We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data. Our proposed model learns to extract textual features using a multi-layer recurrent network, and then performs text classification using gradient-boosted decision trees (GBDT). A self-attention architecture enables the model to focus on the most relevant areas in the text. In order to enrich input representations, we use node2vec to learn globally optimised embeddings for hashtags, which are then given as additional features to the GBDT classifier. Our best model obtains 78.79% macro F1-score on detecting offensive language (subtask A), 66.32% on categorising offence types (targeted/untargeted; subtask B), and 55.36% on identifying the target of offence (subtask C).

Tajik Persian is a dialect of Persian spoken primarily in Tajikistan and written with a modified Cyrillic alphabet. Iranian Persian, or Farsi, as it is natively called, is the lingua franca of Iran and is written with the Persian alphabet, a modified Arabic script. Although the spoken versions of Tajik and Farsi are mutually intelligible to educated speakers of both languages, the difference between the writing systems constitutes a barrier to text compatibility between the two languages. This paper presents a system to transliterate text between these two different Persian dialects that use incompatible writing systems. The system also serves as a mechanism to facilitate sharing of computational linguistic resources between the two languages. This is relevant because of the disparity in resources for Tajik versus Farsi.

In this paper we describe a proof-of-concept for the bootstrapping of a Persian WordNet. This effort was motivated by previous work done at Stanford University on bootstrapping an Arabic WordNet using a parallel corpus and an English WordNet. The principle of that work is based on the premise that paradigmatic relations are by nature deeply semantic, and as such, are likely to remain intact between languages. We performed our task on a Persian-English bilingual corpus of George Orwell’s Nineteen Eighty-Four. The corpus was neither aligned nor sense tagged, so it was necessary that these were undertaken first. A combination of manual and semiautomated methods were used to tag and sentence align the corpus. Actual mapping of English word senses onto Persian was done using automated techniques. Although Persian is written in Arabic script, it is an Indo-European language, while Arabic is a Central Semitic language. Despite their linguistic differences, we endeavor to test the applicability of the Stanford strategy to our task.