Motivations for Participation in Socially Networked Collective Intelligence Systems

One of the most significant challenges facing systems of collective intelligence is how to encourage participation on the scale required to produce high quality data. This paper details ongoing work with Phrase Detectives, an online game-with-a-purpose deployed on Facebook, and investigates user motivations for participation in social network gaming where the wisdom of crowds produces useful data.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag

Creating language resources for under-resourced languages: methodologies, and experiments with Arabic

Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented

Arabic multi-document text summarisation

Multi-document summarisation is the process of producing a single summary of a collection of related documents. Much of the current work on multi-document text summarisation is concerned with the English language; relevant resources are numerous and readily available. These resources include human generated (gold-standard) and automatic summaries. Arabic multi-document summarisation is still in its infancy. One of the obstacles to progress is the limited availability of Arabic resources to support this research. When we started our research there were no publicly available Arabic multi-document gold-standard summaries, which are needed to automatically evaluate system generated summaries. The Document Understanding Conference (DUC) and Text Analysis Conference (TAC) at that time provided resources such as gold-standard extractive and abstractive summaries (both human and system generated) that were only available in English. Our aim was to push forward the state-of-the-art in Arabic multi-document summarisation. This required advancements in at least two areas. The first area was the creation of Arabic test collections. The second area was concerned with the actual summarisation process to find methods that improve the quality of Arabic summaries. To address both points we created single and multi-document Arabic test collections both automatically and manually using a commonly used English dataset and by having human participants. We developed extractive language dependent and language independent single and multi-document summarisers, both for Arabic and English. In our work we provided state-of-the-art approaches for Arabic multi-document summarisation. We succeeded in including Arabic in one of the leading summarisation conferences the Text Analysis Conference (TAC). Researchers on Arabic multi-document summarisation now have resources and tools that can be used to advance the research in this field