Page fixes.
diff --git a/intent-matching.html b/intent-matching.html
index 55f76ef..077327d 100644
--- a/intent-matching.html
+++ b/intent-matching.html
@@ -410,7 +410,7 @@
ctx: NCContext, im: NCIntentMatch, @NCIntentTerm("city") cityEnt: NCEntity
): NCResult =
...
- </pre><p> <b>NOTES:</b></p><ul><li> Intent is defined in the model, <code>line 2</code>.<li> This intent is referenced by annotation <code>@NCIntentRef("intent2")</code> with method <code>onMatch(...)</code> as its callback, <code>line 5</code>.<li> This example defines an intent with two conversational terms both of which have to found for the intent to match.<li> Terms is conversational if it uses <code>'~'</code> and non-conversational if it uses <code>'='</code> symbol in its definition. If term is conversational, the matching algorithm will look into the conversation context short-term-memory (STM) to seek the matching entities for this term. Note that the terms that were fully or partially matched using entities from the conversation context will contribute a smaller weight to the overall intent matching weight since these terms are <em>less specific.</em> Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.<li> Method <code>onMatch(...)</code> will be called when this intent is the best match detected.<li> Note that terms have <code>min=1, max=1</code> quantifiers by default.<li> First term is defined as a single mandatory (<code>min=1, max=1</code>) entity with ID <code>x:time</code> whose element is defined in the model.<li> Second term is defined as a single mandatory (<code>min=1, max=1</code>) entity with entity<code>opennlp:location</code>.<li> Given data model definition above the following sentences will be matched by this intent:<ul><li><code>What time is it now in New York City?</code><li><code>Show me time of the day in London.</code><li><code>Can you please give me the Tokyo's current date and time.</code></ul></ul></section><section id="logic"><h2 class="section-title">Intent Matching Logic <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2><p> <a target="scaladoc" href="/apis/latest/org/apache/nlpcraft/NCPipeline">NCPipeline</a> processing result is collection of <a target="scaladoc" href="/apis/latest/org/apache/nlpcraft/NCVariant">NCVariant</a> instances. As example let's consider the input text <code>'A B C D'</code> and the following elements defined in our model:</p><pre class="brush: js">
+ </pre><p> <b>NOTES:</b></p><ul><li> Intent is defined in the model, <code>line 2</code>.<li> This intent is referenced by annotation <code>@NCIntentRef("intent2")</code> with method <code>onMatch(...)</code> as its callback, <code>line 5</code>.<li> This example defines an intent with two conversational terms both of which have to found for the intent to match.<li> Terms is conversational if it uses <code>'~'</code> and non-conversational if it uses <code>'='</code> symbol in its definition. If term is conversational, the matching algorithm will look into the conversation context short-term-memory (STM) to seek the matching entities for this term. Note that the terms that were fully or partially matched using entities from the conversation context will contribute a smaller weight to the overall intent matching weight since these terms are <em>less specific.</em> Non-conversational terms will be matched using entities found only in the current user input without looking at the conversation context.<li> Method <code>onMatch(...)</code> will be called when this intent is the best match detected.<li> Note that terms have <code>min=1, max=1</code> quantifiers by default.<li> First term is defined as a single mandatory (<code>min=1, max=1</code>) entity with ID <code>x:time</code> whose element is defined in the model.<li> Second term is defined as a single mandatory (<code>min=1, max=1</code>) entity with entity<code>opennlp:location</code>.<li> Given data model definition above the following sentences will be matched by this intent:<ul><li><code>What time is it now in New York City?</code><li><code>Show me time of the day in London.</code><li><code>Can you please give me the Tokyo's current date and time.</code></ul></ul></section><section id="logic"><h2 class="section-title">Intent Matching Logic <a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2><p> <a target="scaladoc" href="/apis/latest/org/apache/nlpcraft/NCPipeline">NCPipeline</a> processing result is collection of <a target="scaladoc" href="/apis/latest/org/apache/nlpcraft/NCVariant">NCVariant</a> instances. <a target="scaladoc" href="/apis/latest/org/apache/nlpcraft/nlp/parsers/NCSemanticEntityParser">NCSemanticEntityParser</a> is used for following example configured via <i>JSON</i> file. Let's consider the input text <code>'A B C D'</code> and the following elements defined in our model:</p><pre class="brush: js">
"elements": [
{
"id": "elm1",
