| Dependency parsers provide syntactic information about sentences. |
| Unlike deep parsers, they do not explicitly find phrases (e.g., NP or VP); rather, they find the dependencies between words. |
| For example, "hormone replacement therapy" would have deep structure: |
| `````(NP (NML (NN hormone) (NN replacement)) (NN therapy))````` |
| but its dependency structure would show that "hormone" depends on "replacement" and "replacement" in turn depends on "therapyl". |
| Below, the first column of numbers indicates the ID of the word, and the second number indicates what it is dependent on. |
| ```23 hormone hormone NN 24 NMOD 24 replacement replacement NN 25 NMOD 25 therapy therapy NN 22 PMOD``` |
| Dependency parsers can be labeled as well, e.g., we could specify that "hormone" is in a noun-modifier (i.e., NMOD) relationship with "therapy" in the example above (the last column). |
| This project provides an [Apache UIMA](https://uima.apache.org/) wrapper and some utilities for [ClearParser](https://github.com/clearnlp), |
| a transition-based dependency parser that achieves state-of-the-art accuracy and speed. |
| |
| ClearParser is described in: |
| "K-best, Locally Pruned, Transition-based Dependency Parsing Using Robust Risk Minimization." |
| Jinho D. Choi, Nicolas Nicolov, Collections of Recent Advances in Natural Language Processing V, |
| 205-216, John Benjamins, Amsterdam & Philadelphia, 2009. |
| |
| The semantic role labeler assigns the predicate-argument structure of the sentence. (Who did what to whom when and where.) |
