6 research outputs found

    Collecte orientée sur le Web pour la recherche d'information spécialisée

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    Les moteurs de recherche verticaux, qui se concentrent sur des segments spĂ©cifiques du Web, deviennent aujourd'hui de plus en plus prĂ©sents dans le paysage d'Internet. Les moteurs de recherche thĂ©matiques, notamment, peuvent obtenir de trĂšs bonnes performances en limitant le corpus indexĂ© Ă  un thĂšme connu. Les ambiguĂŻtĂ©s de la langue sont alors d'autant plus contrĂŽlables que le domaine est bien ciblĂ©. De plus, la connaissance des objets et de leurs propriĂ©tĂ©s rend possible le dĂ©veloppement de techniques d'analyse spĂ©cifiques afin d'extraire des informations pertinentes.Dans le cadre de cette thĂšse, nous nous intĂ©ressons plus prĂ©cisĂ©ment Ă  la procĂ©dure de collecte de documents thĂ©matiques Ă  partir du Web pour alimenter un moteur de recherche thĂ©matique. La procĂ©dure de collecte peut ĂȘtre rĂ©alisĂ©e en s'appuyant sur un moteur de recherche gĂ©nĂ©raliste existant (recherche orientĂ©e) ou en parcourant les hyperliens entre les pages Web (exploration orientĂ©e).Nous Ă©tudions tout d'abord la recherche orientĂ©e. Dans ce contexte, l'approche classique consiste Ă  combiner des mot-clĂ©s du domaine d'intĂ©rĂȘt, Ă  les soumettre Ă  un moteur de recherche et Ă  tĂ©lĂ©charger les meilleurs rĂ©sultats retournĂ©s par ce dernier.AprĂšs avoir Ă©valuĂ© empiriquement cette approche sur 340 thĂšmes issus de l'OpenDirectory, nous proposons de l'amĂ©liorer en deux points. En amont du moteur de recherche, nous proposons de formuler des requĂȘtes thĂ©matiques plus pertinentes pour le thĂšme afin d'augmenter la prĂ©cision de la collecte. Nous dĂ©finissons une mĂ©trique fondĂ©e sur un graphe de cooccurrences et un algorithme de marche alĂ©atoire, dans le but de prĂ©dire la pertinence d'une requĂȘte thĂ©matique. En aval du moteur de recherche, nous proposons de filtrer les documents tĂ©lĂ©chargĂ©s afin d'amĂ©liorer la qualitĂ© du corpus produit. Pour ce faire, nous modĂ©lisons la procĂ©dure de collecte sous la forme d'un graphe triparti et appliquons un algorithme de marche alĂ©atoire biaisĂ© afin d'ordonner par pertinence les documents et termes apparaissant dans ces derniers.Dans la seconde partie de cette thĂšse, nous nous focalisons sur l'exploration orientĂ©e du Web. Au coeur de tout robot d'exploration orientĂ©e se trouve une stratĂ©gie de crawl qui lui permet de maximiser le rapatriement de pages pertinentes pour un thĂšme, tout en minimisant le nombre de pages visitĂ©es qui ne sont pas en rapport avec le thĂšme. En pratique, cette stratĂ©gie dĂ©finit l'ordre de visite des pages. Nous proposons d'apprendre automatiquement une fonction d'ordonnancement indĂ©pendante du thĂšme Ă  partir de donnĂ©es existantes annotĂ©es automatiquement.Vertical search engines, which focus on a specific segment of the Web, become more and more present in the Internet landscape. Topical search engines, notably, can obtain a significant performance boost by limiting their index on a specific topic. By doing so, language ambiguities are reduced, and both the algorithms and the user interface can take advantage of domain knowledge, such as domain objects or characteristics, to satisfy user information needs.In this thesis, we tackle the first inevitable step of a all topical search engine : focused document gathering from the Web. A thorough study of the state of art leads us to consider two strategies to gather topical documents from the Web: either relying on an existing search engine index (focused search) or directly crawling the Web (focused crawling).The first part of our research has been dedicated to focused search. In this context, a standard approach consists in combining domain-specific terms into queries, submitting those queries to a search engine and down- loading top ranked documents. After empirically evaluating this approach over 340 topics, we propose to enhance it in two different ways: Upstream of the search engine, we aim at formulating more relevant queries in or- der to increase the precision of the top retrieved documents. To do so, we define a metric based on a co-occurrence graph and a random walk algorithm, which aims at predicting the topical relevance of a query. Downstream of the search engine, we filter the retrieved documents in order to improve the document collection quality. We do so by modeling our gathering process as a tripartite graph and applying a random walk with restart algorithm so as to simultaneously order by relevance the documents and terms appearing in our corpus.In the second part of this thesis, we turn to focused crawling. We describe our focused crawler implementation that was designed to scale horizontally. Then, we consider the problem of crawl frontier ordering, which is at the very heart of a focused crawler. Such ordering strategy allows the crawler to prioritize its fetches, maximizing the number of in-domain documents retrieved while minimizing the non relevant ones. We propose to apply learning to rank algorithms to efficiently order the crawl frontier, and define a method to learn a ranking function from existing crawls.PARIS11-SCD-Bib. Ă©lectronique (914719901) / SudocSudocFranceF

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

    Toponym Resolution in Text

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    Institute for Communicating and Collaborative SystemsBackground. In the area of Geographic Information Systems (GIS), a shared discipline between informatics and geography, the term geo-parsing is used to describe the process of identifying names in text, which in computational linguistics is known as named entity recognition and classification (NERC). The term geo-coding is used for the task of mapping from implicitly geo-referenced datasets (such as structured address records) to explicitly geo-referenced representations (e.g., using latitude and longitude). However, present-day GIS systems provide no automatic geo-coding functionality for unstructured text. In Information Extraction (IE), processing of named entities in text has traditionally been seen as a two-step process comprising a flat text span recognition sub-task and an atomic classification sub-task; relating the text span to a model of the world has been ignored by evaluations such as MUC or ACE (Chinchor (1998); U.S. NIST (2003)). However, spatial and temporal expressions refer to events in space-time, and the grounding of events is a precondition for accurate reasoning. Thus, automatic grounding can improve many applications such as automatic map drawing (e.g. for choosing a focus) and question answering (e.g. , for questions like How far is London from Edinburgh?, given a story in which both occur and can be resolved). Whereas temporal grounding has received considerable attention in the recent past (Mani and Wilson (2000); Setzer (2001)), robust spatial grounding has long been neglected. Concentrating on geographic names for populated places, I define the task of automatic Toponym Resolution (TR) as computing the mapping from occurrences of names for places as found in a text to a representation of the extensional semantics of the location referred to (its referent), such as a geographic latitude/longitude footprint. The task of mapping from names to locations is hard due to insufficient and noisy databases, and a large degree of ambiguity: common words need to be distinguished from proper names (geo/non-geo ambiguity), and the mapping between names and locations is ambiguous (London can refer to the capital of the UK or to London, Ontario, Canada, or to about forty other Londons on earth). In addition, names of places and the boundaries referred to change over time, and databases are incomplete. Objective. I investigate how referentially ambiguous spatial named entities can be grounded, or resolved, with respect to an extensional coordinate model robustly on open-domain news text. I begin by comparing the few algorithms proposed in the literature, and, comparing semiformal, reconstructed descriptions of them, I factor out a shared repertoire of linguistic heuristics (e.g. rules, patterns) and extra-linguistic knowledge sources (e.g. population sizes). I then investigate how to combine these sources of evidence to obtain a superior method. I also investigate the noise effect introduced by the named entity tagging step that toponym resolution relies on in a sequential system pipeline architecture. Scope. In this thesis, I investigate a present-day snapshot of terrestrial geography as represented in the gazetteer defined and, accordingly, a collection of present-day news text. I limit the investigation to populated places; geo-coding of artifact names (e.g. airports or bridges), compositional geographic descriptions (e.g. 40 miles SW of London, near Berlin), for instance, is not attempted. Historic change is a major factor affecting gazetteer construction and ultimately toponym resolution. However, this is beyond the scope of this thesis. Method. While a small number of previous attempts have been made to solve the toponym resolution problem, these were either not evaluated, or evaluation was done by manual inspection of system output instead of curating a reusable reference corpus. Since the relevant literature is scattered across several disciplines (GIS, digital libraries, information retrieval, natural language processing) and descriptions of algorithms are mostly given in informal prose, I attempt to systematically describe them and aim at a reconstruction in a uniform, semi-formal pseudo-code notation for easier re-implementation. A systematic comparison leads to an inventory of heuristics and other sources of evidence. In order to carry out a comparative evaluation procedure, an evaluation resource is required. Unfortunately, to date no gold standard has been curated in the research community. To this end, a reference gazetteer and an associated novel reference corpus with human-labeled referent annotation are created. These are subsequently used to benchmark a selection of the reconstructed algorithms and a novel re-combination of the heuristics catalogued in the inventory. I then compare the performance of the same TR algorithms under three different conditions, namely applying it to the (i) output of human named entity annotation, (ii) automatic annotation using an existing Maximum Entropy sequence tagging model, and (iii) a našıve toponym lookup procedure in a gazetteer. Evaluation. The algorithms implemented in this thesis are evaluated in an intrinsic or component evaluation. To this end, we define a task-specific matching criterion to be used with traditional Precision (P) and Recall (R) evaluation metrics. This matching criterion is lenient with respect to numerical gazetteer imprecision in situations where one toponym instance is marked up with different gazetteer entries in the gold standard and the test set, respectively, but where these refer to the same candidate referent, caused by multiple near-duplicate entries in the reference gazetteer. Main Contributions. The major contributions of this thesis are as follows: ‱ A new reference corpus in which instances of location named entities have been manually annotated with spatial grounding information for populated places, and an associated reference gazetteer, from which the assigned candidate referents are chosen. This reference gazetteer provides numerical latitude/longitude coordinates (such as 51320 North, 0 50 West) as well as hierarchical path descriptions (such as London > UK) with respect to a world wide-coverage, geographic taxonomy constructed by combining several large, but noisy gazetteers. This corpus contains news stories and comprises two sub-corpora, a subset of the REUTERS RCV1 news corpus used for the CoNLL shared task (Tjong Kim Sang and De Meulder (2003)), and a subset of the Fourth Message Understanding Contest (MUC-4; Chinchor (1995)), both available pre-annotated with gold-standard. This corpus will be made available as a reference evaluation resource; ‱ a new method and implemented system to resolve toponyms that is capable of robustly processing unseen text (open-domain online newswire text) and grounding toponym instances in an extensional model using longitude and latitude coordinates and hierarchical path descriptions, using internal (textual) and external (gazetteer) evidence; ‱ an empirical analysis of the relative utility of various heuristic biases and other sources of evidence with respect to the toponym resolution task when analysing free news genre text; ‱ a comparison between a replicated method as described in the literature, which functions as a baseline, and a novel algorithm based on minimality heuristics; and ‱ several exemplary prototypical applications to show how the resulting toponym resolution methods can be used to create visual surrogates for news stories, a geographic exploration tool for news browsing, geographically-aware document retrieval and to answer spatial questions (How far...?) in an open-domain question answering system. These applications only have demonstrative character, as a thorough quantitative, task-based (extrinsic) evaluation of the utility of automatic toponym resolution is beyond the scope of this thesis and left for future work

    Exploiting tag information for search and personalization

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