Search (68 results, page 4 of 4)

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Theme

Semantic Web

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Abstract

Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notable Web corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph-based approach to constructing a cross-language association dictionary (CLAD) from Wikipedia, which can be used in a variety of cross-language accessing and processing applications. In order to evaluate the quality of the mined CLAD, and to demonstrate how the mined CLAD can be used in practice, we explore two different applications of the mined CLAD to cross-language information retrieval (CLIR). First, we use the mined CLAD to conduct cross-language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standard CLIR test collections show that the CLIR retrieval performance can be substantially improved with the above two applications of CLAD, which indicates that the mined CLAD is of sound quality.

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Abstract

A lot of information that is already available on the Web, or retrieved from local information systems and social networks, is structured in data silos that are not semantically related. Semantic technologies make it apparent that the use of typed links that directly express their relations are an advantage for every application that can reuse the incorporated knowledge about the data. For this reason, data integration, through reengineering (e.g., triplify) or querying (e.g., D2R), is an important task in order to make information available for everyone. Thus, in order to build a semantic map of the data, we need knowledge about data items itself and the relation between heterogeneous data items. Here we present our work of providing Lexical Linked Data (LLD) through a meta-model that contains all the resources and gives the possibility to retrieve and navigate them from different perspectives. After giving the definition of Lexical Linked Data, we describe the existing datasets we collected and the new datasets we included. Here we describe their format and show some use cases where we link lexical data, and show how to reuse and inference semantic data derived from lexical data. Different lexical resources (MultiWordNet, EuroWordNet, MEMODATA Lexicon, the Hamburg Methaphor Database) are connected to each other towards an Integrated Vocabulary for LLD that we evaluate and present.

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Abstract

A multilingual thesaurus' development needs the appropriate methodological considerations not only for linguistics, but also cultural heterogeneity, as demonstrated in this report on the multilingual project of the Art & Architecture Thesaurus (AAT) in the Chinese language, which has been a collaboration between the Academia Sinica Center for Digital Culture and the Getty Research Institute for more than a decade. After a brief overview of the project, the paper will introduce a holistic methodology for considering how to enable Western art to be accessible to Chinese users and Chinese art accessible to Western users. The conceptual and structural issues will be discussed, especially the challenges of developing terminology in two different cultures. For instance, some terms shared by Western and Chinese cultures could be understood differently in each culture, which raises questions regarding their locations within the hierarchical structure of the AAT. Finally, the report will provide cases to demonstrate how the Chinese-Language AAT language supports online exhibitions, digital humanities and linking of digital art history content to the web of data.

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Abstract

This picture will rapidly change. The twin challenges of massive information overload via the web and ubiquitous computers present us with an unavoidable task: developing techniques to handle multilingual and multi-modal information robustly and efficiently, with as high quality performance as possible. The most effective way for us to address such a mammoth task, and to ensure that our various techniques and applications fit together, is to start talking across the artificial research boundaries. Extending the current technologies will require integrating the various capabilities into multi-functional and multi-lingual natural language systems. However, at this time there is no clear vision of how these technologies could or should be assembled into a coherent framework. What would be involved in connecting a speech recognition system to an information retrieval engine, and then using machine translation and summarization software to process the retrieved text? How can traditional parsing and generation be enhanced with statistical techniques? What would be the effect of carefully crafted lexicons on traditional information retrieval? At which points should machine translation be interleaved within information retrieval systems to enable multilingual processing?

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Abstract

Introduction Multilingualism has long been an issue that have been discussed and debated at ELAG conferences. Members of ELAG have generally considered the role of automation as an important factor in the development of multilingual subject access solutions. It is quite fitting that in the context of this year's theme of "Cross language applications and the web" that the latest development of the MACS project be presented. As the title indicates, this presentation will focus an the latest development of the Link management Interface (LMI) which is the pivotal tool of the MACS multilingual subject access solution. It will update the presentation given by Genevieve ClavelMerrin at last year's ELAG 2002 Conference in Rome. That presentation gave a thorough description of the work that had been undertaken since 1997. In particular, G. Clavel-Merrin described the development of the MACS prototype in which the mechanisms for the establishment and management of links between subject heading languages (SHLs) and the user search interface had been implemented.

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Abstract

The proper handling of acronyms plays a crucial role in medical texts, e.g. in patient records, as well as in scientific literature. Chapter six presents an approach, in which acronyms are automatically acquired from (bio-) medical literature. Furthermore, acronyms and their definitions in different languages are linked to each other using the MorphoSaurus text processing system. Automatic word sense disambiguation is still one of the most challenging tasks in Natural Language Processing. In Chapter seven, cross-lingual considerations lead to a new methodology for automatic disambiguation applied to subwords. Beginning with Chapter eight, a series of applications based onMorphoSaurus are introduced. Firstly, the implementation of the subword approach within a crosslanguage information retrieval setting for the medical domain is described and evaluated on standard test document collections. In Chapter nine, this methodology is extended to multilingual information retrieval in the Web, for which user queries are translated into target languages based on the segmentation into subwords and their interlingual mappings. The cross-lingual, automatic assignment of document descriptors to documents is the topic of Chapter ten. A large-scale evaluation of a heuristic, as well as a statistical algorithm is carried out using a prominent medical thesaurus as a controlled vocabulary. In Chapter eleven, it will be shown how MorphoSaurus can be used to map monolingual, lexical resources across different languages. As a result, a large multilingual medical lexicon with high coverage and complete lexical information is built and evaluated against a comparable, already available and commonly used lexical repository for the medical domain. Chapter twelve sketches a few applications based on MorphoSaurus. The generality and applicability of the subword approach to other domains is outlined, and proof-of-concepts in real-world scenarios are presented. Finally, Chapter thirteen recapitulates the most important aspects of MorphoSaurus and the potential benefit of its employment in medical information systems is carefully assessed, both for medical experts in their everyday life, but also with regard to health care consumers and their existential information needs.

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Footnote

Rez. in: Machine translation review: 1999, no.10, S.26-27 (D. Lewis): "Cross Language Information Retrieval (CLIR) addresses the growing need to access large volumes of data across language boundaries. The typical requirement is for the user to input a free form query, usually a brief description of a topic, into a search or retrieval engine which returns a list, in ranked order, of documents or web pages that are relevant to the topic. The search engine matches the terms in the query to indexed terms, usually keywords previously derived from the target documents. Unlike monolingual information retrieval, CLIR requires query terms in one language to be matched to indexed terms in another. Matching can be done by bilingual dictionary lookup, full machine translation, or by applying statistical methods. A query's success is measured in terms of recall (how many potentially relevant target documents are found) and precision (what proportion of documents found are relevant). Issues in CLIR are how to translate query terms into index terms, how to eliminate alternative translations (e.g. to decide that French 'traitement' in a query means 'treatment' and not 'salary'), and how to rank or weight translation alternatives that are retained (e.g. how to order the French terms 'aventure', 'business', 'affaire', and 'liaison' as relevant translations of English 'affair'). Grefenstette provides a lucid and useful overview of the field and the problems. The volume brings together a number of experiments and projects in CLIR. Mark Davies (New Mexico State University) describes Recuerdo, a Spanish retrieval engine which reduces translation ambiguities by scanning indexes for parallel texts; it also uses either a bilingual dictionary or direct equivalents from a parallel corpus in order to compare results for queries on parallel texts. Lisa Ballesteros and Bruce Croft (University of Massachusetts) use a 'local feedback' technique which automatically enhances a query by adding extra terms to it both before and after translation; such terms can be derived from documents known to be relevant to the query.