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However, Chinese knowledge graphs are still in the development stage, and contain fewer entities. DBpedia, YAGO and other English knowledge graphs provide open access to huge amounts of high-quality named entities. It plays an important role in the field of named entity query.
Numbers in different languages manual#
Manual comparative evaluation performed for seven language pairs in the information technology domain shows that the proportion of correctly translated terms increases for all language pairs by up to +52.6%.Ī knowledge graph is a structured knowledge system which contains a huge amount of entities and relations. Automatic evaluation for four investigated language pairs in the automotive domain shows SMT quality improvements by up to 26.9% (or 3.41 absolute BLEU points) over baseline systems.
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In almost all experiments performed in the scope of the thesis the methods allowed achieving SMT quality improvements. However, the most impressive achievement of the author’s work is the dynamic terminology integration method in SMT systems using a source text pre-processing workflow. The static terminology integration methods allow achieving a cumulative SMT quality improvement by up to 28.1% (or 3.56 absolute BLEU points) over an initial baseline system for the English-Latvian language pair. The evaluation efforts show that the methods for monolingual term identification and cross-lingual term mapping allow achieving state-of-the-art performance, which has been also validated by third party (independent) evaluation efforts. The terminology integration methods have been evaluated using the Moses SMT system and the LetsMT platform. The thesis describes and evaluates methods designed and implemented by the author for: 1) monolingual term identification in SMT system training data as well as documents submitted for translation, 2) term normalisation for acquisition of canonical forms of terms from terms in different inflected forms, 3) cross-lingual term mapping in parallel and comparable corpora collected from the Web, 4) probabilistic dictionary filtering in order to acquire resources for cross-lingual term mapping, 5) development of character-based SMT transliteration systems from probabilistic dictionaries, 6) inflected form generation for terms through rule-based morphological synthesis or monolingual corpus look-up, and other methods involved in the workflows for static and dynamic terminology integration in SMT systems. The work focusses not only on the SMT integration techniques, but also on methods for acquisition of linguistic resources necessary for different tasks involved in workflows for terminology integration in SMT systems. The author presents novel methods for terminology integration in SMT systems during training (through static integration) and during translation (through dynamic integration). The aim of this doctoral thesis is to research methods and develop tools that allow successfully integrating bilingual terminology into statistical machine translation systems so that the translation quality of terminology would increase and that the overall translation quality of the source text would increase. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal. cross-lingual mapping, and web-based content processing, e.g. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon.
Numbers in different languages download#
This entity name variant data, known as JRC-Names, has been available for public download since 2011. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Netanyahu/Netanjahu/Nétanyahou/Netahny/). The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants.