2,959 research outputs found
Towards using web-crawled data for domain adaptation in statistical machine translation
This paper reports on the ongoing work focused on domain adaptation of statistical machine translation using domain-speciïŹc data obtained by domain-focused web crawling. We present a strategy for crawling monolingual and parallel data and their exploitation for testing, language modelling, and system tuning in a phrase--based machine translation framework. The proposed approach is evaluated on the domains of Natural Environment and Labour Legislation and two language
pairs: EnglishâFrench and EnglishâGreek
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
Exploiting Social Annotation for Automatic Resource Discovery
Information integration applications, such as mediators or mashups, that
require access to information resources currently rely on users manually
discovering and integrating them in the application. Manual resource discovery
is a slow process, requiring the user to sift through results obtained via
keyword-based search. Although search methods have advanced to include evidence
from document contents, its metadata and the contents and link structure of the
referring pages, they still do not adequately cover information sources --
often called ``the hidden Web''-- that dynamically generate documents in
response to a query. The recently popular social bookmarking sites, which allow
users to annotate and share metadata about various information sources, provide
rich evidence for resource discovery. In this paper, we describe a
probabilistic model of the user annotation process in a social bookmarking
system del.icio.us. We then use the model to automatically find resources
relevant to a particular information domain. Our experimental results on data
obtained from \emph{del.icio.us} show this approach as a promising method for
helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on
the We
iCrawl: Improving the Freshness of Web Collections by Integrating Social Web and Focused Web Crawling
Researchers in the Digital Humanities and journalists need to monitor,
collect and analyze fresh online content regarding current events such as the
Ebola outbreak or the Ukraine crisis on demand. However, existing focused
crawling approaches only consider topical aspects while ignoring temporal
aspects and therefore cannot achieve thematically coherent and fresh Web
collections. Especially Social Media provide a rich source of fresh content,
which is not used by state-of-the-art focused crawlers. In this paper we
address the issues of enabling the collection of fresh and relevant Web and
Social Web content for a topic of interest through seamless integration of Web
and Social Media in a novel integrated focused crawler. The crawler collects
Web and Social Media content in a single system and exploits the stream of
fresh Social Media content for guiding the crawler.Comment: Published in the Proceedings of the 15th ACM/IEEE-CS Joint Conference
on Digital Libraries 201
Methodologies for the Automatic Location of Academic and Educational Texts on the Internet
Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as âappropriateâ to a given database, a problem only solved by complex text content analysis.
This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined
Methodologies for the Automatic Location of Academic and Educational Texts on the Internet
Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as âappropriateâ to a given database, a problem only solved by complex text content analysis.
This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined
Community Detection on Evolving Graphs
Clustering is a fundamental step in many information-retrieval and data-mining applications. Detecting clusters in graphs is also a key tool for finding the community structure in social and behavioral networks. In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph. Furthermore, there are often limitations on the frequency of such probes, either imposed explicitly by the online platform (e.g., in the case of crawling proprietary social networks like twitter) or implicitly because of resource limitations (e.g., in the case of crawling the web). In this paper, we study a model of clustering on evolving graphs that captures this aspect of the problem. Our model is based on the classical stochastic block model, which has been used to assess rigorously the quality of various static clustering methods. In our model, the algorithm is supposed to reconstruct the planted clustering, given the ability to query for small pieces of local information about the graph, at a limited rate. We design and analyze clustering algorithms that work in this model, and show asymptotically tight upper and lower bounds on their accuracy. Finally, we perform simulations, which demonstrate that our main asymptotic results hold true also in practice
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