Studying the adaptation of Portuguese NER for different textual genres

Named Entity Recognition (NER) is the task of automatically identifying named entities and classifying them into predefined categories such as person, place, organization, among other. This task is important and challenging, especially when the system must be able to recognize named entities in many textual genres, including genres that differ from those for which it was trained. This paper aims to report the initial efforts made to adapt a NER system for many textual genres in accordance with the proposed Portuguese Named Entity Recognition task in IberLEF 2019. To achieve this goal, the system was trained in an augmented training corpus. In addition, a Local Grammar (handmade rules to identify named entities within the text) was adapted to capture rules of different textual genres. We discuss the results of this study and some difficulties involved in this task.