Udapi version 0.1.3

Introduction

Udapi is a collection of three APIs for processing Universal Dependencies data. The three mutations are developed in Perl, Java, and Python respectively. The APIs are based on the same object-oriented conceptual model, and are harmonized as much the differences between these programming languages allow.

All mutations of Udapi are provided with similar command line tools (udapi.pl, udapy, udapi.groovy) that allow quick and comfortable application of Udapi processing units on CoNLL-U files.

The development of Udapi is hosted at GitHub. Anyone is very welcome to contribute. For more information on Udapi, please see http://udapi.github.io/.

Conceptual model

• Classes for data representation
  • Document. A document consists of a sequence of bundles, mirroring a sequence of sentences in a typical natural language text. A document instance can be composed programatically or can be loaded from (or stored to) a CoNLL-U formatted file.
  • Bundle. - A bundle corresponds to a sentence, possibly in more forms or with different representations, such as sentence-tuples from a parallel corpora, or a same single sentence with which more trees are associated (e.g. parses produced by different dependency parsers). The bundle level is unimportant for those who work only with the basic Universal Dependencies treebank collection, as it contains no parallel data and no multiple trees. If there are more trees in a bundle, then they must be distinguished by a so called zone.
  • Root. A root is a special (artificial) node that is added to the top of a CoNLL-U tree in the Udapi model. The root serves as a representant of the whole tree (e.g. it bears the sentence's identifier). The root's functionality partially overlaps with functionality of nodes (e.g., a root has its children), but differs in other aspects (its lemma cannot be set, its linear position cannot be changed too, etc.).
  • Node. A node in the Udapi model corresponds to a node of a dependency tree in the CoNLL-U format. It has all a CoNLL-U-defined attributes and a bunch of methods for tree traversal and for tree manipulation (both dependencies and linear ordering can be changed).
• Classes for data processing
  • Block. A block is the smallest processing unit that can be applid on UD data. Block classes implement usually some reasonably limited and well defined task, often corresponding to the classical NLP components (tokenization, tagging, parsing...), but there can be blocks for purely technical tasks (such as for feature extraction or for collecting statistical counts).
  • Run. A run corresponds typically to a sequence of blocks (also called scenario) that are to be applied on data one after another. Such scenarios can compose very complex NLP pipelines.

Classes