A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval

In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail

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

Semantic annotation of multilingual learning objects based on a domain ontology

One of the important tasks in the use of learning resources in e-learning is the necessity to annotate learning objects with appropriate metadata. However, annotating resources by hand is time consuming and difficult. Here we explore the problem of automatic extraction of metadata for description of learning resources. First, theoretical constraints for gathering certain types of metadata important for e-learning systems are discussed. Our approach to annotation is then outlined. This is based on a domain ontology, which allows us to annotate learning resources in a language independent way.We are motivated by the fact that the leading providers of learning content in various domains are often spread across countries speaking different languages. As a result, cross-language annotation can facilitate accessibility, sharing and reuse of learning resources

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge

Extending, trimming and fusing WordNet for technical documents

This paper describes a tool for the automatic extension and trimming of a multilingual WordNet database for cross-lingual retrieval and multilingual ontology building in intranets and domain-specific document collections. Hierarchies, built from automatically extracted terms and combined with the WordNet relations, are trimmed with a disambiguation method based on the document salience of the words in the glosses. The disambiguation is tested in a cross-lingual retrieval task, showing considerable improvement (7%-11%). The condensed hierarchies can be used as browse-interfaces to the documents complementary to retrieval

Multilingual Schema Matching for Wikipedia Infoboxes

Recent research has taken advantage of Wikipedia's multilingualism as a resource for cross-language information retrieval and machine translation, as well as proposed techniques for enriching its cross-language structure. The availability of documents in multiple languages also opens up new opportunities for querying structured Wikipedia content, and in particular, to enable answers that straddle different languages. As a step towards supporting such queries, in this paper, we propose a method for identifying mappings between attributes from infoboxes that come from pages in different languages. Our approach finds mappings in a completely automated fashion. Because it does not require training data, it is scalable: not only can it be used to find mappings between many language pairs, but it is also effective for languages that are under-represented and lack sufficient training samples. Another important benefit of our approach is that it does not depend on syntactic similarity between attribute names, and thus, it can be applied to language pairs that have distinct morphologies. We have performed an extensive experimental evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and English. The results show that not only does our approach obtain high precision and recall, but it also outperforms state-of-the-art techniques. We also present a case study which demonstrates that the multilingual mappings we derive lead to substantial improvements in answer quality and coverage for structured queries over Wikipedia content.Comment: VLDB201

Providing Multilingual Access to Health-Oriented Content

Finding health-related content is not an easy task. People have to know what to search for, which medical terms to use, and where to find accurate information. This task becomes even harder when people such as immigrants wish to find information in their country of residence and do not speak the national language very well. In this paper, we present a new health information system that allows users to search for health information using natural language queries composed of multiple languages. We present the technical details of the system and outline the results of a preliminary user study to demonstrate the usability of the system

Automatic multi-label subject indexing in a multilingual environment

This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages