403 research outputs found
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
Sheffield University CLEF 2000 submission - bilingual track: German to English
We investigated dictionary based cross language information
retrieval using lexical triangulation. Lexical triangulation combines the results
of different transitive translations. Transitive translation uses a pivot language
to translate between two languages when no direct translation resource is
available. We took German queries and translated then via Spanish, or Dutch
into English. We compared the results of retrieval experiments using these
queries, with other versions created by combining the transitive translations or
created by direct translation. Direct dictionary translation of a query introduces
considerable ambiguity that damages retrieval, an average precision 79% below
monolingual in this research. Transitive translation introduces more ambiguity,
giving results worse than 88% below direct translation. We have shown that
lexical triangulation between two transitive translations can eliminate much of
the additional ambiguity introduced by transitive translation
Knowledge Representation and WordNets
Knowledge itself is a representation of âreal factsâ.
Knowledge is a logical model that presents facts from âthe real worldâ witch can be expressed in a formal language. Representation means the construction of a model of some part of reality.
Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence.
Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge.
Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams
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
Indexing with WordNet synsets can improve Text Retrieval
The classical, vector space model for text retrieval is shown to give better
results (up to 29% better in our experiments) if WordNet synsets are chosen as
the indexing space, instead of word forms. This result is obtained for a
manually disambiguated test collection (of queries and documents) derived from
the Semcor semantic concordance. The sensitivity of retrieval performance to
(automatic) disambiguation errors when indexing documents is also measured.
Finally, it is observed that if queries are not disambiguated, indexing by
synsets performs (at best) only as good as standard word indexing.Comment: 7 pages, LaTeX2e, 3 eps figures, uses epsfig, colacl.st
Towards a universal index of meaning
The Inter-Lingual-Index (ILI) in the EuroWordNet
architecture is an initially unstructured fund of concepts which functions as the link between the various language wordnets.The ILI concepts originate from WordNet1.5, and have been restructured on the basis of aspects of the internal structure of Word-Net,links between WordNet and other resources,and multilingual mapping between the wordnets.
This leads to a differentiation of the status of ILI concepts,a reduction of the Wordnet polysemy,and a greater connectivity between the wordnets. The restructured ILI represents the first step towards a
standardized set of word meanings,is a working platform for further development and testing,and can be put to use in NLP tasks such as (multilingual)information retrieval
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