Gustavo Laboreiro

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The automatic processing of microblogging messages may be problematic, even in the case of very elementary operations such as tokenization. The problems arise from the use of non-standard language, including media-specific words (e.g. "2day", "gr8", "tl;dr", "loool"), emoticons (e.g. "(ò_ó)", "(=^-^=)"), non-standard letter casing (e.g. "dr.(More)
In this paper we propose a set of stylistic markers for automatically attributing authorship to micro-blogging messages. The proposed markers include highly personal and idiosyncratic editing options, such as ‘emoticons’, interjections, punctuation, abbreviations and other low-level features. We evaluate the ability of these features to help discriminate(More)
It is difficult to determine the country of origin of the author of a short message based only on the text. This is an even more complex problem when more than one country uses the same native language. In this paper, we address the specific problem of detecting the two main variants of the Portuguese language --- European and Brazilian --- in Twitter(More)
In this paper we study the problem of identifying systems that automatically inject non-personal messages in micro-blogging message streams, thus potentially biasing results of certain information extraction procedures, such as opinion-mining and trend analysis. We also study several classes of features, namely features based on the time of posting, the(More)
The INESC Porto group has participated in the search task (automatic and interactive). Our approach combines high-level features (the 39 concepts of the LSCOM-Lite set) with low-level features. We use a large set of low-level features with the intention of analysing as many facets as possible of each shot. The aggregation of large feature sets can be time(More)
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