3,034 research outputs found
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
ATLAS: A flexible and extensible architecture for linguistic annotation
We describe a formal model for annotating linguistic artifacts, from which we
derive an application programming interface (API) to a suite of tools for
manipulating these annotations. The abstract logical model provides for a range
of storage formats and promotes the reuse of tools that interact through this
API. We focus first on ``Annotation Graphs,'' a graph model for annotations on
linear signals (such as text and speech) indexed by intervals, for which
efficient database storage and querying techniques are applicable. We note how
a wide range of existing annotated corpora can be mapped to this annotation
graph model. This model is then generalized to encompass a wider variety of
linguistic ``signals,'' including both naturally occuring phenomena (as
recorded in images, video, multi-modal interactions, etc.), as well as the
derived resources that are increasingly important to the engineering of natural
language processing systems (such as word lists, dictionaries, aligned
bilingual corpora, etc.). We conclude with a review of the current efforts
towards implementing key pieces of this architecture.Comment: 8 pages, 9 figure
Video summarisation: A conceptual framework and survey of the state of the art
This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
With the abundant amount of available online and offline text data, there
arises a crucial need to extract the relation between phrases and summarize the
main content of each document in a few words. For this purpose, there have been
many studies recently in Open Information Extraction (OIE). OIE improves upon
relation extraction techniques by analyzing relations across different domains
and avoids requiring hand-labeling pre-specified relations in sentences. This
paper surveys recent approaches of OIE and its applications on Knowledge Graph
(KG), text summarization, and Question Answering (QA). Moreover, the paper
describes OIE basis methods in relation extraction. It briefly discusses the
main approaches and the pros and cons of each method. Finally, it gives an
overview about challenges, open issues, and future work opportunities for OIE,
relation extraction, and OIE applications.Comment: 15 pages, 9 figure
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