论文标题
使用单词嵌入来分析抗议新闻
Using Word Embeddings to Analyze Protests News
论文作者
论文摘要
CLEF 2019抗议新事件的前两项任务重点是在二进制分类任务中区分抗议和非抗议的新闻文章和句子。在提交中，选择了两个表现良好的模型，以用Elmo和Distilbert替换现有的Word embeddings Word2Vec和Fast Test。与单词或更早的向量方法不同，Elmo和Distilbert通过基于文本中的上下文信息捕获含义来表示单词作为向量的顺序。与FastText实现相比，Distilbert一词以外的原始模型的架构没有任何其他模型的架构，而是在F1得分上提高了0.66的性能。 Distilbert在任务和模型中也优于Elmo。通过删除停止词并窃听单词来清洁数据集，已证明在使用印度新闻文章的数据集培训时，可以使模型在不同环境中更具概括性，并在数据集中评估了与中国新闻文章的模型。
The first two tasks of the CLEF 2019 ProtestNews events focused on distinguishing between protest and non-protest related news articles and sentences in a binary classification task. Among the submissions, two well performing models have been chosen in order to replace the existing word embeddings word2vec and FastTest with ELMo and DistilBERT. Unlike bag of words or earlier vector approaches, ELMo and DistilBERT represent words as a sequence of vectors by capturing the meaning based on contextual information in the text. Without changing the architecture of the original models other than the word embeddings, the implementation of DistilBERT improved the performance measured on the F1-Score of 0.66 compared to the FastText implementation. DistilBERT also outperformed ELMo in both tasks and models. Cleaning the datasets by removing stopwords and lemmatizing the words has been shown to make the models more generalizable across different contexts when training on a dataset with Indian news articles and evaluating the models on a dataset with news articles from China.
