Advances in automatic speech recognition allow us to search large speech collections using traditional information retrieval methods. The problem of "aboutness" for documents --- is a document about a certain concept --- has been at the core of document indexing for the entire history of IR. This problem is more difficult for speech indexing since automatic speech transcriptions often contain mistakes. In this study we show that document expansion can be successfully used to alleviate the effect of transcription mistakes on speech retrieval. The loss

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Research in content-based 3D retrieval has already started, and several approaches have been proposed which use in different manner a similarity assessment to match the shape of the query against the shape of the objects in the database. However, the success of these solutions are far from the success obtained by their textual counterparts. A major drawback of most existing 3D retrieval solutions is their inability to support partial queries, that is, a query which does not need to be formulated by specifying a whole query shape, but just a part of it, for example a detail of its overall shape, just like documents are retrieved by specifying words and not whole texts. Recently, researchers have focused their investigation on 3D retrieval which is solved by partial shape matching. However, at the extent of our knowledge, there is still no 3D search engine that provides an indexing of the 3D models based on all the interesting subparts of the models. In this paper we present a novel approach to 3D shape retrieval that uses a collection-aware shape decomposition combined with a shape thesaurus and inverted indexes to describe and retrieve 3D models using part-in-whole matching. The proposed method clusters similar segments obtained trough a multilevel decomposition of models, constructing from such partition the shape thesaurus. Then, to retrieve a model containing a sub-part similar to a given query, instead of looking on

Keyphrases are short phrases that reflect the main topic of a document. Because manually annotating documents with keyphrases is a time-consuming process, several automatic approaches have been developed. Typically, candidate phrases are extracted using features such as position or frequency in the document text. Document structure may contain useful information about which parts or phrases of a document are important, but has rarely been considered as a source of information for keyphrase extraction. We address this issue in the context of keyphrase extraction from scientific literature. We introduce a new, large corpus that consists of full-text journal articles, where the rich collection and document structure available at the publishing stage is explicitly annotated. We explore features based on the XML tags contained in the documents, and based on generic section types derived using position and cue words in section titles. For XML tags we find sections, abstract, and title to perform best, but many smaller elements may be beneficial in combination with other features. Of the generic section types, the discussion section is found to be the most useful for keyphrase extraction.

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this paper, IR will imply text-based retrieval unless explicitly stated otherwise.