This work studies something new in Web search to cater for users' cross-lingual information needs by using the common search interests found across different languages. We assume a generic scenario for monolingual users who are interested to find their relevant information under three general settings: (1) find relevant information in a foreign language, which needs machine to translate search results into the user's own language; (2) find relevant information in multiple languages including the source language, which also requires machine translation for back translating search results; (3) find relevant information only in the user's language, but due to the intrinsic cross-lingual nature of many queries, monolingual search can be done with the assistance of cross-lingual information from another language. / We approach the problem by substantially extending two core mechanics of information retrieval for Web search across languages, namely, query formulation and relevance ranking. First, unlike traditional cross-lingual methods such as query translation and expansion, we propose a novel Cross-Lingual Query Suggestion model by leveraging large-scale query logs of search engine to learn to suggest closely related queries in the target language for a given source language query. The rationale behind our approach is the ever-increasing common search interests across Web users in different languages. Second, we generalize the usefulness of common search interests to enhance relevance ranking of documents by exploiting the correlation among the search results derived from bilingual queries, and overcome the weakness of traditional relevance estimation that only uses information of a single language or that of different languages separately. To this end, we attempt to learn a ranking function that incorporates various similarity measures among the retrieved documents in different languages. By modeling the commonality or similarity of search results, relevant documents in one language may help the relevance estimation of documents in a different language, and hence can improve the overall relevance estimation. This similar intuition is applicable to all the three settings described above. / Gao, Wei. / Adviser: Kaw-Fai Wong. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 114-122). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Li Kar Wing. / "July 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 127-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

In the first part of this thesis, we present a new understanding of the latent semantic space of a dataset from the dual perspective, which relaxes the above assumed conditions and leads naturally to a unified kernel function for a class of vector space models. New semantic analysis methods based on the unified kernel function are developed, which combine the advantages of LSI and GVSM. We also show that the new methods possess the stable property on the rank choice, i.e., even if the selected rank is quite far away from the optimal one, the retrieval performance will not degrade much. The experimental results of our methods on the standard test sets are promising. / In the second part of this thesis, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector-space model (VSM) for information retrieval. The Q-analysis devised by R. H. Atkin may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new query expansion method. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective. Finally, the notion of homology is introduced to the topological analysis of datasets and its possible relation to word sense disambiguation is studied through a simple example. / With the vast amount of textual information available today, the task of designing effective and efficient retrieval methods becomes more important and complex. The Basic Vector Space Model (BVSM) is well known in information retrieval. Unfortunately, it can not retrieve all relevant documents since it is based on literal term matching. The Generalized Vector Space Model (GVSM) and the Latent Semantic Indexing (LSI) are two famous semantic retrieval methods, in which some underlying latent semantic structures in the dataset are assumed. However, their assumptions about where the semantic structure locates are a bit strong. Moreover, the performance of LSI can be very different for various datasets and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. The present thesis focuses on providing answers to these two questions. / Li , Dandan. / "August 2007." / Adviser: Chung-Ping Kwong. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1108. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 118-120). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

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Automatic music transcription (AMT) is commonly defined as the process of converting an acoustic musical signal into some form of musical notation, and can be split into two separate phases: (1) multi-pitch detection, the conversion of an audio signal into a time-frequency representation similar to a MIDI file; and (2) converting from this time-frequency representation into a musical score. A substantial amount of AMT research in recent years has concentrated on multi-pitch detection, and yet, in the case of the transcription of polyphonic music, there has been little progress. There are many potential reasons for this slow progress, but this thesis concentrates on the (lack of) use of music language models during the transcription process. In particular, a music language model would impart to a transcription system the background knowledge of music theory upon which a human transcriber relies. In the related field of automatic speech recognition, it has been shown that the use of a language model drawn from the field of natural language processing (NLP) is an essential component of a system for transcribing spoken word into text, and there is no reason to believe that music should be any different. This thesis will show that a music language model inspired by NLP techniques can be used successfully for transcription. In fact, this thesis will create the blueprint for such a music language model. We begin with a brief overview of existing multi-pitch detection systems, in particular noting four key properties which any music language model should have to be useful for integration into a joint system for AMT: it should (1) be probabilistic, (2) not use any data a priori, (3) be able to run on live performance data, and (4) be incremental. We then investigate voice separation, creating a model which achieves state-of-the-art performance on the task, and show that, used as a simple music language model, it improves multi-pitch detection performance significantly. This is followed by an investigation of metrical detection and alignment, where we introduce a grammar crafted for the task which, combined with a beat-tracking model, achieves state-of-the-art results on metrical alignment. This system's success adds more evidence to the long-existing hypothesis that music and language consist of extremely similar structures. We end by investigating the joint analysis of music, in particular showing that a combination of our two models running jointly outperforms each running independently. We also introduce a new joint, automatic, quantitative metric for the complete transcription of an audio recording into an annotated musical score, something which the field currently lacks.

Chan, Kam Tong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves [101]-113). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Major Topic --- p.1 / Chapter 1.1.1 --- Opinion Mining --- p.1 / Chapter 1.1.2 --- Human Computation --- p.2 / Chapter 1.2 --- Major Work and Contributions --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Opinion Mining --- p.6 / Chapter 2.1.1 --- Feature Extraction --- p.6 / Chapter 2.1.2 --- Sentiment Analysis --- p.9 / Chapter 2.2 --- Social Computing --- p.15 / Chapter 2.2.1 --- Social Bookmarking --- p.15 / Chapter 2.2.2 --- Social Games --- p.18 / Chapter 3 --- Feature-Opinion Association for Sentiment Analysis --- p.25 / Chapter 3.1 --- Motivation --- p.25 / Chapter 3.2 --- Problem Definition --- p.27 / Chapter 3.2.1 --- Definitions --- p.27 / Chapter 3.3 --- Closer look at the problem --- p.28 / Chapter 3.3.1 --- Discussion --- p.29 / Chapter 3.4 --- Proposed Approach --- p.29 / Chapter 3.4.1 --- Nearest Opinion Word (DIST) --- p.31 / Chapter 3.4.2 --- Co-Occurrence Frequency (COF) --- p.31 / Chapter 3.4.3 --- Co-Occurrence Ratio (COR) --- p.32 / Chapter 3.4.4 --- Likelihood-Ratio Test (LHR) --- p.32 / Chapter 3.4.5 --- Combined Method --- p.34 / Chapter 3.4.6 --- Feature-Opinion Association Algorithm --- p.35 / Chapter 3.4.7 --- Sentiment Lexicon Expansion --- p.36 / Chapter 3.5 --- Evaluation --- p.37 / Chapter 3.5.1 --- Corpus Data Set --- p.37 / Chapter 3.5.2 --- Test Data set --- p.37 / Chapter 3.5.3 --- Feature-Opinion Association Accuracy --- p.38 / Chapter 3.6 --- Summary --- p.45 / Chapter 4 --- Social Game for Opinion Mining --- p.46 / Chapter 4.1 --- Motivation --- p.46 / Chapter 4.2 --- Social Game Model --- p.47 / Chapter 4.2.1 --- Definitions --- p.48 / Chapter 4.2.2 --- Social Game Problem --- p.51 / Chapter 4.2.3 --- Social Game Flow --- p.51 / Chapter 4.2.4 --- Answer Extraction Procedure --- p.52 / Chapter 4.3 --- Social Game Properties --- p.53 / Chapter 4.3.1 --- Type of Information --- p.53 / Chapter 4.3.2 --- Game Structure --- p.55 / Chapter 4.3.3 --- Verification Method --- p.59 / Chapter 4.3.4 --- Game Mechanism --- p.60 / Chapter 4.3.5 --- Player Requirement --- p.62 / Chapter 4.4 --- Design Guideline --- p.63 / Chapter 4.5 --- Opinion Mining Game Design --- p.65 / Chapter 4.5.1 --- OpinionMatch --- p.65 / Chapter 4.5.2 --- FeatureGuess --- p.68 / Chapter 4.6 --- Summary --- p.71 / Chapter 5 --- Tag Sentiment Analysis for Social Bookmark Recommendation System --- p.72 / Chapter 5.1 --- Motivation --- p.72 / Chapter 5.2 --- Problem Statement --- p.74 / Chapter 5.2.1 --- Social Bookmarking Model --- p.74 / Chapter 5.2.2 --- Social Bookmark Recommendation (SBR) Problem --- p.75 / Chapter 5.3 --- Proposed Approach --- p.75 / Chapter 5.3.1 --- Social Bookmark Recommendation Framework --- p.75 / Chapter 5.3.2 --- Subjective Tag Detection (STD) --- p.77 / Chapter 5.3.3 --- Similarity Matrices --- p.80 / Chapter 5.3.4 --- User-Website matrix: --- p.81 / Chapter 5.3.5 --- User-Tag matrix --- p.81 / Chapter 5.3.6 --- Website-Tag matrix --- p.82 / Chapter 5.4 --- Pearson Correlation Coefficient --- p.82 / Chapter 5.5 --- Social Network-based User Similarity --- p.83 / Chapter 5.6 --- User-oriented Website Ranking --- p.85 / Chapter 5.7 --- Evaluation --- p.87 / Chapter 5.7.1 --- Bookmark Data --- p.87 / Chapter 5.7.2 --- Social Network --- p.87 / Chapter 5.7.3 --- Subjective Tag List --- p.87 / Chapter 5.7.4 --- Subjective Tag Detection --- p.88 / Chapter 5.7.5 --- Bookmark Recommendation Quality --- p.90 / Chapter 5.7.6 --- System Evaluation --- p.91 / Chapter 5.8 --- Summary --- p.93 / Chapter 6 --- Conclusion and Future Work --- p.94 / Chapter A --- List of Symbols and Notations --- p.97 / Chapter B --- List of Publications --- p.100 / Bibliography --- p.101

Les psychologues (comme A. Damasio, K. R. Scherer, P. Ekman) ont montré que l’émotion est un élément essentiel dans la prise de décision, dans l’évolution des capacités d’apprentissage et de création, dans l’interaction sociale. Il est donc naturel de s'intéresser à l'expression d'émotions dans le cadre de l'interaction homme-machine. Nous avons proposé dans un premier temps le modèle GRACE, modèle générique des émotions pour les applications computationnelles. Nous nous sommes basés en particulier sur la théorie psychologique de K. R. Scherer, qui cherche à produire une théorie des processus émotionnels qui soit modélisable et calculable. La pertinence de notre modèle a été vérifiée et validée via une comparaison avec différents modèles computationnels existants. Si le modèle GRACE est générique, nous nous sommes attachés à montrer qu’il pouvait s’instancier dans un contexte particulier, en l’occurrence l’interaction homme-robot utilisant la modalité musicale. Nous nous sommes intéressés pour cela d’une part à la conception d’un module d’analyse du contenu émotionnel d’une séquence musicale, d’autre part à la conception de mouvements émotionnellement expressifs pour un robot mobile. Du point de vue de l’analyse musicale, la contribution principale de la thèse porte sur la proposition d’un ensemble réduit d’indicateurs musicaux et sur la validation du module d’analyse sur une base de données de grande taille conçue par un expert en musicologie. Du point de vue de la robotique, nous avons pu montrer expérimentalement qu’un robot avec des capacités expressives très limitées (déplacements, mouvements de caméra) pouvait néanmoins exprimer de manière satisfaisante un ensemble réduit d’émotions simples (joie, colère, tristesse, sérénité). / Emotion, as psychologists argue (like A. Damasio, K. R. Scherer, P. Ekman), is an essential factor for human beings in making decision, learning, inventing things, and interacting with others. Based on this statement, researchers in Human-Machine Interaction have been interested in adding emotional abilities to their applications. With the same goal of studying emotional abilities, we propose, in our work, a model of emotions named GRACE, which helps in modelling emotions in computational applications. We based our model on the work of psychologist Klaus R. Scherer, who intensively searches to form a generic model of emotion applicable to computational domain (like informatics, robotics, etc.). We demonstrate the pertinence of our model by comparing it to other existing models of emotions in the field of informatics and robotics. In this thesis, we also worked on the instantiation of GRACE, in particular the components Cognitive Interpretation and Expression. These two components have been developed to be applied in the context of interacting with users using music. To develop Cognitive Interpretation, we worked on the extraction of emotional content in musical excerpts. Our contribution consists in proposing a reduced number of musical features to efficiently extract the emotional content in music, and in validating them via a learning system with a large database designed by a musicologist. For Expression, we have worked on the design of emotional moves of a mobile robot. Through very limited moves (moves in space, camera moves), we have shown that with dance-inspired motions, the robot could efficiently convey basic emotions (i.e. happiness, sadness, anger, serenity) to people.

A system is proposed which separates clauses from complex sentences into simpler stand-alone sentences. This is useful as an initial step on raw text, where the resulting processed text may be fed into text-to-text applications such as Automatic Summarization, Question Answering, and Machine Translation, where complex sentences are difficult to process. Grammatical natural language transformations provide a possible method to simplify complex sentences to enhance the results of text-to-text applications. Using shallow parsing, this system improves the performance of existing systems to identify and separate marked and unmarked embedded clauses in complex sentence structure resulting in syntactically simplified source for further processing.

Orientador: Walter Moreira / Banca: Edberto Ferneda / Banca: Rogério Aparecido Sá Ramalho / Resumo: Aborda sobre as relações conceituais que se mostram presentes em todos os sistemas de organização do conhecimento. Ao organizar o conhecimento, delimita-se classes e cada classe desempenha relações com outras classes. Neste contexto, inserem-se a Classificação Decimal de Dewey (CDD) e as ontologias como forma de organizar o conhecimento físico e digital, respectivamente. Partindo-se do pressuposto de que os sistemas de organização do conhecimento se complementam, o objetivo geral desta pesquisa é o de comparar o modelo das relações conceituais presentes nos sistemas de classificação com o modelo de relações conceituais presentes nas ontologias. Para tanto, tomou-se como objeto de análise a classe 590 da CDD sobre Animais, e a BBC Wildlife Ontology e, assim, foram definidos como objetivos específicos: Compreender os tipos de relações conceituais e suas características; Analisar as relações conceituais da classe 590 da CDD; Investigar a consistência das relações conceituais na BBC Wildlife Ontology; Realizar análise comparativa sobre os tipos de relações conceituais identificados na CDD e na BBC Wildlife Ontology; Criar um modelo de ontologia que sintetize os dois sistemas de organização do conhecimento analisados para fins de sistematização. Para a realização desses objetivos adotou-se, em síntese, os seguintes procedimentos metodológicos: 1) coleta de dados a partir da literatura da área; 2) análise das relações conceituais da classe 590 da CDD; 3) análise das relações concei... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: It deals with the conceptual relations that are present in all Knowledge Organization Systems. When knowledge is organized, classes are delimited and each class performs relationships with other classes. The Dewey Decimal Classification (CDD) and the ontologies are inserted in this context as a way of organizing the physical and digital knowledge, respectively. Assuming that the Knowledge Systems Organization complement each other, the general objective of this research is to compare the model of the conceptual relations present in the classification systems with the model of conceptual relations present in the ontologies. Therefore, the CDD class 590 about Animals and the BBC Wildlife Ontology were taken as the object of analysis and thus were defined as specific objectives. To understand the types of conceptual relationships and their characteristics; to analyze the conceptual relationships of class 590 of the CDD; To investigate the consistency of conceptual relationships in the BBC Wildlife Ontology; To conduct a comparative analysis of the types of conceptual relationships identified in the CDD and the BBC Wildlife Ontology; To create an ontology model that synthesizes the two Knowledge Organization Systems analyzed for systematization purposes. In order to achieve these objectives, the following methodological procedures were adopted: 1) data collection from the literature of the area; 2) analysis of the conceptual relationships of class 590 from CDD; 3) analysis of con... (Complete abstract click electronic access below) / Mestre

Navigating through the debris of the information explosion requires powerful, flexible search tools. These tools must be both useful and useable; that is, they must do their jobs effectively without placing too many burdens on the user. While general interest search engines, such as Google, have addressed this latter challenge well, more topic-specific search engines, such as PubMed, have not. These search engines, though effective, often require training in their use, as well as in-depth knowledge of the domain over which they operate. Furthermore, search results are often returned in an order irrespective of users' preferences, forcing them to manually search through search results in order to find the documents they find most useful. To solve these problems, we intend to learn ranking functions from user relevance preferences. Applying these ranking functions to search results allows us to improve search usability without having to reengineer existing, effective search engines. Using ranking SVMs and active learning techniques, we can effectively learn what is relevant to a user from relatively small amounts of preference data, and apply these learned models as ranking functions. This gives users the convenience of seeing relevance-ordered search results, which are tailored to their preferences as opposed to using a one-size-fits-all sorting method. As giving preference feedback does not require in-depth domain knowledge, this approach is suitable for use by domain experts as well as neophytes. Furthermore, giving preference feedback does not require a great deal of training, adding very little overhead to the search process.