This is an automated email from the ASF dual-hosted git repository.
aradzinski pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-nlpcraft-website.git
The following commit(s) were added to refs/heads/master by this push:
new 4130861 WIP.
4130861 is described below
commit 4130861045fe9327b56bb496be38efa72a24116d
Author: Aaron Radzinski <[email protected]>
AuthorDate: Sun May 9 11:31:55 2021 -0700
WIP.
---
docs.html | 10 +++++-----
intent-matching.html | 8 ++++----
2 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/docs.html b/docs.html
index 161386a..18f3806 100644
--- a/docs.html
+++ b/docs.html
@@ -25,15 +25,15 @@ id: overview
<section id="overview">
<h2 class="section-title">Overview <a href="#"><i class="top-link fas
fa-fw fa-angle-double-up"></i></a></h2>
<p>
- Apache NLPCraft is a Java-based <a target=_blank
href="https://www.apache.org/licenses/";>open source</a> library
+ Apache NLPCraft is a JVM-based <a target=_blank
href="https://www.apache.org/licenses/";>open source</a> library
for adding a natural language interface to modern applications.
It enables people to interact with your products using voice or text. NLPCraft
can connect with
any private or public data source, and has no hardware or software
lock-ins. Its design is based on advanced
<a href="/intent-matching.html">Intent Definition Language</a>
(IDL) for defining non-trivial intents and a fully deterministic intent matching
algorithm for the input utterances. You can build intents for
NLPCraft using any JVM-based languages like Java, Scala, Kotlin, Groovy, etc.
NLPCraft
- exposes REST APIs for integration with any of your applications.
+ exposes REST APIs for integration with end-user applications.
</p>
<p>
- One of the key features of NLPCraft is its use of IDL coupled with
deterministic intent matching that are tailor made for
+ One of the key features of NLPCraft is its use of <a
href="/intent-matching.html">IDL</a> coupled with deterministic intent matching
that are tailor made for
<em>domain-specific</em> natural language interface. This design
doesn't force developers to use direct deep learning
approach with time consuming corpora development and model
training - resulting in much a
<em>simpler <span class="amp">&</span> faster</em> implementation.
@@ -65,14 +65,14 @@ id: overview
<section id="data-model">
<h2 class="section-title">Data Model <a href="#"><i class="top-link
fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
- NLPCraft employs model-as-a-code approach where everything you do
in NLPCraft is part of your source code. Data model is an implementation of
+ NLPCraft employs model-as-a-code approach where everything you do
in NLPCraft is part of your source code. Data model is simply an implementation
of
<a target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCModel.html">NCModel</a> Java
interface that
can be developed using any JVM programming language like Java,
Scala, Kotlin or Groovy.
Data model defines named entities, various configuration
properties as well as intents to interpret user input. Model-as-a-code natively
supports
any software lifecycle tools and frameworks in Java ecosystem.
</p>
<p>
- Typically, declarative portion of the model will be stored in a
separate JSON or YAML file
+ Declarative portion of the model can be stored in a separate JSON
or YAML file
for simpler maintenance. There are no practical limitation on how
complex or simple a model
can be, or what other tools it can use. Data models use <a
href="/intent-matching.html">intents</a> to match the user input.
</p>
diff --git a/intent-matching.html b/intent-matching.html
index 572cbc3..5ce5044 100644
--- a/intent-matching.html
+++ b/intent-matching.html
@@ -34,13 +34,13 @@ id: intent_matching
<p>
The goal of the data model implementation is to take the input
utterance and
match it to a specific user-defined code that will execute for
that input. The mechanism that
- provides this match between the input utterance and the
user-defined code is called an <em>intent</em>.
+ provides this matching is called an <em>intent</em>.
</p>
<p>
The intent refers to the goal that the end-user had in mind when
speaking or typing the input utterance.
The intent has a <em>declarative part or template</em> written in
<a href="#idl">Intent Definition Language</a> that describes
a particular form or type of the input utterance.
- Intent is also <a href="#annotations">bound</a> to a callback
method that will be executed when that intent, i.e. its template, is detected
as the best match
+ Intent is also <a href="#binding">bound</a> to a callback method
that will be executed when that intent, i.e. its template, is detected as the
best match
for a given input utterance. A typical data model will have
multiple intents defined for each form of the expected user input
that model wants to react to.
</p>
@@ -67,7 +67,7 @@ id: intent_matching
<p>
IDL intent defines a match between the parsed input utterance,
i.e. the collection of
<a class="not-code" target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">tokens</a>,
- and the user-define callback method. To accomplish that, IDL <a
href="#idl_functions">functions</a> provide
+ and the user-define callback method. To accomplish that, IDL
through its <a href="#idl_functions">functions</a> provides
access to the following information:
</p>
<ul>
@@ -550,7 +550,7 @@ id: intent_matching
intent=id2
flow='id1 id2'
term={tok_id() == 'mytok' && signum(get(meta_tok('score'),
'best')) != -1}
- term={has_all(tok_groups(), list('actors', 'owners')) &&
size(meta_part('partAlias, 'text')) > 10}
+ term={has_any(tok_groups(), list('actors', 'owners')) &&
size(meta_part('partAlias, 'text')) > 10}
</pre>
<p><b>NOTES:</b></p>
<ul>
