---
active_crumb: Docs
layout: documentation
id: built-in-token-parser
---

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<div class="col-md-8 second-column">
    <section id="overview">
        <h2 class="section-title">Built-in Token Parser<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>

        <p>
            <a href="apis/latest/org/apache/nlpcraft/NCTokenParser.html">NCTokenParser</a>
            component implementation should parse user input plain text and split this text
            into <code>tokens</code> list.

            NLPCraft provides default English language implementation of token parser.

            Also, project contains examples for <a href="examples/light_switch_fr.html">French</a> and
            <a href="examples/light_switch_ru.html">Russia</a> languages token parser implementations.
        </p>

    </section>

    <section id="parser-opennlp">
        <h2 class="section-title">Apache OpenNLP Based Parser<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            There is <a href="apis/latest/org/apache/nlpcraft/nlp/parsers/NCOpenNLPTokenParser.html">NCOpenNLPTokenParser</a> implementation.

            It is token parser implementation which is wrapper on
            <a href="https://opennlp.apache.org/">Apache OpenNLP</a> project tokenizer.
            </p>
    </section>

    <section id="parser-stanford">
        <h2 class="section-title">Stanford NLP Based Parser<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
        <p>
            There is <a href="NCStanfordNLPTokenParser.html">NCStanfordNLPTokenParser</a> implementation.

            It is token parser implementation which is wrapper on
            <a href="https://nlp.stanford.edu/">Stanford NLP</a> project tokenizer.
        </p>
    </section>
    <section id="remarks">
        <h2 class="section-title">Remarks<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>

        <p>
            There are two built-in token parsers added for one English language because they have some difference
            in their algorithm and can provide different list of tokens for same user text input.
            Some built-in components are required token parser instance as their parameter.

        </p>
        <ul>
            <li>
                If you use <a href="https://opennlp.apache.org/">Apache OpenNLP</a> based components
                you should use <a href="#parser-opennlp">Apache OpenNLP based parser</a> in your model pipeline.
            </li>
            <li>
                If you use <a href="https://nlp.stanford.edu/">Stanford NLP</a> based components
                you should use <a href="#parser-stanford">Stanford based parser</a> in your model pipeline.
            </li>
        </ul>
    </section>
</div>
<div class="col-md-2 third-column">
    <ul class="side-nav">
        <li class="side-nav-title">On This Page</li>
        <li><a href="#overview">Overview</a></li>
        <li><a href="#parser-opennlp">Apache OpenNLP Based Parser</a></li>
        <li><a href="#parser-stanford">Stanford NLP Based Parser</a></li>
        <li><a href="#remarks">Remarks</a></li>
        {% include quick-links.html %}
    </ul>
</div>