Clare R. Voss

TL;DR: This paper proposed a framework for automated phrase mining, $\mathsf{AutoPhrase}$, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger.

TL;DR: CoType as mentioned in this paper proposes a domain-independent framework that runs a data-driven text segmentation algorithm to extract entity mentions and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, where objects whose types are close will also have similar representations.

TL;DR: This work proposes a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition that discovers high quality topical phrases with negligible extra cost to the bag-of-words topic model in a variety of datasets.

TL;DR: A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other.

TL;DR: This work investigates machine learning methods to de-anonymize source code authors of C/C++ using coding style using random forest and abstract syntax tree-based approach, and finds that the code resulting from difficult programming tasks is easier to attribute than easier tasks and skilled programmers are easier to attributes than less skilled programmers.