Contextual correlates of synonymy
Communications of the ACM
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to rank with (a lot of) word features
Information Retrieval
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
WSABIE: scaling up to large vocabulary image annotation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
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We propose methods for computing semantic relatedness between words or texts by using knowledge from hypertext encyclopedias such as Wikipedia. A network of concepts is built by filtering the encyclopedia's articles, each concept corresponding to an article. A random walk model based on the notion of Visiting Probability (VP) is employed to compute the distance between nodes, and then between sets of nodes. To transfer learning from the network of concepts to text analysis tasks, we develop two common representation approaches. In the first approach, the shared representation space is the set of concepts in the network and every text is represented in this space. In the second approach, a latent space is used as the shared representation, and a transformation from words to the latent space is trained over VP scores. We applied our methods to four important tasks in natural language processing: word similarity, document similarity, document clustering and classification, and ranking in information retrieval. The performance is state-of-the-art or close to it for each task, thus demonstrating the generality of the proposed knowledge resource and the associated methods.