Measuring the similarity between words, sentences, paragraphs and documents is an
important component in various tasks such as information retrieval, document clustering …

Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval
(IR). Like IR, CLIR is centered on the search for documents and for information contained …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Most commonly used approaches to develo** automated or artificially intelligent
algorithmic systems are Big Data-driven and machine learning-based. However, these …

Plagiarism can be of many different natures, ranging from copying texts to adopting ideas,
without giving credit to its originator. This paper presents a new taxonomy of plagiarism that …

We present a new approach to crosslanguage text classification that builds on structural
correspondence learning, a recently proposed theory for domain adaptation. The approach …

Adequate representation of natural language semantics requires access to vast amounts of
common sense and domain-specific world knowledge. Prior work in the field was based on …

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 …

Information retrieval systems traditionally rely on textual keywords to index and retrieve
documents. Keyword-based retrieval may return inaccurate and incomplete results when …