Cross-lingual Dataless Classification for Languages with Small Wikipedia PresenceThe term “dataless classification” was used due to historical reason. To be more concrete, it would be supervisionless, which means that we have no supervised training documents, but still we can do document classification. One would also think about the term labelless, however, we have the label names of the categories.
Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data.
['Rk.', 'Hausa Bridge', 'Hausa Acc.', 'Uzbek Bridge', 'Uzbek Acc.']
Given the fact that for both Hausa and Uzbek, Arabic outperforms English for bridged CLDDC, and the fact that there are more local languages out of 7,000 languages in the world that cannot be translated to English but may be able to be translated to local popular language, we want to evaluate which language can be the best language as a bridge for the SWLs. All the translation of words are performed by Google Translate. The results show that Arabic is the best bridge for both languages. For all the 39 SWLs, we also checked the bridged results based on all the 49 LWLs, and we selected the best bridging languages and report the classification results. Some of the top bridging LWLs are in the same family with the target languages. For example, Arabic, Hebrew, and Hausa are in the Afro-Asiatic family. Moreover, region of the native speakers is also reflected. Persian speakers in Iran live relatively near to Uzbek speakers in Uzbekistan. However, writing system, language family, and region are not the only factors that affects the ranking. For example, Korean and Japanese are also ranked in the top ten, but none of the above factors appears. Besides other linguistic factors, we also presume that either the size of Wikipedia or the less ambiguity of translation may help them result in relatively good accuracy. Then the remaining question is how to automatically select a good bridging LWL for the SWL document classification.