18,320 research outputs found
InfoLink: analysis of Dutch broadcast news and cross-media browsing
In this paper, a cross-media browsing demonstrator named InfoLink is described. InfoLink automatically links the content of Dutch broadcast news videos to related information sources in parallel collections containing text and/or video. Automatic segmentation, speech recognition and available meta-data are used to index and link items. The concept is visualised using SMIL-scripts for presenting the streaming broadcast news video and the information links
On the Feasibility of Automated Detection of Allusive Text Reuse
The detection of allusive text reuse is particularly challenging due to the
sparse evidence on which allusive references rely---commonly based on none or
very few shared words. Arguably, lexical semantics can be resorted to since
uncovering semantic relations between words has the potential to increase the
support underlying the allusion and alleviate the lexical sparsity. A further
obstacle is the lack of evaluation benchmark corpora, largely due to the highly
interpretative character of the annotation process. In the present paper, we
aim to elucidate the feasibility of automated allusion detection. We approach
the matter from an Information Retrieval perspective in which referencing texts
act as queries and referenced texts as relevant documents to be retrieved, and
estimate the difficulty of benchmark corpus compilation by a novel
inter-annotator agreement study on query segmentation. Furthermore, we
investigate to what extent the integration of lexical semantic information
derived from distributional models and ontologies can aid retrieving cases of
allusive reuse. The results show that (i) despite low agreement scores, using
manual queries considerably improves retrieval performance with respect to a
windowing approach, and that (ii) retrieval performance can be moderately
boosted with distributional semantics
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
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