Due to the growth of online shopping and services, various types of products can be recommended to an individual. After reviewing the current methods for cross-domain recommendations, we believe that there is a need to make different types of recommendations by relying on a common base, and that it is better to depend on a target customer’s information when building the base, because the customer is the one common element in all the purchases. Therefore, we suggest a recommender system (RS) that develops a personality profile for each product, and represents items by an aggregated vector of personality features of the people who have liked the items. We investigate two ways to build personality profiles for items (IPPs). The first way is called average-based IPPs, which represents each item with five attributes that reflect the average Big Five Personality values of the users who like it. The second way is named proportion-based IPPs, which consists of 15 attributes that aggregate the number of fans who have high, average and low Big Five values. The system functions like an item-based collaborative filtering recommender; that is, it recommends items similar to those the user liked. Our system demonstrates the highest recommendation quality in providing cross-domain recommendations, compared to traditional item-based collaborative filtering systems and content-based recommenders.

No description available.

This thesis contributes to the cross-domain deterrence literature by examining how states use their various sources of power across its Diplomatic, Information, Military and Economic (DIME) instruments in order to determine the outcome of immediate deterrence stand-offs. With the increased complexities nation-states face in the 21st century, an update of the old framework was overdue. This study considers the Taiwan Strait Crisis and the Russian Invasion in Ukraine to expand the military-focused literature to incorporate all different sources of state power. This paper finds that cross-domain signals, through an interaction of the bargaining position and bargaining power, decide the outcome of immediate deterrence cases.

No description available.

Over the last decade, excess amount of information is being provided on the web and information filtering systems such as recommender systems have become one of the most important technologies to overcome the &bdquo / Information Overload&amp / #8223 / problem by providing personalized services to users. Several researches have been made to improve quality of recommendations and provide maximum user satisfaction within a single domain based on the domain specific knowledge. However, the current infrastructures of the recommender systems cannot provide the complete mechanisms to meet user needs in several domains and recommender systems show poor performance in cross-domain item recommendations. Within this thesis work, a dynamic framework is proposed which differs from the previous works as it focuses on the easy development of knowledge-based recommenders and it proposes an intensive cross domain capability with the help of domain knowledge. The framework has a generic and flexible structure that data models and user interfaces are generated based on ontologies. New recommendation domains can be integrated to the framework easily in order to improve recommendation diversity. The cross-domain recommendation is accomplished via an abstraction in domain features if the direct matching of the domain features is not possible when the domains are not very close to each other.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / There is an increasing amount of user-generated information in online documents, includ- ing user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as either positive, negative or neutral based on the words present in the document. Supervised learning approaches have been successfully used for sentiment classification in domains that are rich in labeled data. Some of these approaches make use of features such as unigrams, bigrams, sentiment words, adjective words, syntax trees (or variations of trees obtained using pruning strategies), etc. However, for some domains the amount of labeled data can be relatively small and we cannot train an accurate classifier using the supervised learning approach. Therefore, it is useful to study domain adaptation techniques that can transfer knowledge from a source domain that has labeled data to a target domain that has little or no labeled data, but a large amount of unlabeled data. We address this problem in the context of product reviews, specifically reviews of movies, DVDs and kitchen appliances. Our approach uses an Adapted Naive Bayes classifier (ANB) on top of the Expectation Maximization (EM) algorithm to predict the sentiment of a sentence. We use grams derived from complete syntax trees or from syntax subtrees as features, when training the ANB classifier. More precisely, we extract grams from syntax trees correspond- ing to sentences in either the source or target domains. To be able to transfer knowledge from source to target, we identify generalized features (grams) using the frequently co-occurring entropy (FCE) method, and represent the source instances using these generalized features. The target instances are represented with all grams occurring in the target, or with a reduced grams set obtained by removing infrequent grams. We experiment with different types of grams in a supervised framework in order to identify the most predictive types of gram, and further use those grams in the domain adaptation framework. Experimental results on several cross-domains task show that domain adaptation approaches that combine source and target data (small amount of labeled and some unlabeled data) can help learn classifiers for the target that are better than those learned from the labeled target data alone.

A Mineração de Opiniões é um processo que tem por objetivo extrair as opiniões e suas polaridades de sentimentos expressas em textos em língua natural. Essa área de pesquisa tem ganhado destaque devido ao volume de opiniões que os usuários compartilham na Internet, como revisões em sites de e-commerce, rede sociais e tweets. A Mineração de Opiniões baseada em Aspectos é uma alternativa promissora para analisar a polaridade do sentimento em um maior nível de detalhes. Os métodos tradicionais para extração de aspectos e classificação de sentimentos exigem a participação de especialistas de domínio para criar léxicos ou definir regras de extração para diferentes idiomas e domínios. Além disso, tais métodos usualmente exploram algoritmos de aprendizado supervisionado, porém exigem um grande conjunto de dados rotulados para induzir um modelo de classificação. Os desafios desta tese de doutorado estão relacionados a como diminuir a necessidade de grande esforço humano tanto para rotular dados, quanto para tratar a dependência de domínio para as tarefas de extração de aspectos e classificação de sentimentos dos aspectos para Mineração de Opiniões. Para reduzir a necessidade de grande quantidade de exemplos rotulados foi proposta uma abordagem semissupervisionada, denominada por Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN) em que são propostas representações de textos nas quais os atributos linguísticos, os aspectos candidatos e os rótulos de sentimentos são modelados por meio de redes heterogêneas. Para redução dos esforços para construir recursos específicos de domínio foi proposta uma abordagem baseada em aprendizado por transferência entre domínios denominada Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) que utiliza dados rotulados de outros domínios para suportar tarefas de aprendizado em domínios sem dados rotulados. Nessa abordagem são propostos uma representação em uma rede heterogênea e um método de propagação de rótulos. Os vértices da rede são os aspectos rotulados do domínio de origem, os atributos linguísticos e os candidatos a aspectos do domínio alvo. Além disso, foram analisados métodos de extração de aspectos e propostas algumas variações para considerar cenários nãosupervisionados e independentes de domínio. As soluções propostas nesta tese de doutorado foram avaliadas e comparadas as do estado-da-arte utilizando coleções de revisões de diferentes produtos e serviços. Os resultados obtidos nas avaliações experimentais são competitivos e demonstram que as soluções propostas são promissoras. / Opinion Mining is a process that aims to extract opinions and their sentiment polarities expressed in natural language texts. This area of research has been in the highlight because of the volume of opinions that users share on the available visualization means on the Internet (reviews on e-commerce sites, social networks, tweets, others). Aspect-based Opinion Mining is a promising alternative for analyzing the sentiment polarity on a high level of detail. The traditional methods for aspect extraction and sentiment classification require the participation of domain experts to create lexicons or define extraction rules for different languages and domains. In addition, such methods usually exploit supervised machine learning algorithms, but require a large set of labeled data to induce a classification model. The challenges of this doctoral thesis are related on to how to reduce the need for great human effort both: (i) to label data; and (ii) to treat domain dependency for the tasks of aspect extraction and aspect sentiment classification for Opinion Mining. In order to reduce the need for a large number of labeled examples, a semi-supervised approach was proposed, called Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN). In this approach, text representations are proposed in which linguistic attributes, candidate aspects and sentiment labels are modeled by heterogeneous networks. Also, a cross-domain learning approach called Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) is proposed in order to reduce efforts to build domain-specific resources, This approach uses labeled data from other domains to support learning tasks in domains without labeled data. A representation in a heterogeneous network and a label propagation method are proposed in this cross-domain learning approach. The vertices of the network are the labeled aspects of the source domain, the linguistic attributes, and the candidate aspects of the target domain. In addition, aspect extraction methods were analyzed and some variations were proposed to consider unsupervised and domain independent scenarios. The solutions proposed in this doctoral thesis were evaluated and compared to the state-of-the-art solutions using collections of different product and service reviews. The results obtained in the experimental evaluations are competitive and demonstrate that the proposed solutions are promising.

A Mineração de Opiniões é um processo que tem por objetivo extrair as opiniões e suas polaridades de sentimentos expressas em textos em língua natural. Essa área de pesquisa tem ganhado destaque devido ao volume de opiniões que os usuários compartilham na Internet, como revisões em sites de e-commerce, rede sociais e tweets. A Mineração de Opiniões baseada em Aspectos é uma alternativa promissora para analisar a polaridade do sentimento em um maior nível de detalhes. Os métodos tradicionais para extração de aspectos e classificação de sentimentos exigem a participação de especialistas de domínio para criar léxicos ou definir regras de extração para diferentes idiomas e domínios. Além disso, tais métodos usualmente exploram algoritmos de aprendizado supervisionado, porém exigem um grande conjunto de dados rotulados para induzir um modelo de classificação. Os desafios desta tese de doutorado estão relacionados a como diminuir a necessidade de grande esforço humano tanto para rotular dados, quanto para tratar a dependência de domínio para as tarefas de extração de aspectos e classificação de sentimentos dos aspectos para Mineração de Opiniões. Para reduzir a necessidade de grande quantidade de exemplos rotulados foi proposta uma abordagem semissupervisionada, denominada por Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN) em que são propostas representações de textos nas quais os atributos linguísticos, os aspectos candidatos e os rótulos de sentimentos são modelados por meio de redes heterogêneas. Para redução dos esforços para construir recursos específicos de domínio foi proposta uma abordagem baseada em aprendizado por transferência entre domínios denominada Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) que utiliza dados rotulados de outros domínios para suportar tarefas de aprendizado em domínios sem dados rotulados. Nessa abordagem são propostos uma representação em uma rede heterogênea e um método de propagação de rótulos. Os vértices da rede são os aspectos rotulados do domínio de origem, os atributos linguísticos e os candidatos a aspectos do domínio alvo. Além disso, foram analisados métodos de extração de aspectos e propostas algumas variações para considerar cenários nãosupervisionados e independentes de domínio. As soluções propostas nesta tese de doutorado foram avaliadas e comparadas as do estado-da-arte utilizando coleções de revisões de diferentes produtos e serviços. Os resultados obtidos nas avaliações experimentais são competitivos e demonstram que as soluções propostas são promissoras. / Opinion Mining is a process that aims to extract opinions and their sentiment polarities expressed in natural language texts. This area of research has been in the highlight because of the volume of opinions that users share on the available visualization means on the Internet (reviews on e-commerce sites, social networks, tweets, others). Aspect-based Opinion Mining is a promising alternative for analyzing the sentiment polarity on a high level of detail. The traditional methods for aspect extraction and sentiment classification require the participation of domain experts to create lexicons or define extraction rules for different languages and domains. In addition, such methods usually exploit supervised machine learning algorithms, but require a large set of labeled data to induce a classification model. The challenges of this doctoral thesis are related on to how to reduce the need for great human effort both: (i) to label data; and (ii) to treat domain dependency for the tasks of aspect extraction and aspect sentiment classification for Opinion Mining. In order to reduce the need for a large number of labeled examples, a semi-supervised approach was proposed, called Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN). In this approach, text representations are proposed in which linguistic attributes, candidate aspects and sentiment labels are modeled by heterogeneous networks. Also, a cross-domain learning approach called Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) is proposed in order to reduce efforts to build domain-specific resources, This approach uses labeled data from other domains to support learning tasks in domains without labeled data. A representation in a heterogeneous network and a label propagation method are proposed in this cross-domain learning approach. The vertices of the network are the labeled aspects of the source domain, the linguistic attributes, and the candidate aspects of the target domain. In addition, aspect extraction methods were analyzed and some variations were proposed to consider unsupervised and domain independent scenarios. The solutions proposed in this doctoral thesis were evaluated and compared to the state-of-the-art solutions using collections of different product and service reviews. The results obtained in the experimental evaluations are competitive and demonstrate that the proposed solutions are promising.

La présente étude intitulée «Terminologie juridique moderne de la langue anglaise: aspects linguistiques et cognitifs » aborde le langage juridique contemporain dans le cadre de la linguistique cognitive. Les objectifs de l'étude sont d'étudier les particularités de la terminologie juridique et de proposer des principes de systématisation, en se référant à la théorie cognitive de la métaphore. Il s’agit principalement : 1) de déterminer les concepts de base utilisés métaphoriquement dans la langue juridique ; 2) d'établir les correspondances principales entre domaines et les corrélations entre des éléments particuliers dans des domaines spécifiques. Pour répondre à cette question, un corpus d’anglais juridique a été constitué et soumis à une étude quantitative. Les expressions métaphoriques liées à la terminologie juridique ont été retirés et classés selon leur sens métaphorique. Il est ainsi apparu que les métaphores conceptuelles de la GUERRE, de la MEDECINE, du SPORT et de la CONSTRUCTION étaient les plus nombreuses et prégnantes en anglais juridique. Les projections et correspondances entre ces domaines sources et le domaine cible de la LOI ont été établies.Cette étude empirique repose sur 156 textes juridiques qui ont été rassemblés au sein d’un même corpus (COLE – Corpus of Legal English). Les sources renvoient à différentes catégories thématiques. Le corpus a été utilisé pour établir la réalité de certains phénomènes et interpréter les résultats quantitatifs dans le cadre de la théorie de la métaphore conceptuelle. / The present doctoral dissertation entitled “Modern English Legal Terminology: linguistic and cognitive aspects” investigates the contemporary legal idiom, from a cognitive linguistics perspective. The aim of this study is to map out the peculiarities of English legal terminology and develop principles of systematization, within the framework of conceptual metaphor theory. This means 1) determining the basic concepts used metaphorically in English legal language, and 2) establishing the main cross-domain mappings and correlations between separate items within concrete domains.The Corpus of Legal English (COLE) was set up and a quantitative analysis performed, in which metaphorical expressions related to legal terminology were searched for and classified on the basis of meanings, conceptual domains and mappings. Thus, the conceptual metaphors of WAR, MEDICINE, SPORT and CONSTRUCTION were found to be the most numerous and valuable in Legal English. The main cross-domain mappings between these source domains and the target domain of LAW were established.In order to carry out this data-driven study, 156 legal texts were selected and compiled into the Corpus of Legal English (COLE). The source-texts represent various thematic categories. The COLE was systematically used to interpret frequency counts from the point of view of conceptual metaphor theory. / Дисертаційне дослідження на тему «Сучасна англійська юридична термінологія: лінгвокогнитивний аспект» досліджує сучасну мову права з точки зору когнітивної лінгвістики. Головною метою дослідження було дослідження особливостей англійської юридичної термінології та принципів її систематизації з точки зору когнітивної теорії і власне теорії концептуальної метафори. В ході написання роботи були поставлені наступні цілі: 1) визначити головні концепти які використовуються у якості метафор в англійській мові права; 2) встановити головні концептуальні зв’язки між окремими елементами доменів.З метою вирішення цих питань і задач був проведений кількісний аналіз корпусу юридичної англійської мови. В ході цього аналізу біли виділені та класифіковані метафоричні вирази які пов’язані з юридичною термінологією згідно їх метафоричного значення. В результаті аналізу було виявлено що концептуальні метафори WAR, MEDICINE, SPORT та CONSTRUCTION займають домінуюче положення в мові права. Також були встановлені основні концептуальні зв’язки між сферою-джерелом та сферою-ціллю.В даному дослідженні було використано спеціально створений корпус, який включає в себе 156 правових текстів різноманітної сюжетної направленості, для проведення кількісного аналізу з точки зору концептуальної метафори.

Modern deep neural networks are proficient at solving various visual recognition and understanding tasks, as long as a sufficiently large labeled dataset is available during the training time. However, the progress of these visual tasks is limited by the number of manual annotations. On the other hand, it is usually time-consuming and error-prone to annotate visual data, rendering the challenge of scaling up human labeling for many visual tasks. Fortunately, it is easy to collect large-scale, diverse unlabeled visual data from the Internet. And we can acquire a large amount of synthetic visual data with annotations from game engines effortlessly. In this dissertation, we explore how to utilize the unlabeled data and synthetic labeled data for various visual tasks, aiming to replace or reduce the direct supervision from the manual annotations. The key idea is to encourage deep neural networks to produce consistent predictions across different transformations (\eg geometry, temporal, photometric, etc.). We organize the dissertation as follows. In Part I, we propose to use the consistency over different geometric formulations and a cycle consistency over time to tackle the low-level scene geometry perception tasks in a self-supervised learning setting. In Part II, we tackle the high-level semantic understanding tasks in a semi-supervised learning setting, with the constraint that different augmented views of the same visual input maintain consistent semantic information. In Part III, we tackle the cross-domain image segmentation problem. By encouraging an adaptive segmentation model to output consistent results for a diverse set of strongly-augmented synthetic data, the model learns to perform test-time adaptation on unseen target domains with one single forward pass, without model training or optimization at the inference time. / Doctor of Philosophy / Recently, deep learning has emerged as one of the most powerful tools to solve various visual understanding tasks. However, the development of deep learning methods is significantly limited by the amount of manually labeled data. On the other hand, it is usually time-consuming and error-prone to annotate visual data, making the human labeling process not easily scalable. Fortunately, it is easy to collect large-scale, diverse raw visual data from the Internet (\eg search engines, YouTube, Instagram, etc.). And we can acquire a large amount of synthetic visual data with annotations from game engines effortlessly. In this dissertation, we explore how we can utilize the raw visual data and synthetic data for various visual tasks, aiming to replace or reduce the direct supervision from the manual annotations. The key idea behind this is to encourage deep neural networks to produce consistent predictions of the same visual input across different transformations (\eg geometry, temporal, photometric, etc.). We organize the dissertation as follows. In Part I, we propose using the consistency over different geometric formulations and a forward-backward cycle consistency over time to tackle the low-level scene geometry perception tasks, using unlabeled visual data only. In Part II, we tackle the high-level semantic understanding tasks using both a small amount of labeled data and a large amount of unlabeled data jointly, with the constraint that different augmented views of the same visual input maintain consistent semantic information. In Part III, we tackle the cross-domain image segmentation problem. By encouraging an adaptive segmentation model to output consistent results for a diverse set of strongly-augmented synthetic data, the model learns to perform test-time adaptation on unseen target domains.