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Semantic document annotation may be useful for many tasks. In particular, in the framework of the MDM project(, topical annotation -- i.e. the annotation of document segments with tags identifying the topics discussed in the segments -- is used to enhance the retrieval of multimodal meeting records. Indeed, with such an annotation, meeting retrieval can integrate topics in the search criteria offered to the users. Contrarily to standard approaches to topic annotation, the technique used in this work does not centraly rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary( -- that provides a concept hierarchy based on hyponym and hypernym relations.This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts preserving the document's content. The identification of the topically homogeneous segments -- often called Text Tiling -- is performed to ease the computation as the algorithm will work on smaller text fragments. In addition, it is believed to improve the precision of the extraction as it is performed on topically homogeneous segments. For this task, a standard techniques -- proposed by \cite{richmond97detecting} -- relying on similarity computation based on vector space representations have been implemented. Hence, the main challenge in the project was to create a novel topic identification algorithm, based on the available semantic resource, that produces good results when applied on the automatically generated segments. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most interesting set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concept that are not directly present in the document. The segmentation algorithm was evaluated on the Reuters corpus, composed of 806'791 news items. These items were aggregated to construct a virtual document where the algorithm had to detect boundaries. These automatically generated segments were then compared to the initial news items and a metric has been developed to evaluate the accuracy of the algorithm. The proposed approach for topic extraction was experimentally tested and evaluated on a database of 238 documents corresponding to bibliographic descriptions extracted from the INSPEC database( A novel evaluation metric was designed to take into account the fact that the topics associated with the INSPEC descriptions -- taken as the golden truth for the evaluation -- were not directly produced based on the EDR dictionary, and therefore needed to be approximated by the available entries. Alltogether, the combination of existing document segmentation methods -- i.e text tiling -- with novel topic identification ones leads to an additional document annotation useful for more robust retrieval.