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CompNet · May 13, 2024 · aeda5fc · aeda5fc
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diff --git a/_images/pp.svg b/_images/pp.svg
diff --git a/_sources/introduction.rst.txt b/_sources/introduction.rst.txt
@@ -22,3 +22,16 @@ networks and their automated extraction.
 
 .. _LabatutAndBost2019: https://arxiv.org/abs/1907.02704
 
+
+Target Audience and Intended Usage
+==================================
+
+Renard can be used by digital humanities researchers to extract
+character networks, for example to bring additional information when
+performing literary analysis.
+
+Renard can also be used by Natural Language Processing (NLP)
+researchers and practitioners to extract graph representations of
+texts, in order to use these as inputs to downstream NLP tasks. Graphs
+embedding algorithms such as Graph2Vec may be used on these graphs to
+derive a vectorial representation of texts.
diff --git a/_sources/reference.rst.txt b/_sources/reference.rst.txt
@@ -184,3 +184,10 @@ Plot utils
 
 .. automodule:: renard.plot_utils
    :members:
+
+
+NER utils
+---------
+
+.. automodule:: renard.ner_utils
+   :members:
diff --git a/genindex.html b/genindex.html
@@ -98,9 +98,11 @@ <h1 id="index">Index</h1>
 <h2 id="_">_</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="reference.html#renard.pipeline.character_unification.GraphRulesCharacterUnifier.__call__">__call__() (renard.pipeline.character_unification.GraphRulesCharacterUnifier method)</a>
+      <li><a href="reference.html#renard.ner_utils.DataCollatorForTokenClassificationWithBatchEncoding.__call__">__call__() (renard.ner_utils.DataCollatorForTokenClassificationWithBatchEncoding method)</a>
 
       <ul>
+        <li><a href="reference.html#renard.pipeline.character_unification.GraphRulesCharacterUnifier.__call__">(renard.pipeline.character_unification.GraphRulesCharacterUnifier method)</a>
+</li>
         <li><a href="reference.html#renard.pipeline.character_unification.NaiveCharacterUnifier.__call__">(renard.pipeline.character_unification.NaiveCharacterUnifier method)</a>
 </li>
         <li><a href="reference.html#renard.pipeline.core.Pipeline.__call__">(renard.pipeline.core.Pipeline method)</a>
@@ -154,9 +156,13 @@ <h2 id="_">_</h2>
         <li><a href="reference.html#renard.pipeline.ner.NEREntity.__hash__">(renard.pipeline.ner.NEREntity method)</a>
 </li>
       </ul></li>
-      <li><a href="reference.html#renard.pipeline.character_unification.Character.__init__">__init__() (renard.pipeline.character_unification.Character method)</a>
+      <li><a href="reference.html#renard.ner_utils.DataCollatorForTokenClassificationWithBatchEncoding.__init__">__init__() (renard.ner_utils.DataCollatorForTokenClassificationWithBatchEncoding method)</a>
 
       <ul>
+        <li><a href="reference.html#renard.ner_utils.NERDataset.__init__">(renard.ner_utils.NERDataset method)</a>
+</li>
+        <li><a href="reference.html#renard.pipeline.character_unification.Character.__init__">(renard.pipeline.character_unification.Character method)</a>
+</li>
         <li><a href="reference.html#renard.pipeline.character_unification.GraphRulesCharacterUnifier.__init__">(renard.pipeline.character_unification.GraphRulesCharacterUnifier method)</a>
 </li>
         <li><a href="reference.html#renard.pipeline.character_unification.NaiveCharacterUnifier.__init__">(renard.pipeline.character_unification.NaiveCharacterUnifier method)</a>
@@ -243,6 +249,8 @@ <h2 id="_">_</h2>
       <li><a href="reference.html#renard.pipeline.core.Pipeline._pipeline_init_steps">_pipeline_init_steps() (renard.pipeline.core.Pipeline method)</a>
 </li>
       <li><a href="reference.html#renard.pipeline.corefs.SpacyCorefereeCoreferenceResolver._spacy_try_infer_spaces">_spacy_try_infer_spaces() (renard.pipeline.corefs.SpacyCorefereeCoreferenceResolver static method)</a>
+</li>
+      <li><a href="reference.html#renard.ner_utils._tokenize_and_align_labels">_tokenize_and_align_labels() (in module renard.ner_utils)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -307,6 +315,10 @@ <h2 id="C">C</h2>
 
 <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference.html#renard.ner_utils.DataCollatorForTokenClassificationWithBatchEncoding">DataCollatorForTokenClassificationWithBatchEncoding (class in renard.ner_utils)</a>
+</li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference.html#renard.graph_utils.dynamic_graph_to_gephi_graph">dynamic_graph_to_gephi_graph() (in module renard.graph_utils)</a>
 </li>
@@ -347,6 +359,10 @@ <h2 id="G">G</h2>
 
 <h2 id="H">H</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference.html#renard.ner_utils.hgdataset_from_conll2002">hgdataset_from_conll2002() (in module renard.ner_utils)</a>
+</li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference.html#renard.resources.hypocorisms.HypocorismGazetteer">HypocorismGazetteer (class in renard.resources.hypocorisms)</a>
 </li>
@@ -365,6 +381,10 @@ <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference.html#renard.graph_utils.layout_with_names">layout_with_names() (in module renard.graph_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference.html#renard.ner_utils.load_conll2002_bio">load_conll2002_bio() (in module renard.ner_utils)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -379,6 +399,8 @@ <h2 id="M">M</h2>
 
       <ul>
         <li><a href="reference.html#module-renard.graph_utils">renard.graph_utils</a>
+</li>
+        <li><a href="reference.html#module-renard.ner_utils">renard.ner_utils</a>
 </li>
         <li><a href="reference.html#module-renard.pipeline.preprocessing">renard.pipeline.preprocessing</a>
 </li>
@@ -433,6 +455,10 @@ <h2 id="N">N</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference.html#renard.ner_utils.ner_entities">ner_entities() (in module renard.ner_utils)</a>
+</li>
+      <li><a href="reference.html#renard.ner_utils.NERDataset">NERDataset (class in renard.ner_utils)</a>
+</li>
       <li><a href="reference.html#renard.pipeline.ner.NEREntity">NEREntity (class in renard.pipeline.ner)</a>
 </li>
       <li><a href="reference.html#renard.pipeline.ner.NLTKNamedEntityRecognizer">NLTKNamedEntityRecognizer (class in renard.pipeline.ner)</a>
@@ -534,6 +560,13 @@ <h2 id="R">R</h2>
 
       <ul>
         <li><a href="reference.html#module-renard.graph_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    renard.ner_utils
+
+      <ul>
+        <li><a href="reference.html#module-renard.ner_utils">module</a>
 </li>
       </ul></li>
       <li>
@@ -543,15 +576,15 @@ <h2 id="R">R</h2>
         <li><a href="reference.html#module-renard.pipeline.preprocessing">module</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     renard.pipeline.stanford_corenlp
 
       <ul>
         <li><a href="reference.html#module-renard.pipeline.stanford_corenlp">module</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     renard.plot_utils
 

diff --git a/index.html b/index.html
@@ -79,7 +79,10 @@ <h1>Welcome to Renard’s documentation!<a class="headerlink" href="#welcome-to-
 <div class="toctree-wrapper compound">
 <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="introduction.html">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction.html">Introduction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="introduction.html#target-audience-and-intended-usage">Target Audience and Intended Usage</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html#using-pip">Using Pip</a></li>
 <li class="toctree-l2"><a class="reference internal" href="installation.html#manual-installation">Manual Installation</a></li>

diff --git a/introduction.html b/introduction.html
@@ -43,7 +43,10 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Introduction</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Introduction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#target-audience-and-intended-usage">Target Audience and Intended Usage</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="pipeline.html">The Pipeline</a></li>
 <li class="toctree-l1"><a class="reference internal" href="extending.html">Extending Renard</a></li>
@@ -91,7 +94,18 @@ <h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 </ul>
 <p>See <a class="reference external" href="https://arxiv.org/abs/1907.02704">LabatutAndBost2019</a> for a more complete overview of character
 networks and their automated extraction.</p>
-<img alt="pp.svg" src="pp.svg" /></section>
+<img alt="_images/pp.svg" src="_images/pp.svg" /><section id="target-audience-and-intended-usage">
+<h2>Target Audience and Intended Usage<a class="headerlink" href="#target-audience-and-intended-usage" title="Permalink to this headline"></a></h2>
+<p>Renard can be used by digital humanities researchers to extract
+character networks, for example to bring additional information when
+performing literary analysis.</p>
+<p>Renard can also be used by Natural Language Processing (NLP)
+researchers and practitioners to extract graph representations of
+texts, in order to use these as inputs to downstream NLP tasks. Graphs
+embedding algorithms such as Graph2Vec may be used on these graphs to
+derive a vectorial representation of texts.</p>
+</section>
+</section>
 
 
            </div>

diff --git a/objects.inv b/objects.inv
diff --git a/py-modindex.html b/py-modindex.html
@@ -96,6 +96,11 @@ <h1>Python Module Index</h1>
        <td>&#160;&#160;&#160;
        <a href="reference.html#module-renard.graph_utils"><code class="xref">renard.graph_utils</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="reference.html#module-renard.ner_utils"><code class="xref">renard.ner_utils</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;