61,093 research outputs found
A survey on the use of relevance feedback for information access systems
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems
Artefacts and Errors: Acknowledging Issues of Representation in the Digital: Imaging of Ancient Texts
It is assumed, in palaeography, papyrology and epigraphy, that a certain amount of
uncertainty is inherent in the reading of damaged and abraded texts. Yet we have
not really grappled with the fact that, nowadays, as many scholars tend to deal with
digital images of texts, rather than handling the texts themselves, the procedures for
creating digital images of texts can insert further uncertainty into the representation
of the text created. Technical distortions can lead to the unintentional introduction
of âartefactsâ into images, which can have an effect on the resulting representation. If
we cannot trust our digital surrogates of texts, can we trust the readings from them?
How do scholars acknowledge the quality of digitised images of texts? Furthermore,
this leads us to the type of discussions of representation that have been present in
Classical texts since Plato: digitisation can be considered as an alternative form of
representation, bringing to the modern debate of the use of digital technology in Classics
the familiar theories of mimesis (imitation) and ekphrasis (description): the conversion
of visual evidence into explicit descriptions of that information, stored in computer
files in distinct linguistic terms, with all the difficulties of conversion understood in the
ekphratic process. The community has not yet considered what becoming dependent
on digital texts means for the field, both in practical and theoretical terms. Issues of
quality, copying, representation, and substance should be part of our dialogue when
we consult digital surrogates of documentary material, yet we are just constructing
understandings of what it means to rely on virtual representations of artefacts. It is
necessary to relate our understandings of uncertainty in palaeography and epigraphy
to our understanding of the mechanics of visualization employed by digital imaging
techniques, if we are to fully understand the impact that these will have
TRECVID: evaluating the effectiveness of information retrieval tasks on digital video
TRECVID is an annual exercise which encourages research in information retrieval from digital video by providing a large video test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of some semantic features, and the automatic segmentation of TV news broadcasts into non-overlapping news stories. TRECVID has a broad range of over 40 participating groups from across the world and as it is now (2004) in its 4th annual cycle it is opportune to stand back and look at the lessons we have learned from the cumulative activity. In this paper we shall present a brief and high-level overview of the TRECVID activity covering the data, the benchmarked tasks, the overall results obtained by groups to date and an overview of the approaches taken by selective groups in some tasks. While progress from one year to the next cannot be measured directly because of the changing nature of the video data we have been using, we shall present a summary of the lessons we have learned from TRECVID and include some pointers on what we feel are the most important of these lessons
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
Improving Search through A3C Reinforcement Learning based Conversational Agent
We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.Comment: 17 pages, 7 figure
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Collaborative yet independent: Information practices in the physical sciences
In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data.
This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research.
Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
Living Knowledge
Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following
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