In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data.Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event.

In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.

Xavier Carreras, Lluís Padró, Lei Zhang, Achim Rettinger, Zhixing Li, Esteban García-Cuesta, Željko Agić, Božo Bekavac, Blaz Fortuna, Tadej Štajner. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014.

In this paper, we present a document visualization technique for data analysis based on the semantic representation of text in the form of a directed graph, referred to as semantic graph. It is derived using natural language processing as follows. Firstly subject– verb – object triplets are automatically extracted from the Penn Treebank parse tree obtained for each sentence in the document. Secondly, the triplets are further enhanced by linking them to their corresponding co-referenced named entity, by resolving pronominal anaphors as well as attaching the associated WordNet synset. Starting from the document's semantic graph and the list of extracted triplets we automatically generate the document summary, for which we also derive the semantic representation.

In this paper we present a system for cross-lingual information retrieval, which can handle tens of languages and millions of documents. Functioning of the system is demonstrated on corpus of European Legislation (22 languages, more than 400,000 documents per language). The system uses an interactive web-interface, which can take advantage of a predefined thesaurus allowing the user to dynamically re-rank the retrieval results based on the mapping onto a predefined thesaurus.

On-line community services such as Live QnA and Yahoo! Answers enable their members to ask questions and have them answered by the community. The questions are labelled by the users to facilitate search, navigation, and recommendations. In this paper we provide an in-depth analysis of the question labelling practices by contrasting the use of community generated tags in the Live QnA service with the use of topic categories from a fixed taxonomy in the Yahoo! Answers service. We found that community tagging is related to higher levels of social interactions amongst users. Analysis of the most frequently used community tags reveals that active users may establish strong social ties around specific tags. Furthermore, the discriminative value of individual community tags can be low since the corresponding questions may cover a variety of topics. Thus, appropriate care needs to be taken when designing search, browsing, and recommender features for question discovery.

Improving the classification of newsgroup messages through social network analysis.

Web sites are often organized into several regions, each dedicated to a specific topic or serving a particular function. From a user's perspective, these regions typically form coherent sets of pages characterized by a distinct navigation structure and page layout-we refer to them as subsites. In this paper we propose to characterize Web site structure as a collection of subsites and devise a method for detecting subsites and entry points for subsite navigation. In our approach we use a new model for representing Web site structure called Link Structure Graph (LSG). The LSG captures a complete hyperlink structure of a Web site and models link associations reflected in the page layout. We analyze a sample of Web sites and compare the LSG based approach to commonly used statistics for Web graph analysis. We demonstrate that LSG approach reveals site properties that are beyond the reach of standard site models. Furthermore, we devise a method for evaluating the performance of subsite detection algorithms and provide evaluation guidelines.

In this paper we describe a solution for incorporating background knowledge into the OntoGen system for semi-automatic ontology construction. This makes it easier for different users to construct different and more personalized ontologies for the same domain. To achieve this we introduce a word weighting schema to be used in the document representation. The weighting schema is learned based on the background knowledge provided by user. It is than used by OntoGen's machine learning and text mining algorithms.