Hierarchical Label Propagation and Discovery for Machine Generated Email
By James Bradley Wendt, Michael Bendersky, Lluís Garcia Pueyo, Vanja Josifovski, Balint Miklos, Ivo Krka, Amitabh Saikia, Jie Yang, Marc-Allen Cartright, Sujith Ravi. 2016
30 citations recorded by the source corpus.
Machine-generated documents such as email or dynamic web pages are single instantiations of a pre-defined structural template. As such, they can be viewed as a hierarchy of template and document specific content. This hierarchical template representation has several important advantages for document clustering and classification. First, templates capture common topics among the documents, while filtering out the potentially noisy variabilities such as personal information. Second, template representations scale far better than document representations since a single template captures numerous documents. Finally, since templates group together structurally similar documents, they can propagate properties between all the documents that match the template. In this paper, we use these advantages for document classification by formulating an efficient and effective hierarchical label propagation and discovery algorithm. The labels are propagated first over a template graph (constructed based on either term-based or topic-based similarities), and then to the matching documents. We evaluate the performance of the proposed algorithm using a large donated email corpus and show that the resulting template graph is significantly more compact than the corresponding document graph and the hierarchical label propagation is both efficient and effective in increasing the coverage of the baseline document classification algorithm. We demonstrate that the template label propagation achieves more than 91% precision and 93% recall, while increasing the label coverage by more than 11%.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sujith Ravi
- Karol Kurach
- Andrew Tomkins
- Anjuli Kannan
- Balint Miklos
- Greg S. Corrado
- László Lukács
- Yoav Goldberg
In Information Systems
Citations in the corpus
- Smart Reply: Automated Response Suggestion for Email (2016, positive): “While Expander was originally proposed for knowledge expansion and classification tasks [26], our work is the first to use it to discover semantic intent clusters from user-generated content.”