Subjectivity Detection through Socio-Linguistic Features

Social media platforms have opened new dimensions within the information retrieval domain leading to a novel concept known as Social Information Retrieval. We argue that the concept of Social Information Retrieval can be extended by augmenting the huge amount of content on the traditional Web with the ever-growing rich Social Web content to increase the information richness of today’s search engines. This paper proposes a subjectivity detection framework which can lead towards a proposed emotion-aware search engine interface. Our proposed method differs from previous subjectivity analysis approaches in that it is the first method that takes into account social features of social media platforms for the subjectivity classification task. Through experimental evaluations, we observe the accuracy of the proposed method to be 86.21% which demonstrates a promising outcome for large-scale application of our proposed subjectivity analysis technique

A Method for the Automated, Reliable Retrieval of Publication-Citation Records

BACKGROUND: Publication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices. METHODOLOGY: In this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of 98% and specificity of 72% (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value 10%, compared to raw search results which have overestimates of, on average, 75%. CONCLUSIONS: These results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Distributed resource discovery using a context sensitive infrastructure

Distributed Resource Discovery in a World Wide Web environment using full-text indices will never scale. The distinct properties of WWW information (volume, rate of change, topical diversity) limits the scaleability of traditional approaches to distributed Resource Discovery. An approach combining metadata clustering and query routing can, on the other hand, be proven to scale much better. This paper presents the Content-Sensitive Infrastructure, which is a design building on these results. We also present an analytical framework for comparing scaleability of different distribution strategies

CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference

The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world

Query-driven indexing in large-scale distributed systems

Efficient and effective search in large-scale data repositories requires complex indexing solutions deployed on a large number of servers. Web search engines such as Google and Yahoo! already rely upon complex systems to be able to return relevant query results and keep processing times within the comfortable sub-second limit. Nevertheless, the exponential growth of the amount of content on the Web poses serious challenges with respect to scalability. Coping with these challenges requires novel indexing solutions that not only remain scalable but also preserve the search accuracy. In this thesis we introduce and explore the concept of query-driven indexing – an index construction strategy that uses caching techniques to adapt to the querying patterns expressed by users. We suggest to abandon the strict difference between indexing and caching, and to build a distributed indexing structure, or a distributed cache, such that it is optimized for the current query load. Our experimental and theoretical analysis shows that employing query-driven indexing is especially beneficial when the content is (geographically) distributed in a Peer-to-Peer network. In such a setting extensive bandwidth consumption has been identified as one of the major obstacles for efficient large-scale search. Our indexing mechanisms combat this problem by maintaining the query popularity statistics and by indexing (caching) intermediate query results that are requested frequently. We present several indexing strategies for processing multi-keyword and XPath queries over distributed collections of textual and XML documents respectively. Experimental evaluations show significant overall traffic reduction compared to the state-of-the-art approaches. We also study possible query-driven optimizations for Web search engine architectures. Contrary to the Peer-to-Peer setting, Web search engines use centralized caching of query results to reduce the processing load on the main index. We analyze real search engine query logs and show that the changes in query traffic that such a results cache induces fundamentally affect indexing performance. In particular, we study its impact on index pruning efficiency. We show that combination of both techniques enables efficient reduction of the query processing costs and thus is practical to use in Web search engines