Parser Adaptation for Social Media by Integrating Normalization

Previous photos and videos This work explores normalization for parser adaptation. Traditionally, normalization is used as separate preprocessing step. We show that integrating the normalization model into the parsing algorithm is beneficial. To this end, we use a normalization model combined with the parsing as intersection algorithm. This way, multiple normalization candidates can be leveraged, which improves parsing performance on social media. We test this hypothesis by modifying the Berkeley parser; outof-the-box it reaches an F1 score of 66.52. Our integrated approach performs significantly better, with an F1 score of 67.36, while using the best normalization sequence results in an F1 score of only 66.94.