Linguistic corpus annotation is one of the most important phases for solving Natural Language Processing (NLP) tasks, as these methods are deeply involved with corpus-based techniques. However, meta-data annotation is a highly laborious manual task. A supportive alternative requires the use of computational tools. They are likely to simplify some of these operations, while can be adjusted appropriately to the needs of particular language features at the same time. Therefore, this paper presents ChAnot, a web-based annotation tool developed for Peruvian indigenous and highly agglutinative languages, where Shipibo-Konibo was the case study. This new tool is able to support a diverse set of linguistic annotation tasks, such as word segmentation, POS-tag markup, among others. Also, it includes a suggestion engine based on historic and machine learning models, and a set of statistics about pr...

Natural Language Processing deals with the understanding and generation of texts through computer programs. There are many different functionalities used in this area, but among them there are some functions that are the support of the remaining ones. These methods are related to the core processing of the morphology of the language (such as lemmatization) and automatic identification of the part-of-speech tag. Thereby, this paper describes the implementation of a basic NLP toolkit for a new language, focusing in the features mentioned before, and testing them in an own corpus built for the occasion. The obtained results exceeded the expected results and could be used for more complex tasks such as machine translation.