1. Dependency parsing spaCy and Stanford nndep

2. Universal Dependency (UD)
nsubj: nominal subject (more agentive, the do-er)
obj: object
csubj: clause subject
aux: auxiliary verb

3. Dependency parsers spaCy Stanford corenlp nndep
open-source library for industrial-strength NLP (Cpython)
Transition-based parser, averaged perceptron
MIT license
accurate and fast (~2 sec for a Wikipedia article)
An Improved Non-monotonic Transition System for Dependency Parsing, CoNLL 2013
Stanford corenlp nndep
Open-source java library
Transition-based parser, 1 hidden layer neural network
full GPL license
accurate but slow (~15 sec for a Wikipedia article)
A Fast and Accurate Dependency Parser using Neural Networks, EMNLP 2014

4. Transition-based parser
Arc-eager (spaCy)
Actions: Shift, left-arc, right-arc, reduce
Reduce: remove the top token from Stack
Shift: takes the first incoming word from the buffer and push it onto the stack.
Left-arc: creates a dependency between the top of the stack and the first word of the buffer, and reduce
Right-arc: creates a dependency between the top of the stack and the first word of the buffer, and shift
Arc-standard (Stanford corenlp nndep)
Actions: Shift, left-arc, right-arc
Output
(They, subj, told)
(told, iobj, him)
(a, det, story)
(told, dobj, story)

5. Features used to determine actions (Stanford nndep)
s1.w: word of the top token in stack
b1.t: part-of-speech of the first token in buffer
o: concatenate (s1.t = VB, s2.t= NN, s1.t o s2.t =VBNN)
1 hidden layer feedforward neural network is used

6. Features used to determine actions (spaCy)
 averaged perceptron is used

8. The End Thank you very much. http://universaldependencies.org/u/dep/