First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors.
In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities.
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.
Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading.