59,508 research outputs found
Learning to Retrieve Videos by Asking Questions
The majority of traditional text-to-video retrieval systems operate in static
environments, i.e., there is no interaction between the user and the agent
beyond the initial textual query provided by the user. This can be sub-optimal
if the initial query has ambiguities, which would lead to many falsely
retrieved videos. To overcome this limitation, we propose a novel framework for
Video Retrieval using Dialog (ViReD), which enables the user to interact with
an AI agent via multiple rounds of dialog, where the user refines retrieved
results by answering questions generated by an AI agent. Our novel multimodal
question generator learns to ask questions that maximize the subsequent video
retrieval performance using (i) the video candidates retrieved during the last
round of interaction with the user and (ii) the text-based dialog history
documenting all previous interactions, to generate questions that incorporate
both visual and linguistic cues relevant to video retrieval. Furthermore, to
generate maximally informative questions, we propose an Information-Guided
Supervision (IGS), which guides the question generator to ask questions that
would boost subsequent video retrieval accuracy. We validate the effectiveness
of our interactive ViReD framework on the AVSD dataset, showing that our
interactive method performs significantly better than traditional
non-interactive video retrieval systems. We also demonstrate that our proposed
approach generalizes to the real-world settings that involve interactions with
real humans, thus, demonstrating the robustness and generality of our framewor
Language-based multimedia information retrieval
This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material by use of human language technologies. Thus, in contrast to image or sound-based retrieval methods, where both the query language and the indexing methods build on non-linguistic data, these methods attempt to exploit advanced text retrieval technologies for the retrieval of non-textual material. While POP-EYE was building on subtitles or captions as the prime language key for disclosing video fragments, OLIVE is making use of speech recognition to automatically derive transcriptions of the sound tracks, generating time-coded linguistic elements which then serve as the basis for text-based retrieval functionality
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Automated speech and audio analysis for semantic access to multimedia
The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives
ON MONITORING LANGUAGE CHANGE WITH THE SUPPORT OF CORPUS PROCESSING
One of the fundamental characteristics of language is that it can change over time. One
method to monitor the change is by observing its corpora: a structured language
documentation. Recent development in technology, especially in the field of Natural
Language Processing allows robust linguistic processing, which support the description of
diverse historical changes of the corpora. The interference of human linguist is inevitable as
it determines the gold standard, but computer assistance provides considerable support by
incorporating computational approach in exploring the corpora, especially historical
corpora. This paper proposes a model for corpus development, where corpus are annotated
to support further computational operations such as lexicogrammatical pattern matching,
automatic retrieval and extraction. The corpus processing operations are performed by local
grammar based corpus processing software on a contemporary Indonesian corpus. This
paper concludes that data collection and data processing in a corpus are equally crucial
importance to monitor language change, and none can be set aside
Report of MIRACLE team for Geographical IR in CLEF 2006
The main objective of the designed experiments is testing the effects of geographical information retrieval from documents that contain geographical tags. In the designed experiments we try to isolate geographical retrieval from textual retrieval replacing all geo-entity textual references from topics with associated tags and splitting the retrieval process in two phases: textual retrieval from the textual part of the topic without geo-entity references and geographical retrieval from the tagged text generated by the topic tagger. Textual and geographical results are combined applying different techniques: union, intersection, difference, and external join based. Our geographic information retrieval system consists of a set of basics components organized in two categories: (i) linguistic tools oriented to textual analysis and retrieval and (ii) resources and tools oriented to geographical analysis. These tools are combined to carry out the different phases of the system: (i) documents and topics analysis, (ii) relevant documents retrieval and (iii) result combination. If we compare the results achieved to the last campaignâs results, we can assert that mean average precision gets worse when the textual geo-entity references are replaced with geographical tags. Part of this worsening is due to our experiments return cero pertinent documents if no documents satisfy de geographical sub-query. But if we only analyze the results of queries that satisfied both textual and geographical terms, we observe that the designed experiments recover pertinent documents quickly, improving R-Precision values. We conclude that the developed geographical information retrieval system is very sensible to textual georeference and therefore it is necessary to improve the name entity recognition module
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
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
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