Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
复制标题
DOI：
10.18653/v1/p19-1242
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Mounica Maddela;W. Xu;Daniel Preotiuc-Pietro
中科院分区：
文献类型：
--
作者：
Mounica Maddela;W. Xu;Daniel Preotiuc-Pietro
主题标签通常在社交媒体上使用，以将元数据添加到文本话语中，目的是增加可扩展性，帮助搜索或提供额外的语义。然而，主题标签的语义内容并不容易推断，因为这些表示经常包括连接在一起的多个单词的临时约定，并且可以包括缩写和非正统拼写。我们构建了一个包含12，594个标签的数据集，这些标签被分成各个片段，并提出了一套标签分割方法，将其框架为候选分割之间的成对排名问题。与当前最先进的方法相比，我们的新型神经方法在主题标签分割准确性方面减少了24.6%的错误。最后，我们证明了通过分割获得的对hashtag语义的更深入理解对于情感分析等下游应用是有用的，我们在SemEval 2017情感分析数据集上实现了2.6%的平均召回率增长。
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6% increase in average recall on the SemEval 2017 sentiment analysis dataset.