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IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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While significant effort has been put into annotating linguistic resources for several languages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagating information (specifically mention detection information) into such low resource languages from richer ones. Experiments run on three language pairs (Arabic-English, Chinese-English, and Spanish-English) show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance using various degrees of linguistic information in a statistical framework, results show that propagated features from English help improve the source-language system performance even when used in conjunction with all feature types built from the source language. The experiments also show that using propagated features in conjunction with lexically-derived features only (as can be obtained directly from a mention annotated corpus) yields similar performance to using feature types derived from many linguistic resources.