Approaches to implement and evaluate aggregated search

La recherche d'information agrégée peut être vue comme un troisième paradigme de recherche d'information après la recherche d'information ordonnée (ranked retrieval) et la recherche d'information booléenne (boolean retrieval). Les deux paradigmes les plus explorés jusqu'à aujourd'hui retournent un ensemble ou une liste ordonnée de résultats. C'est à l'usager de parcourir ces ensembles/listes et d'en extraire l'information nécessaire qui peut se retrouver dans plusieurs documents. De manière alternative, la recherche d'information agrégée ne s'intéresse pas seulement à l'identification des granules (nuggets) d'information pertinents, mais aussi à l'assemblage d'une réponse agrégée contenant plusieurs éléments. Dans nos travaux, nous analysons les travaux liés à la recherche d'information agrégée selon un schéma général qui comprend 3 parties: dispatching de la requête, recherche de granules d'information et agrégation du résultat. Les approches existantes sont groupées autours de plusieurs perspectives générales telle que la recherche relationnelle, la recherche fédérée, la génération automatique de texte, etc. Ensuite, nous nous sommes focalisés sur deux pistes de recherche selon nous les plus prometteuses: (i) la recherche agrégée relationnelle et (ii) la recherche agrégée inter-verticale. * La recherche agrégée relationnelle s'intéresse aux relations entre les granules d'information pertinents qui servent à assembler la réponse agrégée. En particulier, nous nous sommes intéressés à trois types de requêtes notamment: requête attribut (ex. président de la France, PIB de l'Italie, maire de Glasgow, ...), requête instance (ex. France, Italie, Glasgow, Nokia e72, ...) et requête classe (pays, ville française, portable Nokia, ...). Pour ces requêtes qu'on appelle requêtes relationnelles nous avons proposés trois approches pour permettre la recherche de relations et l'assemblage des résultats. Nous avons d'abord mis l'accent sur la recherche d'attributs qui peut aider à répondre aux trois types de requêtes. Nous proposons une approche à large échelle capable de répondre à des nombreuses requêtes indépendamment de la classe d'appartenance. Cette approche permet l'extraction des attributs à partir des tables HTML en tenant compte de la qualité des tables et de la pertinence des attributs. Les différentes évaluations de performances effectuées prouvent son efficacité qui dépasse les méthodes de l'état de l'art. Deuxièmement, nous avons traité l'agrégation des résultats composés d'instances et d'attributs. Ce problème est intéressant pour répondre à des requêtes de type classe avec une table contenant des instances (lignes) et des attributs (colonnes). Pour garantir la qualité du résultat, nous proposons des pondérations sur les instances et les attributs promouvant ainsi les plus représentatifs. Le troisième problème traité concerne les instances de la même classe (ex. France, Italie, Allemagne, ...). Nous proposons une approche capable d'identifier massivement ces instances en exploitant les listes HTML. Toutes les approches proposées fonctionnent à l'échelle Web et sont importantes et complémentaires pour la recherche agrégée relationnelle. Enfin, nous proposons 4 prototypes d'application de recherche agrégée relationnelle. Ces derniers peuvent répondre des types de requêtes différents avec des résultats relationnels. Plus précisément, ils recherchent et assemblent des attributs, des instances, mais aussi des passages et des images dans des résultats agrégés. Un exemple est la requête ``Nokia e72" dont la réponse sera composée d'attributs (ex. prix, poids, autonomie batterie, ...), de passages (ex. description, reviews, ...) et d'images. Les résultats sont encourageants et illustrent l'utilité de la recherche agrégée relationnelle. * La recherche agrégée inter-verticale s'appuie sur plusieurs moteurs de recherche dits verticaux tel que la recherche d'image, recherche vidéo, recherche Web traditionnelle, etc. Son but principal est d'assembler des résultats provenant de toutes ces sources dans une même interface pour répondre aux besoins des utilisateurs. Les moteurs de recherche majeurs et la communauté scientifique nous offrent déjà une série d'approches. Notre contribution consiste en une étude sur l'évaluation et les avantages de ce paradigme. Plus précisément, nous comparons 4 types d'études qui simulent des situations de recherche sur un total de 100 requêtes et 9 sources différentes. Avec cette étude, nous avons identifiés clairement des avantages de la recherche agrégée inter-verticale et nous avons pu déduire de nombreux enjeux sur son évaluation. En particulier, l'évaluation traditionnelle utilisée en RI, certes la moins rapide, reste la plus réaliste. Pour conclure, nous avons proposé des différents approches et études sur deux pistes prometteuses de recherche dans le cadre de la recherche d'information agrégée. D'une côté, nous avons traité trois problèmes importants de la recherche agrégée relationnelle qui ont porté à la construction de 4 prototypes d'application avec des résultats encourageants. De l'autre côté, nous avons mis en place 4 études sur l'intérêt et l'évaluation de la recherche agrégée inter-verticale qui ont permis d'identifier les enjeux d'évaluation et les avantages du paradigme. Comme suite à long terme de ce travail, nous pouvons envisager une recherche d'information qui intègre plus de granules relationnels et plus de multimédia.Aggregated search or aggregated retrieval can be seen as a third paradigm for information retrieval following the Boolean retrieval paradigm and the ranked retrieval paradigm. In the first two, we are returned respectively sets and ranked lists of search results. It is up to the time-poor user to scroll this set/list, scan within different documents and assemble his/her information need. Alternatively, aggregated search not only aims the identification of relevant information nuggets, but also the assembly of these nuggets into a coherent answer. In this work, we present at first an analysis of related work to aggregated search which is analyzed with a general framework composed of three steps: query dispatching, nugget retrieval and result aggregation. Existing work is listed aside different related domains such as relational search, federated search, question answering, natural language generation, etc. Within the possible research directions, we have then focused on two directions we believe promise the most namely: relational aggregated search and cross-vertical aggregated search. * Relational aggregated search targets relevant information, but also relations between relevant information nuggets which are to be used to assemble reasonably the final answer. In particular, there are three types of queries which would easily benefit from this paradigm: attribute queries (e.g. president of France, GDP of Italy, major of Glasgow, ...), instance queries (e.g. France, Italy, Glasgow, Nokia e72, ...) and class queries (countries, French cities, Nokia mobile phones, ...). We call these queries as relational queries and we tackle with three important problems concerning the information retrieval and aggregation for these types of queries. First, we propose an attribute retrieval approach after arguing that attribute retrieval is one of the crucial problems to be solved. Our approach relies on the HTML tables in the Web. It is capable to identify useful and relevant tables which are used to extract relevant attributes for whatever queries. The different experimental results show that our approach is effective, it can answer many queries with high coverage and it outperforms state of the art techniques. Second, we deal with result aggregation where we are given relevant instances and attributes for a given query. The problem is particularly interesting for class queries where the final answer will be a table with many instances and attributes. To guarantee the quality of the aggregated result, we propose the use of different weights on instances and attributes to promote the most representative and important ones. The third problem we deal with concerns instances of the same class (e.g. France, Germany, Italy ... are all instances of the same class). Here, we propose an approach that can massively extract instances of the same class from HTML lists in the Web. All proposed approaches are applicable at Web-scale and they can play an important role for relational aggregated search. Finally, we propose 4 different prototype applications for relational aggregated search. They can answer different types of queries with relevant and relational information. Precisely, we not only retrieve attributes and their values, but also passages and images which are assembled into a final focused answer. An example is the query ``Nokia e72" which will be answered with attributes (e.g. price, weight, battery life ...), passages (e.g. description, reviews ...) and images. Results are encouraging and they illustrate the utility of relational aggregated search. * The second research direction that we pursued concerns cross-vertical aggregated search, which consists of assembling results from different vertical search engines (e.g. image search, video search, traditional Web search, ...) into one single interface. Here, different approaches exist in both research and industry. Our contribution concerns mostly evaluation and the interest (advantages) of this paradigm. We propose 4 different studies which simulate different search situations. Each study is tested with 100 different queries and 9 vertical sources. Here, we could clearly identify new advantages of this paradigm and we could identify different issues with evaluation setups. In particular, we observe that traditional information retrieval evaluation is not the fastest but it remains the most realistic. To conclude, we propose different studies with respect to two promising research directions. On one hand, we deal with three important problems of relational aggregated search following with real prototype applications with encouraging results. On the other hand, we have investigated on the interest and evaluation of cross-vertical aggregated search. Here, we could clearly identify some of the advantages and evaluation issues. In a long term perspective, we foresee a possible combination of these two kinds of approaches to provide relational and cross-vertical information retrieval incorporating more focus, structure and multimedia in search results

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Colombus: providing personalized recommendations for drifting user interests

The query formulationg process if often a problematic activity due to the cognitive load that it imposes to users. This issue is further amplified by the uncertainty of searchers with regards to their searching needs and their lack of training on effective searching techniques. Also, given the tremendous growth of the world wide web, the amount of imformation users find during their daily search episodes is often overwhelming. Unfortunatelly, web search engines do not follow the trends and advancements in this area, while real personalization features have yet to appear. As a result, keeping up-to-date with recent information about our personal interests is a time-consuming task. Also, often these information requirements change by sliding into new topics. In this case, the rate of change can be sudden and abrupt, or more gradual. Taking into account all these aspects, we believe that an information assistant, a profile-aware tool capable of adapting to users’ evolving needs and aiding them to keep track of their personal data, can greatly help them in this endeavor. Information gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible effort from them. In this paper, we describe the design, development and evaluation of Colombus, a system aiming to meet individual needs of the searchers. The system’s goal is to pro-actively fetch and present relevant, high quality documents on regular basis. Based entirely on implicit feedback gathering, our system concentrates on detecting drifts in user interests and accomodate them effectively in their profiles with no additional interaction from their side. Current methodologies in information retrieval do not support the evaluation of such systems and techniques. Lab-based experiments can be carried out in large batches but their accuracy often questione. On the other hand, user studies are much more accurate, but setting up a user base for large-scale experiments is often not feasible. We have designed a hybrid evaluation methodology that combines large sets of lab experiments based on searcher simulations together with user experiments, where fifteen searchers used the system regularly for 15 days. At the first stage, the simulation experiments were aiming attuning Colombus, while the various component evaluation and results gathering was carried out at the second stage, throughout the user study. A baseline system was also employed in order to make a direct comparison of Colombus against a current web search engine. The evaluation results illustrate that the Personalized Information Assistant is effective in capturing and satisfying users’ evolving information needs and providing additional information on their behalf

Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari

Text-image synergy for multimodal retrieval and annotation

Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text und Bild sind die beiden häufigsten Arten von Inhalten im Internet. Während es für Menschen einfach ist, gerade aus dem Zusammenspiel von Text- und Bildinhalten Informationen zu erfassen, stellt diese kombinierte Darstellung von Inhalten Softwaresysteme vor große Herausforderungen. In dieser Dissertation werden Probleme studiert, für deren Lösung das Verständnis des Zusammenspiels von Text- und Bildinhalten wesentlich ist. Es werden Methoden und Vorschläge präsentiert und empirisch bewertet, die semantische Verbindungen zwischen Text und Bild in multimodalen Daten herstellen. Wir stellen in dieser Dissertation vier miteinander verbundene Text- und Bildprobleme vor: • Bildersuche. Ob Bilder anhand von textbasierten Suchanfragen gefunden werden, hängt stark davon ab, ob der Text in der Nähe des Bildes mit dem der Anfrage übereinstimmt. Bilder ohne textuellen Kontext, oder sogar mit thematisch passendem Kontext, aber ohne direkte Übereinstimmungen der vorhandenen Schlagworte zur Suchanfrage, können häufig nicht gefunden werden. Zur Abhilfe schlagen wir vor, drei Arten von Informationen in Kombination zu nutzen: visuelle Informationen (in Form von automatisch generierten Bildbeschreibungen), textuelle Informationen (Stichworte aus vorangegangenen Suchanfragen), und Alltagswissen. • Verbesserte Bildbeschreibungen. Bei der Objekterkennung durch Computer Vision kommt es des Öfteren zu Fehldetektionen und Inkohärenzen. Die korrekte Identifikation von Bildinhalten ist jedoch eine wichtige Voraussetzung für die Suche nach Bildern mittels textueller Suchanfragen. Um die Fehleranfälligkeit bei der Objekterkennung zu minimieren, schlagen wir vor Alltagswissen einzubeziehen. Durch zusätzliche Bild-Annotationen, welche sich durch den gesunden Menschenverstand als thematisch passend erweisen, können viele fehlerhafte und zusammenhanglose Erkennungen vermieden werden. • Bild-Text Platzierung. Auf Internetseiten mit Text- und Bildinhalten (wie Nachrichtenseiten, Blogbeiträge, Artikel in sozialen Medien) werden Bilder in der Regel an semantisch sinnvollen Positionen im Textfluss platziert. Wir nutzen dies um ein Framework vorzuschlagen, in dem relevante Bilder ausgesucht werden und mit den passenden Abschnitten eines Textes assoziiert werden. • Bildunterschriften. Bilder, die als Teil von multimodalen Inhalten zur Verbesserung der Lesbarkeit von Texten dienen, haben typischerweise Bildunterschriften, die zum Kontext des umgebenden Texts passen. Wir schlagen vor, den Kontext beim automatischen Generieren von Bildunterschriften ebenfalls einzubeziehen. Üblicherweise werden hierfür die Bilder allein analysiert. Wir stellen die kontextbezogene Bildunterschriftengenerierung vor. Unsere vielversprechenden Beobachtungen und Ergebnisse eröffnen interessante Möglichkeiten für weitergehende Forschung zur computergestützten Erfassung des Zusammenspiels von Text- und Bildinhalten

Enriching unstructured media content about events to enable semi-automated summaries, compilations, and improved search by leveraging social networks

(i) Mobile devices and social networks are omnipresent Mobile devices such as smartphones, tablets, or digital cameras together with social networks enable people to create, share, and consume enormous amounts of media items like videos or photos both on the road or at home. Such mobile devices "by pure definition" accompany their owners almost wherever they may go. In consequence, mobile devices are omnipresent at all sorts of events to capture noteworthy moments. Exemplary events can be keynote speeches at conferences, music concerts in stadiums, or even natural catastrophes like earthquakes that affect whole areas or countries. At such events" given a stable network connection" part of the event-related media items are published on social networks both as the event happens or afterwards, once a stable network connection has been established again. (ii) Finding representative media items for an event is hard Common media item search operations, for example, searching for the official video clip for a certain hit record on an online video platform can in the simplest case be achieved based on potentially shallow human-generated metadata or based on more profound content analysis techniques like optical character recognition, automatic speech recognition, or acoustic fingerprinting. More advanced scenarios, however, like retrieving all (or just the most representative) media items that were created at a given event with the objective of creating event summaries or media item compilations covering the event in question are hard, if not impossible, to fulfill at large scale. The main research question of this thesis can be formulated as follows. (iii) Research question "Can user-customizable media galleries that summarize given events be created solely based on textual and multimedia data from social networks?" (iv) Contributions In the context of this thesis, we have developed and evaluated a novel interactive application and related methods for media item enrichment, leveraging social networks, utilizing the Web of Data, techniques known from Content-based Image Retrieval (CBIR) and Content-based Video Retrieval (CBVR), and fine-grained media item addressing schemes like Media Fragments URIs to provide a scalable and near realtime solution to realize the abovementioned scenario of event summarization and media item compilation. (v) Methodology For any event with given event title(s), (potentially vague) event location(s), and (arbitrarily fine-grained) event date(s), our approach can be divided in the following six steps. 1) Via the textual search APIs (Application Programming Interfaces) of different social networks, we retrieve a list of potentially event-relevant microposts that either contain media items directly, or that provide links to media items on external media item hosting platforms. 2) Using third-party Natural Language Processing (NLP) tools, we recognize and disambiguate named entities in microposts to predetermine their relevance. 3) We extract the binary media item data from social networks or media item hosting platforms and relate it to the originating microposts. 4) Using CBIR and CBVR techniques, we first deduplicate exact-duplicate and near-duplicate media items and then cluster similar media items. 5) We rank the deduplicated and clustered list of media items and their related microposts according to well-defined ranking criteria. 6) In order to generate interactive and user-customizable media galleries that visually and audially summarize the event in question, we compile the top-n ranked media items and microposts in aesthetically pleasing and functional ways

Building and exploiting context on the web

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