Wikipedia Article Titles Research Articles

A cyber risk prediction model using common vulnerabilities and exposures

The cyber risk from malicious external attackers is a significant socio-economic problem. Cyber risk prediction is particularly difficult, given the constantly changing attack vectors. This study presents a model that automatically predicts cyber risks. The model is only based on common vulnerabilities and exposures (CVE) data and supervised prediction algorithms. This approach eliminates expert opinion bias in cyber risk prediction. Our supervised data-driven model, CyRiPred, CVE data into cyber risk groups by mapping the textual description field of the database into relevant Wikipedia article titles. Then CyRiPred aggregates the occurrence and severity of extracted topics for the desired time unit and produces a time series fed to supervised regressors for prediction. The risks are calculated using predicted occurrence and impact. Finally, the cyber risks are ranked by their score, and the top ten risks are presented. The proposed model is evaluated, and the results are discussed.

Neural machine translation for low-resource languages without parallel corpora

The problem of a total absence of parallel data is present for a large number of language pairs and can severely detriment the quality of machine translation. We describe a language-independent method to enable machine translation between a low-resource language (LRL) and a third language, e.g. English. We deal with cases of LRLs for which there is no readily available parallel data between the low-resource language and any other language, but there is ample training data between a closely-related high-resource language (HRL) and the third language. We take advantage of the similarities between the HRL and the LRL in order to transform the HRL data into data similar to the LRL using transliteration. The transliteration models are trained on transliteration pairs extracted from Wikipedia article titles. Then, we automatically back-translate monolingual LRL data with the models trained on the transliterated HRL data and use the resulting parallel corpus to train our final models. Our method achieves significant improvements in translation quality, close to the results that can be achieved by a general purpose neural machine translation system trained on a significant amount of parallel data. Moreover, the method does not rely on the existence of any parallel data for training, but attempts to bootstrap already existing resources in a related language.

Identifying Topics in Microblogs Using Wikipedia.

Twitter is an extremely high volume platform for user generated contributions regarding any topic. The wealth of content created at real-time in massive quantities calls for automated approaches to identify the topics of the contributions. Such topics can be utilized in numerous ways, such as public opinion mining, marketing, entertainment, and disaster management. Towards this end, approaches to relate single or partial posts to knowledge base items have been proposed. However, in microblogging systems like Twitter, topics emerge from the culmination of a large number of contributions. Therefore, identifying topics based on collections of posts, where individual posts contribute to some aspect of the greater topic is necessary. Models, such as Latent Dirichlet Allocation (LDA), propose algorithms for relating collections of posts to sets of keywords that represent underlying topics. In these approaches, figuring out what the specific topic(s) the keyword sets represent remains as a separate task. Another issue in topic detection is the scope, which is often limited to specific domain, such as health. This work proposes an approach for identifying domain-independent specific topics related to sets of posts. In this approach, individual posts are processed and then aggregated to identify key tokens, which are then mapped to specific topics. Wikipedia article titles are selected to represent topics, since they are up to date, user-generated, sophisticated articles that span topics of human interest. This paper describes the proposed approach, a prototype implementation, and a case study based on data gathered during the heavily contributed periods corresponding to the four US election debates in 2012. The manually evaluated results (0.96 precision) and other observations from the study are discussed in detail.

Automatic compilation of language resources for named entity recognition in Turkish by utilizing Wikipedia article titles

We present an automatic approach to compile language resources for named entity recognition (NER) in Turkish by utilizing Wikipedia article titles. First, a subset of the article titles is annotated with the basic named entity types. This subset is then utilized as training data to automatically classify the remaining titles by employing the k-nearest neighbor algorithm, leading to the construction of a significant lexical resource set for Turkish NER. Experiments on different text genres are conducted after extending an existing NER system with the resources and the results obtained confirm that the resources contribute to NER on different genres.

Exploiting Wikipedia Knowledge for Conceptual Hierarchical Clustering of Documents

In this paper, we propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. The proposed method overcomes the classic bag-of-words models disadvantages through the exploitation of Wikipedia textual content and link structure. A robust and compact document representation is built in real-time using the Wikipedia application programmer's interface, without the need to store locally any Wikipedia information. The clustering process is hierarchical and extends the idea of frequent items by using Wikipedia article titles for selecting cluster labels that are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach, both in terms of F-measure and entropy on the one hand and computational cost on the other.

Concordance-based entity-oriented search

We consider the problem of finding relevant named entities in response to a search query over a given text corpus. Entity search can readily be used to augment conventional web search engines for a variety of applications. We use entity concordance documents to generate lists of relevant entities for arbitrary text queries. To assess the significance of entity search, we analyzed the AOL dataset of 36 million web search queries with respect to two different sets of entities: namely (a) 2.3 million distinct entities extracted from a news text corpus and (b) 2.9 million Wikipedia article titles. The results clearly indicate that search engines should be aware of entities, for under various criteria of matching between 18-39% of all web search queries can be recognized as specifically searching for entities, while 73-87% of all queries contain entities. Our entity search engine creates a concordance document for each entity, consisting of all the sentences in the corpus containing that entity. We then index and search these documents using open-source search software. This gives a ranked list of entities as the result of search. Visit http://www.textmap.com for a demonstration of our entity search engine over a large news corpus. In the case where the query is a named entity, we evaluate the performance of our system by comparing the results of our search engine to the list of entities that have highest statistical juxtaposition scores with the queried entity. Juxtaposition score is a measure of how strongly two entities are related in terms of a probabilistic upper bound. The results show excellent performance, particularly over well-characterized classes of entities such as people.