Understanding intent underlying search query recently attracted enormous research interests. Two challenging issues are worth noting: First, words within query are usually ambiguous while query in most cases is too short to disambiguate. Second, ambiguity in some cases cannot be resolved according merely to the limited query context. It is thus demanded that the ambiguity be resolved/analyzed w...

Can we build a semantically adaptive personal learning environment that helps people learn mathematics and that meets reasonable criteria for sustainable growth and development? This is question that applies at the interface between participatory social media and interactive, adaptive, “knowledge media”. Ten years ago, no one had heard of Wikipedia. Perhaps in another ten, P2PU will be as popul...

Services such as Wikipedia, Flickr, Technorati, Yahoo!, YouTube, del.icio.us appear in many web applications, employ the folksonomy as their social tagging mechanism, where users assign tags to resources and share it with each other within their community. As the number of the folksonomy-based systems is increased, some proper data mining approaches to folksonomies are necessary to better under...

In this paper we present an unsupervised approach to Query Classification. The approach exploits the Wikipedia encyclopedia as a corpus and the statistical distribution of terms, from both the category labels and the query, in order to select an appropriate category. We have created a classifier that works with 55 categories extracted from the search section of the Bridgeman Art Library website...

Wikipedia’s category graph is a network of 400,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, we present a new algorithm to efficiently explore this graph and discover accurate classification labels. We implement our algorithm as the core of a query classi...

Most text mining tasks, including clustering and topic detection, are based on statistical methods that treat text as bags of words. Semantics in the text is largely ignored in the mining process, and mining results often have low interpretability. One particular challenge faced by such approaches lies in short text understanding, as short texts lack enough content from which statistical conclu...

Mining suggestion expressing sentences from a given text is a less investigated sentence classification task, and therefore lacks hand labeled benchmark datasets. In this work, we propose and evaluate two approaches for distant supervision in suggestion mining. The distant supervision is obtained through a large silver standard dataset, constructed using the text from wikiHow and Wikipedia. Bot...

Traditional paper-based maps are still superior in several ways to their digital counterparts used on mobile devices. Namely, paper-based maps provide high-resolution, largescale information with zero power consumption. Digital maps offer personalized and dynamic information, but suffer from small outer scales and low resolutions. In this paper, we present WikEye, an interdisciplinary project t...

The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates the use of statistical translation systems, which are unfortunately dependent o...