Configure the Stanford CoreNLP parser.

This provides a plugin architecture for natural language processing tasks in a
pipeline.  A parser takes either an human language utterance or a previously
annotated data parsed from an utterance.


### Parser Libraries

Each parser provides a set of *components* that make up the pipeline.  Each
component (i.e. [[tokenize]]) is a function that returns a map including a map
containing keys:

* **component** a key that's the name of the component to create.
* **parser** a key that is the name of the parser it belongs to.

For example, the Stanford CoreNLP word tokenizer has the following return map:

* **:component** :tokenize
* **:lang**  *lang-code* (e.g. `en`)
* **:parser** :stanford

The map also has additional key/value pairs that represent remaining
configuration given to the parser library used to create it's pipeline
components.  All parse library names (keys) are given in [[all-parsers]].

Use [[register-library]] to add your library with the key name of your parser.


### Usage

You can either create your own custom parser configuration
with [[create-parse-config]] and then create it's respective context
with [[create-context]].  If you do this, then each parse call needs to be in
a [[with-context]] lexical context.  If you don't, a default context is created
and used for each parse invocation.

Once/if configured, use [[zensols.nlparse.parse/parse]] to invoke the parsing
pipeline.

zensols.nlparse.config/tokenize("en"),zensols.nlparse.config/sentence,part-of-speech("english.tagger"),zensols.nlparse.config/morphology

parse-tree({:use-shift-reduce? true :maxtime 1000})

example.namespace/myfunc(arg1,arg2)

Parse a pipeline configruation.  This namespace supports a simple
DSL for parsing a pipeline configuration (see [[zensols.nlparse.config]]).  The
*configuration string* represents is a component separated by commas as a set
of *forms*.  For example the forms:
```
zensols.nlparse.config/tokenize("en"),zensols.nlparse.config/sentence,part-of-speech("english.tagger"),zensols.nlparse.config/morphology
```
creates a pipeline that tokenizes, adds POS and lemmas when called
with [[parse]].  Note the double quotes in the `tokenize` and `part-of-speech`
mnemonics.  The [[parse]] function does this by calling in order:

* ([[zensols.nlparse.config/tokenize]] "en")
* ([[zensols.nlparse.config/sentence]])
* ([[zensols.nlparse.config/part-of-speech]] "english.tagger")
* ([[zensols.nlparse.config/morphology]])

Soem configuration functions are parameterized by positions or maps.
Positional functions are shown in the above example and a map configuration
follows:
```
parse-tree({:use-shift-reduce? true :maxtime 1000})
```
which creates a shift reduce parser that times out after a second (per
sentence).

Note that arguments are option (the parenthetical portion of the form) and so
is the namespace, which defaults to `zensols.nlparse.config`.  To use a
separate namespace for custom plug and play To use a separate namespace for
custom plug and play
components (see [[zensols.nlparse.config/register-library]]) you can specify
your own namespace with a `/`, for example:
```
example.namespace/myfunc(arg1,arg2)
```

Feature utility functions.  In this library, all references to
`panon` stand for *parsed annotation*, which is returned
from [[zensols.nlparse.parse/parse]].

Feature utility functions.  See [[zensols.nlparse.feature.lang]].

Parse an utterance using the Stanford CoreNLP and the ClearNLP SRL.

This is the main client entry point to the package.  A default out of the box
parser works that comes with components listed
in [[zensols.nlparse.config/all-components]].

If you want to customzie or add your own parser plug in, see
the [[zensols.nlparse.config]] namespace.

Configure environment for the NLP pipeline.

Wrap ClearNLP SRL.

Currently the propbank trained version is used.  The main classification
function is [[label]].

Wraps the Stanford CoreNLP parser.

This namesapce provides ways of filtering *stop word* tokens.

To avoid the double negative in function names, *go words* are defined to be
the compliment of a vocabulary with a stop word list.  Functions
like [[go-word?]] tell whether or not a token is a stop word, which are
defined to be:

  * stopwords (predefined list)
  * punctuation
  * numbers
  * non-alphabetic characters

This namespace extends the NER system to easily add any regular
expression using the [Stanford
TokensRegex](http://nlp.stanford.edu/software/tokensregex.html) API.

This takes a sequence of regular expressions and entity metadata as input and
produces a file format the TokensRegex API consumes to tag entities.

[This](https://github.com/plandes/clj-nlp-parse/blob/v0.0.11/test-resources/token-regex.txt)
is an example of the output.

Utility functions