| Language resources|
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|Related keyword(s)||Language, Translation systems, NLP|
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Language resources is included as keyword or extra keyword in 0 datasets, 0 tools and 7 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Language resources extracted from Wikipedia||Vrandecic D.
|KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference||English||2011||Wikipedia provides an interesting amount of text for more than hundred languages. This also includes languages where no reference corpora or other linguistic resources are easily available. We have extracted background language models built from the content of Wikipedia in various languages. The models generated from Simple and English Wikipedia are compared to language models derived from other established corpora. The differences between the models in regard to term coverage, term distribution and correlation are described and discussed. We provide access to the full dataset and create visualizations of the language models that can be used exploratory. The paper describes the newly released dataset for 33 languages, and the services that we provide on top of them.||0||0|
|Ranking multilingual documents using minimal language dependent resources||Santosh G.S.K.
Kiran Kumar N.
|Lecture Notes in Computer Science||English||2011||This paper proposes an approach of extracting simple and effective features that enhances multilingual document ranking (MLDR). There is limited prior research on capturing the concept of multilingual document similarity in determining the ranking of documents. However, the literature available has worked heavily with language specific tools, making them hard to reimplement for other languages. Our approach extracts various multilingual and monolingual similarity features using a basic language resource (bilingual dictionary). No language-specific tools are used, hence making this approach extensible for other languages. We used the datasets provided by Forum for Information Retrieval Evaluation (FIRE) for their 2010 Adhoc Cross-Lingual document retrieval task on Indian languages. Experiments have been performed with different ranking algorithms and their results are compared. The results obtained showcase the effectiveness of the features considered in enhancing multilingual document ranking.||0||0|
|An N-gram-and-wikipedia joint approach to natural language identification||Yang X.
|2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings||English||2010||Natural Language Identification is the process of detecting and determining in which language or languages a given piece of text is written. As one of the key steps in Computational Linguistics/Natural Language Processing(NLP) tasks, such as Machine Translation, Multi-lingual Information Retrieval and Processing of Language Resources, Natural Language Identification has drawn widespread attention and extensive research, making it one of the few relatively well studied sub-fields in the whole NLP field. However, various problems remain far from resolved in this field. Current noncomputational approaches require researchers possess sufficient prior linguistic knowledge about the languages to be identified, while current computational (statistical) approaches demand large-scale training set for each to-be-identified language. Apparently, drawbacks for both are that, few computer scientists are equipped with sufficient knowledge in Linguistics, and the size of the training set may get endlessly larger in pursuit of higher accuracy and the ability to process more languages. Also, faced with multi-lingual documents on the Internet, neither approach can render satisfactory results. To address these problems, this paper proposes a new approach to Natural Language Identification. It exploits N-Gram frequency statistics to segment a piece of text in a language-specific fashion, and then takes advantage of Wikipedia to determine the language used in each segment. Multiple experiments have demonstrated that satisfactory results can be rendered by this approach, especially with multi-lingual documents.||0||0|
|Rich ontology extraction and wikipedia expansion using language resources||Schonberg C.
|Lecture Notes in Computer Science||English||2010||Existing social collaboration projects contain a host of conceptual knowledge, but are often only sparsely structured and hardly machine-accessible. Using the well known Wikipedia as a showcase, we propose new and improved techniques for extracting ontology data from the wiki category structure. Applications like information extraction, data classification, or consistency checking require ontologies of very high quality and with a high number of relationships. We improve upon existing approaches by finding a host of additional relevant relationships between ontology classes, leveraging multi-lingual relations between categories and semantic relations between terms.||0||0|
|Enriching Multilingual Language Resources by Discovering Missing Cross-Language Links in Wikipedia||Jong-Hoon Oh
|Enriching multilingual language resources by discovering missing cross-language links in Wikipedia||Oh J.-H.
|Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008||English||2008||We present a novel method for discovering missing crosslanguage links between English and Japanese Wikipedia articles. We collect candidates of missing cross-language links - a pair of English and Japanese Wikipedia articles, which could be connected by cross-language links. Then we select the correct cross-language links among the candidates by using a classifier trained with various types of features. Our method has three desirable characteristics for discovering missing links. First, our method can discover cross-language links with high accuracy (92% precision with 78% recall rates). Second, the features used in a classifier are language-independent. Third, without relying on any external knowledge, we generate the features based on resources automatically obtained from Wikipedia. In this work, we discover approximately 105 missing crosslanguage links from Wikipedia, which are almost two-thirds as many as the existing cross-language links in Wikipedia.||0||1|
|Extracting bilingual word pairs from Wikipedia||Tyers
|SALTMIL workshop at Language Resources and Evaluation Conference (LREC) 2008||2008||A bilingual dictionary or word list is an important resource for many purposes, among them, machine translation. For many language pairs these are either non-existent, or very often unavailable owing to licensing restrictions. We describe a simple, fast and computationally inexpensive method for extracting bilingual dictionary entries from Wikipedia (using the interwiki link system) and assess the performance of this method with respect to four language pairs. Precision was found to be in the 69-92% region, but open to improvement.||0||1|