This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system 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 are reliable and hardly distinguishable from manual annotations. providing the users a quick way of accessing additional information. Wikipedia contributors perform these annotations by hand following a Wikipedia“manual of style,”which gives guidelines concerning the selection of important concepts in a text, as well as the assignment of links to appropriate related articles. For instance, Figure 1 shows an example of a Wikipedia page, including the definition for one of the meanings of the word “plant.”

We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a wellstructured top-level taxonomy from WordNet. 1

Abstract. Queries submitted to a retrieval system are often ambiguous. In such a situation, a sensible strategy is to diversify the ranking of results to be retrieved, in the hope that users will find at least one of these results to be relevant to their information need. In this paper, we introduce xQuAD, a novel framework for search result diversification that builds such a diversified ranking by explicitly accounting for the relationship between documents retrieved for the original query and the possible aspects underlying this query, in the form of sub-queries. We evaluate the effectiveness of xQuAD using a standard TREC collection. The results show that our framework markedly outperforms state-ofthe-art diversification approaches under a simulated best-case scenario. Moreover, we show that its effectiveness can be further improved by estimating the relative importance of each identified sub-query. Finally, we show that our framework can still outperform the simulated bestcase scenario of the state-of-the-art diversification approaches using subqueries automatically derived from the baseline document ranking itself. 1

Text simplification is the process of changing vocabulary and grammatical structure to create a more accessible version of the text while maintaining the underlying information and content. Automated tools for text simplification are a practical way to make large corpora of text accessible to a wider audience lacking high levels of fluency in the corpus language. In this work, we investigate the potential of Simple Wikipedia to assist automatic text simplification by building a statistical classification system that discriminates simple English from ordinary English. Most text simplification systems are based on hand-written rules (e.g., PEST (Carroll et al., 1999) and its module SYSTAR (Canning et al., 2000)), and therefore face limitations scaling and transferring across domains. The potential for using Simple Wikipedia for text simplification is significant; it contains nearly 60,000 articles with revision histories and aligned articles to ordinary English Wikipedia. Using articles from Simple Wikipedia and ordinary Wikipedia, we evaluated different classifiers and feature sets to identify the most discriminative features of simple English for use across domains. These findings help further understanding of what makes text simple and can be applied as a tool to help writers craft simple text. 1

Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation. 1

We refine the most frequent sense baseline for word sense disambiguation using a number of novel word sense disambiguation techniques. Evaluating on the Senseval-3 English all words task, our combined system focuses on improving every stage of word sense disambiguation: starting with the lemmatization and part of speech tags used, through the accuracy of the most frequent sense baseline, to highly targeted individual systems. Our supervised systems include a ranking algorithm and a Wikipedia similarity measure. 1

In this paper an approach based on Wikipedia link structure for sense disambiguation is presented and evaluated. Wikipedia is used as a reference to obtain lexicographic relationships and in combination with statistical information extraction it is possible to deduce concepts related to the terms extracted from a corpus. In addition, since the corpus covers a representation of a part of the real world the corpus itself is used as training data for choosing the sense which best fit the corpus. 1

Tagged corpora are essential for evaluating and training natural language processing tools. The cost of constructing large enough manually tagged corpora is high, even when the annotation level is shallow. This article describes a simple method to automatically create a partially tagged corpus, using Wikipedia hyperlinks. The resulting corpus contains information about the correct segmentation of 523,599 non-consecutive words in 363,090 sentences. We used our method to construct a corpus of Modern Hebrew (which we have made available at

This paper presents WikiTranslate, a system which performs query translation for cross-lingual information retrieval (CLIR) using only Wikipedia to obtain translations. Queries are mapped to Wikipedia concepts and the corresponding translations of these concepts in the target language are used to create the final query. WikiTranslate is evaluated by searching with topics in Dutch, French and Spanish in an English data collection. The systems achieved a performance of 67 % compared to the monolingual baseline.

Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to predict concepts common to a set of documents. We describe several algorithms to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments demonstrate the feasibility of extending the category system with new concepts identified as a union of pages from the page link graph.