Transition-based dependency parsers generally use heuristic decoding algorithms but can accommodate arbitrarily rich feature representations. In this paper, we show that we can improve the accuracy of such parsers by considering even richer feature sets than those employed in previous systems. In the standard Penn Treebank setup, our novel features improve attachment score form 91.4% to 92.9%, giving the best results so far for transitionbased parsing and rivaling the best results overall. For the Chinese Treebank, they give a signﬁcant improvement of the state of the art. ...
We present a novel method for record extraction from social streams such as Twitter. Unlike typical extraction setups, these environments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual messages may not express the full relation to be uncovered, as is often assumed in extraction tasks.
This novel modeling technique provides a powerful new tool
for tissue evaluation in osteoarthritis research, cartilage tissue
engineering and related animal studies. Once the approach is fully
validated against the gold standard (Pg chemical assays), lu plans
to migrate the current table-top indentation setup to a handheld