The Experts below are selected from a list of 37692 Experts worldwide ranked by ideXlab platform
Erik Cambria - One of the best experts on this subject based on the ideXlab platform.
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affectivespace 2 enabling affective intuition for concept level sentiment analysis
National Conference on Artificial Intelligence, 2015Co-Authors: Erik Cambria, Federica Bisio, Soujanya PoriaAbstract:Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language concepts. By reducing the dimensionality of affective Common-Sense Knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multiword expressions, thus achieving efficient concept-level sentiment analysis.
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a rule based approach to aspect extraction from product reviews
International Conference on Computational Linguistics, 2014Co-Authors: Soujanya Poria, Erik Cambria, Chen Gui, Alexander GelbukhAbstract:Sentiment analysis is a rapidly growing research field that has attracted both academia and industry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based opinion mining, in particular, is one of the fundamental challenges within this research field. In this work, we aim to solve the problem of aspect extraction from product reviews by proposing a novel rule-based approach that exploits Common-Sense Knowledge and sentence dependency trees to detect both explicit and implicit aspects. Two popular review datasets were used for evaluating the system against state-of-the-art aspect extraction techniques, obtaining higher detection accuracy for both datasets.
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senticnet 3 a Common and Common Sense Knowledge base for cognition driven sentiment analysis
National Conference on Artificial Intelligence, 2014Co-Authors: Erik Cambria, Daniel Olsher, Dheeraj RajagopalAbstract:SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of 'energy flows' to connect various parts of extended Common and Common-Sense Knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.
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Common and Common Sense Knowledge integration for concept level sentiment analysis
The Florida AI Research Society, 2014Co-Authors: Erik Cambria, Newton HowardAbstract:In the era of Big Data, Knowledge integration is key for tasks such as social media aggregation, opinion mining, and cyber-issue detection. The integration of different kinds of Knowledge coming from multiple sources, however, is often a problematic issue as it either requires a lot of manual effort in defining aggregation rules or suffers from noise generated by automatic integration techniques. In this work, we propose a method based on conceptual primitives for efficiently integrating pieces of Knowledge coming from different Common and Common-Sense resources, which we test in the field of concept-level sentiment analysis.
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semantic multidimensional scaling for open domain sentiment analysis
IEEE Intelligent Systems, 2014Co-Authors: Erik Cambria, Yangqiu Song, Haixun Wang, Newton HowardAbstract:The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the Common and Common-Sense Knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of Knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of Common Knowledge is blended with a natural-language-based semantic network of Common-Sense Knowledge. Multidimensional scaling is applied on the resulting Knowledge base for open-domain opinion mining and sentiment analysis.
Newton Howard - One of the best experts on this subject based on the ideXlab platform.
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Common and Common Sense Knowledge integration for concept level sentiment analysis
The Florida AI Research Society, 2014Co-Authors: Erik Cambria, Newton HowardAbstract:In the era of Big Data, Knowledge integration is key for tasks such as social media aggregation, opinion mining, and cyber-issue detection. The integration of different kinds of Knowledge coming from multiple sources, however, is often a problematic issue as it either requires a lot of manual effort in defining aggregation rules or suffers from noise generated by automatic integration techniques. In this work, we propose a method based on conceptual primitives for efficiently integrating pieces of Knowledge coming from different Common and Common-Sense resources, which we test in the field of concept-level sentiment analysis.
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semantic multidimensional scaling for open domain sentiment analysis
IEEE Intelligent Systems, 2014Co-Authors: Erik Cambria, Yangqiu Song, Haixun Wang, Newton HowardAbstract:The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the Common and Common-Sense Knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of Knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of Common Knowledge is blended with a natural-language-based semantic network of Common-Sense Knowledge. Multidimensional scaling is applied on the resulting Knowledge base for open-domain opinion mining and sentiment analysis.
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Common Sense Knowledge based personality recognition from text
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013Co-Authors: Basant Agarwal, Erik Cambria, Soujanya Poria, Alexander Gelbukh, Newton HowardAbstract:Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using Common Sense Knowledge with associated sentiment polarity and affective labels. To extract the Common Sense Knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined Common Sense Knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of Common Sense Knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.
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erratum Common Sense Knowledge based personality recognition from text
Mexican International Conference on Artificial Intelligence, 2013Co-Authors: Basant Agarwal, Erik Cambria, Soujanya Poria, Alexander Gelbukh, Newton HowardAbstract:In the original version, the name of the second author was spelled incorrectly by mistake. It should be Alexander Gelbukh.
Soujanya Poria - One of the best experts on this subject based on the ideXlab platform.
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affectivespace 2 enabling affective intuition for concept level sentiment analysis
National Conference on Artificial Intelligence, 2015Co-Authors: Erik Cambria, Federica Bisio, Soujanya PoriaAbstract:Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language concepts. By reducing the dimensionality of affective Common-Sense Knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multiword expressions, thus achieving efficient concept-level sentiment analysis.
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a rule based approach to aspect extraction from product reviews
International Conference on Computational Linguistics, 2014Co-Authors: Soujanya Poria, Erik Cambria, Chen Gui, Alexander GelbukhAbstract:Sentiment analysis is a rapidly growing research field that has attracted both academia and industry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based opinion mining, in particular, is one of the fundamental challenges within this research field. In this work, we aim to solve the problem of aspect extraction from product reviews by proposing a novel rule-based approach that exploits Common-Sense Knowledge and sentence dependency trees to detect both explicit and implicit aspects. Two popular review datasets were used for evaluating the system against state-of-the-art aspect extraction techniques, obtaining higher detection accuracy for both datasets.
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Common Sense Knowledge based personality recognition from text
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013Co-Authors: Basant Agarwal, Erik Cambria, Soujanya Poria, Alexander Gelbukh, Newton HowardAbstract:Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using Common Sense Knowledge with associated sentiment polarity and affective labels. To extract the Common Sense Knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined Common Sense Knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of Common Sense Knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.
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erratum Common Sense Knowledge based personality recognition from text
Mexican International Conference on Artificial Intelligence, 2013Co-Authors: Basant Agarwal, Erik Cambria, Soujanya Poria, Alexander Gelbukh, Newton HowardAbstract:In the original version, the name of the second author was spelled incorrectly by mistake. It should be Alexander Gelbukh.
Catherine Havasi - One of the best experts on this subject based on the ideXlab platform.
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Common Sense reasoning for detection prevention and mitigation of cyberbullying
Ksii Transactions on Internet and Information Systems, 2012Co-Authors: Karthik Dinakar, Catherine Havasi, Henry Lieberman, Birago Jones, Rosalind W PicardAbstract:Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a Common Sense Knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a Common Sense Knowledge base that encodes particular Knowledge about bullying situations. We then perform joint reasoning with Common Sense Knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace Common Sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.
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the glass infrastructure using Common Sense to create a dynamic place based social information system
Ai Magazine, 2012Co-Authors: Catherine Havasi, Richard Borovoy, Boris Kizelshteyn, Polychronis Ypodimatopoulos, Jon Ferguson, Henry Holtzman, Andrew Lippman, Dan Schultz, Matthew Blackshaw, Greg T ElliottAbstract:Most organizations have a wealth of Knowledge about themselves available online, but little for a visitor to interact with on-site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI), that enables small groups of users to physically interact through a touch screen display with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization and its relationships developed using dimensionality reduction and Common Sense Knowledge which automatically classifies and organizes the information. The GI is currently in daily use at the lab. We discuss the AI model's development, the integration of AI into an HCI interface, and the use of the GI during the lab's peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.
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the glass infrastructure using Common Sense to create a dynamic place based social information system
Innovative Applications of Artificial Intelligence, 2011Co-Authors: Catherine Havasi, Richard Borovoy, Boris Kizelshteyn, Polychronis Ypodimatopoulos, Jon Ferguson, Henry Holtzman, Andrew Lippman, Dan Schultz, Matthew Blackshaw, Greg T ElliottAbstract:Most organizations have a wealth of Knowledge about themselves available online, but little for a visitor to interact with on-site. At the MIT Media Lab, we have designed and deployed a novel intelligent signage system, the Glass Infrastructure (GI) that enables small groups of users to physically interact with this data and to discover the latent connections between people, projects, and ideas. The displays are built on an adaptive, unsupervised model of the organization developed using dimensionality reduction and Common Sense Knowledge which automatically classifies and organizes the information. The GI is currently in daily use at the lab. We discuss the AI model’s development, the integration of AI into an HCI interface, and the use of the GI during the lab’s peak visitor periods. We show that the GI is used repeatedly by lab visitors and provides a window into the workings of the organization.
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an interface for targeted collection of Common Sense Knowledge using a mixture model
Intelligent User Interfaces, 2009Co-Authors: Robyn Speer, Catherine Havasi, Jayant Krishnamurthy, Dustin Smith, Henry Lieberman, Kenneth C ArnoldAbstract:We present a game-based interface for acquiring Common Sense Knowledge. In addition to being interactive and entertaining, our interface guides the Knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense Knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter Knowledge more quickly than a more traditional Knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of Knowledge acquisition.
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analogyspace reducing the dimensionality of Common Sense Knowledge
National Conference on Artificial Intelligence, 2008Co-Authors: Robert Speer, Catherine Havasi, Henry LiebermanAbstract:We are interested in the problem of reasoning over very large Common Sense Knowledge bases. When such a Knowledge base contains noisy and subjective data, it is important to have a method for making rough conclusions based on similarities and tendencies, rather than absolute truth. We present Analogy Space, which accomplishes this by forming the analogical closure of a semantic network through dimensionality reduction. It self-organizes concepts around dimensions that can be seen as making distinctions such as "good vs. bad" or "easy vs. hard", and generalizes its Knowledge by judging where concepts lie along these dimensions. An evaluation demonstrates that users often agree with the predicted Knowledge, and that its accuracy is an improvement over previous techniques.
Aparecido Fabiano Pinatti De Carvalho - One of the best experts on this subject based on the ideXlab platform.
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planning learning activities pedagogically suitable by using Common Sense Knowledge
Mexican International Conference on Computer Science, 2009Co-Authors: Aparecido Fabiano Pinatti De Carvalho, Junia Coutinho Anacleto, Silvia Helena ZemmascarenhasAbstract:This paper illustrates the use of Common Sense Knowledge, acquired from volunteers through the web, to support teachers to plan learning activities, which fit to pedagogical issues presented in renowned Learning Theories, so that effective learning can take place. It is approached in this paper how Common Sense Knowledge is related to four Learning Theories, proposed by authors who are aware in the pedagogical area – Freire, Freinet, Ausubel and Gagne – and how computational technologies can make viable the use of this kind of Knowledge by professors.
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learning activities on health care supported by Common Sense Knowledge
ACM Symposium on Applied Computing, 2008Co-Authors: Aparecido Fabiano Pinatti De Carvalho, Junia Coutinho Anacleto, Silvia Helena ZemmascarenhasAbstract:This paper discusses how Common Sense Knowledge can be used by teachers for planning Learning Activities on health care. Using Common Sense statements which were automatically collected, we are developing software that can be used to support the teaching and learning process, in a more contextualized form. When teachers consider the Knowledge that learners already have, taking into account their Common Sense Knowledge, they can devote their attention to correcting misconceptions, covering ignored topics and avoiding the obvious. Also teachers can consider the Common Sense Knowledge from a group of interest, preparing learners to interact with this group by calling their attention to topics which might be discussed with the group. Through the experiment described here, we demonstrate that Common Sense can be useful to support the nursing education process, helping teachers to develop learning activities on the health care domain.
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Improving Human-Computer Interaction by Developing Culture-Sensitive Applications Based on Common Sense Knowledge
Human Computer Interaction: New Developments, 2008Co-Authors: João Coutinho, Aparecido Fabiano Pinatti De CarvalhoAbstract:The advent of Web 3.0, claiming for personalization in interactive systems (Lassila & Hendler, 2007), and the need for systems capable of interacting in a more natural way in the future society flooded with computer systems and devices (Harper et al., 2008) show that great advances in HCI should be done. This chapter presents some contributions of LIA for the future of HCI, defending that using Common Sense Knowledge is a possibility for improving HCI, especially because people assign meaning to their messages based on their Common Sense and, therefore, the use of this Knowledge in developing user interfaces can make them more intuitive to the end-user. Moreover, as Common Sense Knowledge varies from group to group of people, it can be used for developing applications capable of giving different feedback for different target groups, as the applications presented along this chapter illustrate, allowing, in this way, interface personalization taking into account cultural issues. For the purpose of using Common Sense Knowledge in the development and design of computer systems, it is necessary to provide an architecture that allows it. This chapter presents LIAs approaches for Common Sense Knowledge acquisition, representation and use, as well as for natural language processing, contributing with those ones who intent to get into this challenging world to get started.
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can Common Sense uncover cultural differences in computer applications
International Conference on Artificial Intelligence in Theory and Practice, 2006Co-Authors: Junia Coutinho Anacleto, Aparecido Fabiano Pinatti De Carvalho, Henry Lieberman, Marie Tsutsumi, Vânia Paula De Almeida Neris, Jose Espinosa, Muriel De Souza Godoi, Silvia Helena ZemmascarenhasAbstract:Cultural differences play a very important role in matching computer interfaces to the expectations of users from different national and cultural backgrounds. But to date, there has been little systematic research as to the extent of such differences, and how to produce software that automatically takes into account these differences. We are studying these issues using a unique resource: Common Sense Knowledge bases in different languages. Our research points out that this kind of Knowledge can help computer systems to consider cultural differences. We describe our experiences with Knowledge bases containing thousands of sentences describing people and everyday activities, collected from volunteer Web contributors in three different cultures: Brazil, Mexico and the USA, and software which automatically searches for cultural differences amongst the three cultures, alerting the user to potential differences.