In this paper we present our approach and the system description for Sub-task
A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive
Language in Social Media. Sub-task A involves identifying if a given tweet is
offensive or not, and Sub Task B involves detecting if an offensive tweet is
targeted towards someone (group or an individual). Our models for Sub-task A is
based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with
attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B,
we rely on a set of heuristics derived from the training data and manual
observation. We provide detailed analysis of the results obtained using the
trained models. Our team ranked 5th out of 103 participants in Sub-task A,
achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in
Sub Task B achieving a macro F1 of 0.695.

本文介绍了我们在 SemEval 2019 任务 6：识别和分类社交媒体中的冒犯性语言的子任务 A 和子任务 B 中的方法和系统描述。其中子任务 A 包括确定给定推文是否具有冒犯性，子任务 B 涉及检测是否针对某人（团体或个人）的攻击性推文。我们基于卷积神经网络，带有关注机制的双向 LSTM 和双向 LSTM + 双向 GRU 的集成模型进行子任务 A 的建模，而对于子任务 B，我们依赖于从训练数据和手动观察中得出的一组启发式规则。我们详细分析了使用训练模型获得的结果。我们的团队在子任务 A 中排名第 5，获得 0.807 的宏 F1 分数，在子任务 B 中排名第 8，获得 0.695 的宏 F1 分数。

从 Twitter 辨识攻击性帖文和攻击目标

Identifying Offensive Posts and Targeted Offense from Twitter

We present the SemEval 2019 shared task on UCCA parsing in English, German
and French, and discuss the participating systems and results. UCCA is a
cross-linguistically applicable framework for semantic representation, which
builds on extensive typological work and supports rapid annotation. UCCA poses
a challenge for existing parsing techniques, as it exhibits reentrancy
(resulting in DAG structures), discontinuous structures and non-terminal nodes
corresponding to complex semantic units. The shared task has yielded
improvements over the state-of-the-art baseline in all languages and settings.
Full results can be found in the task's website
https://competitions.codalab.org/competitions/19160.

介绍 SemEval 2019 有关英语、德语和法语 UCCA 解析的任务和参与系统和结果。