Vignette d'image

Naji, Nada

Nom
Naji, Nada
Affiliation principale
Identifiants
https://libra.unine.ch/handle/123456789/292

Résultat de la recherche

Filtres

21
1
2
11
Réinitialiser les filtres

Paramètres

Voici les éléments 1 - 2 sur 2
  • Publication
    Accès libre
    Information retrieval of digitized medieval manuscripts
    (2013)
    Naji, Nada 
    ;
    Savoy, Jacques 
    This dissertation investigates the retrieval of noisy texts in general and digitized historical manuscripts in particular. The noise originates from several sources, these include imperfect text recognition (6% word error rate), spelling variation, non-standardized grammar, in addition to user-side confusion due to her/his limited knowledge of the underlying language and/or the searched text. Manual correction or normalization are very time-consuming and resource-demanding tasks and are thus out of the question. Furthermore, external resources, such as thesauri, are not available for the older, lesser-known languages. In this dissertation, we present our contributions to overcoming or at least coping with these issues. We developed several methods that provide a low-cost yet highly-effective text representation to limit the negative impact of recognition error and the variable orthography and morphology. Finally, to account for the user-confusion problem, we developed a low-cost query enrichment function which we deem indispensable for the challenging task of one-word queries.
  • Publication
    Accès libre
    Information Retrieval Strategies for Digitized Handwritten Medieval Documents
    Naji, Nada 
    ;
    Savoy, Jacques 
    This paper describes and evaluates different IR models and search strategies for digitized manuscripts. Written during the thirteenth century, these manuscripts were digitized using an imperfect recognition system with a word error rate of around 6%. Having access to the internal representation during the recognition stage, we were able to produce four automatic transcriptions, each introducing some form of spelling correction as an attempt to improve the retrieval effectiveness. We evaluated the retrieval effectiveness for each of these versions using three text representations combined with five IR models, three stemming strategies and two query formulations. We employed a manually-transcribed error-free version to define the ground-truth. Based on our experiments, we conclude that taking account of the single best recognition word or all possible top-k recognition alternatives does not provide the best performance. Selecting all possible words each having a log-likelihood close to the best alternative yields the best text surrogate. Within this representation, different retrieval strategies tend to produce similar performance levels.