Kukulska-Hulme, Agnes (2008). Encyclopedic knowledge in the mobile age. In: Needham, Gill and

able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations

This paper presents a topic-driven multidocument summarization approach that relies on linking documents to Wikipedia. Wikipedia provides structural support to retrieve relevant concepts from the documents to be summarized, and quantify the strength of the relations between them, thus expanding the topic. We identify concepts in the documents, and assign them scores that describe their relevance to the topic, their significance in general, and a machine-learned confidence that they should appear in the summary. Sentences are ranked according to the scores of the concepts within them and how much new information they provide. The best are extracted and compressed to form the summary. The system is trained and developed using the DUC 2005 and 2006 data. It was tested on the DUC 2007 data before deploying it on the update summarization task of TAC 2009. It performs 5th (compared to 30 peers) in DUC 2007, and 21st (of 52 peers) on the TAC 2009 update task. 1

We present a new method for detecting and disambiguating named entities in open domain text. A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia. The resulting model significantly outperforms a less informed

We propose a method to generate large-scale encyclopedic knowledge, which is valuable for much NLP research, based on the Web.

This paper presents a supervised method for resolving metonymies. We enhance a commonly used feature set with features extracted based on collocation information from corpora, generalized using lexical and encyclopedic knowledge to determine the preferred sense of the potentially metonymic word

different user models representations: one based on keywords and one exploiting encyclopedic knowledge extracted from Wikipedia. A preliminary evaluation involving 51 Facebook and Twitter users has shown that the use of an encyclopedic-based representation better re-flects user preferences, and helps

In this paper, we address the task of crosslingual semantic relatedness. We introduce a method that relies on the information extracted from Wikipedia, by exploiting the interlanguage links available between Wikipedia versions in multiple languages. Through experiments performed on several language pairs, we show that the method performs well, with a performance comparable to monolingual measures of relatedness. 1

This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human

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