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

Thematic Annotation: extracting concepts out of documents

Contrarily to standard approaches to topic annotation, the technique used in this work does not centrally rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary -- that provides a concept hierarchy based on hyponym and hypernym relations. This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts best preserving the document's content. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most relevant set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concepts that are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure

An automatically built named entity lexicon for Arabic

We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold

The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain

Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model named MeSH-gram which relies on a straighforward approach that extends the skip-gram neural network model by considering MeSH (Medical Subject Headings) descriptors instead words. Trained on publicly available corpus PubMed MEDLINE, MeSH-gram is evaluated on reference standards manually annotated for semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. A deeper comparison is performed with tewenty existing models. All the obtained results of Spearman's rank correlations between human scores and computed similarities show that MeSH-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.Comment: 6 pages, 2 table

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

empathi: An ontology for Emergency Managing and Planning about Hazard Crisis

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/