Automatic Learning of Linguistic Resources for Stopword Removal and Stemming from Text

While multimedia digital documents are progressively spreading, most of the content of Digital Libraries is still in the form of text, and this predominance will probably never be questioned. Except pure display of these documents, all other tasks are based on some kind of Natural Language Processing, that must be supported by suitable linguistic resources. Since these resources are clearly language-specific, they might be unavailable for several languages, and manually building them is costly, time-consuming and error-prone. This paper proposes a methodology to automatically learn linguistic resources for a natural language starting from texts written in that language. The learned resources may enable further high-level processing of documents in that language, and/or be taken as a basis for further manual refinements. Experimental results show that its application may effectively provide useful linguistic resources in a fully automatic manner. (C) 2014 The Authors. Published by Elsevier B.V.