This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks.This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced. r

This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task. Task DescriptionKeyphrases 1 are words that capture the main topics of a document. As they represent these key ideas, extracting high-quality keyphrases can benefit various natural language processing (NLP) applications such as summarization, information retrieval and question-answering. In summarization, keyphrases can be used as a form of semantic metadata (Barzilay and Elhadad, 1997;Lawrie et al., 2001;D'Avanzo and Magnini, 2005). In search engines, keyphrases can supplement full-text indexing and assist users in formulating queries.Recently, a resurgence of interest in keyphrase extraction has led to the development of several new systems and techniques for the task (Frank et al.

We propose a new method that enhances automatic keyphrase extraction by using semantic information on terms and phrases gleaned from a domain-specific thesaurus. We evaluate the results against keyphrase sets assigned by a state-of-the-art keyphrase extraction system and those assigned by six professional indexers.

Keyphrases are widely used in both physical and digital libraries as a brief but precise summary of documents. They help organize material based on content, provide thematic access, represent search results, and assist with navigation. Manual assignment is expensive, because trained human indexers must reach an understanding of the document and select appropriate descriptors according to defined cataloguing rules. We propose a new method that enhances automatic keyphrase extraction by using semantic information about terms and phrases gleaned from a domain-specific thesaurus. The key advantage of the new approach is that it performs well with very little training data. We evaluate it on a large set of manually indexed documents in the domain of agriculture, compare its consistency with a group of six professional indexers, and explore its performance on smaller collections of documents in other domains and of French and Spanish documents.