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 f2e04a9 WIP.
f2e04a9 is described below
commit f2e04a9cd828810dec2271e779e545f0fb810982
Author: Aaron Radzinski <[email protected]>
AuthorDate: Tue Mar 30 19:55:04 2021 -0700
WIP.
---
_scss/index.scss | 2 +-
_scss/three-cols.scss | 2 +-
intent-matching.html | 28 ++++++++++++++++------------
3 files changed, 18 insertions(+), 14 deletions(-)
diff --git a/_scss/index.scss b/_scss/index.scss
index 0e2162b..1c9d94f 100644
--- a/_scss/index.scss
+++ b/_scss/index.scss
@@ -28,7 +28,7 @@
#banner {
min-height: 300px;
padding: 35px 0 0 40px;
-N
+
a#get-started-btn {
border-color: #c17dac !important;
background-color: #fff !important;
diff --git a/_scss/three-cols.scss b/_scss/three-cols.scss
index 471840d..f370de0 100644
--- a/_scss/three-cols.scss
+++ b/_scss/three-cols.scss
@@ -137,7 +137,7 @@
}
}
- margin-bottom: 40px;
+ margin-bottom: 30px;
p, li {
letter-spacing: 0.02em;
diff --git a/intent-matching.html b/intent-matching.html
index f33b537..abcddc7 100644
--- a/intent-matching.html
+++ b/intent-matching.html
@@ -25,28 +25,32 @@ id: intent_matching
<section id="overview">
<h2 class="section-title">Overview</h2>
<p>
- <a href="/data-model.html#logic">Data Model</a> processing logic
is defined as a collection of intents. The sections
- below will explain what intent is and how to define it in your
model.
+ <a href="/data-model.html#logic">Data Model</a> processing logic
is defined as a collection of one or more intents. The sections
+ below explain what intent is, how to define it in your model, and
how it works.
</p>
</section>
<section id="matching">
<h2 class="section-title">Intent</h2>
<p>
- The main goal of the data model is to take the input utterance and
- match it to a piece of user-defined code that will execute for
that particular input. The mechanism that
+ 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>.
</p>
<p>
- The intent is a <em>declarative template</em> of an input
utterance written in <a href="#idl">Intent Definition Language</a>.
- Intent is <a href="#annotations">bound</a> to a callback method
that will be called when such 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.
+ The intent is a <em>declarative 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 called when that intent 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 differently to.
</p>
<p>
- Intents can be specific and or generic in terms of what user input
they match.
- Multiple intents can overlap and NLPCraft will
- disambiguate such cases to select the intent with the overall best
match. In general, the most specific
- intent match wins.
+ For example, a banking application data model can have multiple
intents for each domain-specific group of utterances such as
+ opening an account, closing an account, transferring money,
getting statements, etc.
+ </p>
+ <p>
+ Intents can be specific or generic in terms of what input they
match.
+ Multiple intents can overlap and NLPCraft will disambiguate such
cases to select the intent with the
+ overall best match. In general, the most specific intent match
wins.
</p>
</section>
<section id="idl">
