Proper nouns (names of unique things in the world, such as Berkeley and James) can be translated in the same way as common nouns (names of classes of things, such as city and person). For example, the same city in Ukraine is known as Lviv in English, Львів (L’viv) in Ukrainian, Львов (L’vov) in Russian, Lwów in Polish, and Lemberg […]
On November 25, the PanLex team began a month-long stay in Yogyakarta, a city on the island of Java in Indonesia. Mataram, the historical region in which Yogyakarta is located, was controlled by several medieval and early modern kingdoms, and then for two centuries was part of the Dutch East Indies. The region is home to two […]
In the first two posts in this series, we elaborated our belief that all people should be able to use their native language to exercise human rights and have access to opportunity. We showed that machine translation technology currently falls far short of this goal, but that there are realistic ways to make progress. In […]
The PanLex Database uses thousands of sources (multilingual dictionaries and databases) to produce word translations. Let’s go under the hood a little on this complex and interesting process. Let’s meet the Platyplex visualization.
In the first post in this series, we discussed the importance of bringing machine translation to all of the world’s languages and some of the economic and technical challenges in doing so. In this post, we will explore in more detail the current status of technological support for machine translation in under-served languages. Along the […]