This page describes the plans and actions required for getting DELPH-IN data and processing into the NLTK. For some context, please see this discussion from the 2016 Stanford Summit.

Contents

1. Motivation
2. Goals
3. Tasks
4. Questions

The NLTK (Natural-Language ToolKit) is a large and widely used (particularly in education) Python package supporting a number of NLP tasks, but currently it only has limited support for semantic representations, and nothing for representing/accessing DELPH-IN data (aside from a REPP wrapper). This is a good opportunity for us to expand our presence.

There are three kinds of additions we can provide to the NLTK:

• Data representations (e.g. modules for representing MRS, Derivation trees, etc.)

• Data (e.g. make Redwoods available through nltk.download() and provide necessary CorpusReaders)

• Processors (e.g. ACE or RESTful server interfaces)

Specifically, the following:

• Data representations
  • MRS
  • DMRS
  • EDS
  • DM (bilexical dependencies)
  • Derivation (and labeled) trees
• Data

  • Package Redwoods 9th growth or later

  • Provide CorpusReader for [incr tsdb()] profiles

• Processors
  • ACE interface
  • RESTful client

We should see if NLTK's DependencyGraph or FeatureStructure classes can be used for the data representations.

There are some non-programming tasks that need to be done, as well.

• Contact the NLTK maintainers (Ewan Klein, Liling Tan, or the nltk-devs mailinglist)
  • if our plans are appropriate for the NLTK (or some subset of them)
  • how to proceed with implementations
• Provide unit tests
• Write or collaborate on writing new book sections for the functionality

• We have several Python implementations (see pyDelphin or pyDMRS), but can we drop that code in directly, or should we refactor based on NLTK's base classes?