Combining statistical and semantic approaches to the translation of ontologies and taxonomies

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approac

Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

Mind the Cultural Gap: Bridging Language-Specific DBpedia Chapters for Question Answering

International audienceIn order to publish information extracted from language specific pages of Wikipedia in a structured way, the Semantic Web community has started an effort of internationalization of DBpedia. Language specific DBpedia chapters can contain very different information from one language to another, in particular they provide more details on certain topics, or fill information gaps. Language specific DBpedia chapters are well connected through instance interlinking, extracted from Wikipedia. An alignment between properties is also carried out by DBpedia contributors as a mapping from the terms in Wikipedia to a common ontology, enabling the exploitation of information coming from language specific DBpedia chapters. However, the mapping process is currently incomplete, it is time-consuming as it is performed manually, and it may lead to the introduction of redundant terms in the ontology. In this chapter we first propose an approach to automatically extend the existing alignments, and we then present an extension of QAKiS, a system for Question Answering over Linked Data that allows to query language specific DB-pedia chapters relying on the above mentioned property alignment. In the current version of QAKiS, English, French and German DBpedia chapters are queried using a natural language interface