Named Entities in Stock Market Tweets: A Fine-Grained and Linguistically-Motivated Annotation - Piai, Laís; Di-Felippo, Ariani; Roman, Norton Trevisan (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2025: Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, 2025)
This work provides a second look at the Named Entity annotation of DANTEStocks – a corpus of stock market tweets in Portuguese – offering an in-depth analysis of the linguistic decisions involved in creating a gold-standard annotation tailored to this genre and domain. Our methodology builds on the guidelines of the Second HAREM evaluation, extending and reinterpreting them to the adopted genre and domain. The article furnishes then an analysis of the linguistic phenomena that challenge to this task, proposes specific strategies for entity delimitation and classification, and presents a linguistic characterization of the corpus based on the class distribution that resulted from the annotation.