroni and Zamparelli 2010 Baroni, M., and Zamparelli., R. Nouns are vectors, adjectives are matrices:
Representing adjective-noun constructions in semantic space. In in Proc. of EMNLP (2010).
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