Maritime security requires full-time monitoring of the situation, mainly based on technical data (radar, AIS) but also from OSINT-like inputs (e.g., newspapers). Some threats to the operational reliability of this maritime surveillance, such as malicious actors, introduce discrepancies between hard and soft data (sensors and texts), either by tweaking their AIS emitters or by emitting false information on pseudo-newspapers.Many techniques exist to identify these pieces of false information, including using knowledge base population techniques to build a structured view of the information. This paper presents a use case for suspect data identification in a maritime setting. The proposed system UMBAR ingests data from sensors and texts, processing them through an information extraction step, in order to feed a Knowledge Base and finally perform coherence checks between the extracted facts.

Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. It is informal and does not follow any conventional rules, raising many NLP challenges. In particular, Arabizi has recently emerged as the Arabic language in online social networks, becoming of great interest for opinion mining and sentiment analysis. Unfortunately, only few Arabizi resources exist and state-of-the-art language models such as BERT do not consider Arabizi. In this work, we construct and release two datasets: (i) LAD, a corpus of 7.7M tweets written in Arabizi and (ii) SALAD, a subset of LAD, manually annotated for sentiment analysis. Then, a BERT architecture is pre-trained on LAD, in order to create and distribute an Arabizi language model called BAERT. We show that a language model (BAERT) pre-trained on a large corpus (LAD) in the same language (Arabizi) as that of the fine-tuning dataset (SALAD), outperforms a state-of-the-art multi-lingual pretrained model (multilingual BERT) on a sentiment analysis task.