Name
Abstract | ||
|---|---|---|
Named-entities in free text represent a challenge to text analysis in Machine Translation and Cross Language Information Retrieval. These phrases are often transliterated into another language with a different sound inventory and writing system. Named-entities found in free text are often not listed in bilingual dictionaries. Although it is possible to identify and translate named-entities on the fly without a list of proper names and transliterations, an extensive list of existing transliterations certainly will ensure high precision rate. We use a seed list of proper names and transliterations to train a Machine Transliteration Model. With the model it is possible to extract proper names and their transliterations in monolingual or parallel corpora with high precision and recall rates. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30194-3_20 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
machine translation,proper names,text analysis | Bilingual dictionary,Computer science,Multilingualism,Precision and recall,Machine translation,Computational linguistics,Speech recognition,Artificial intelligence,Natural language processing,Proper noun,Cross-language information retrieval,Transliteration | Conference |
Volume | ISSN | Citations |
3265 | 0302-9743 | 3 |
PageRank | References | Authors |
0.50 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tracy Lin | 1 | 13 | 2.77 |
| Jian-Cheng Wu | 2 | 70 | 13.30 |
| Jason S. Chang | 3 | 345 | 62.64 |