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<html><head><base href="http://www.aikm.ai" />
<title>Machine Learning Institute for Enterprise Knowledge Management</title>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
body {
font-family: Arial, sans-serif;
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max-width: 1200px;
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padding: 20px;
}
header {
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color: white;
text-align: center;
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background-color: #f4f4f4;
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</head>
<body>
<header>
<h1>AIKm Research</h1>
<p>Empowering Enterprises through Advanced ML Solutions</p>
</header>
<nav>
<ul>
<li><a href="http://www.aikm.ai">Home</a></li>
<li><a href="#about">About</a></li>
<li><a href="#services">Services</a></li>
<li><a href="#research">Research</a></li>
<li><a href="#contact">Contact</a></li>
</ul>
</nav>
<body link=blue vlink=purple style='tab-interval:36.0pt;word-wrap:break-word'>
<div class=WordSection1>
<h1><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>Natural Language Processing for Knowledge Extraction<o:p></o:p></span></h1>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>Unlocking the
Power of Unstructured Text Data in Enterprises<o:p></o:p></span></p>
<!--
<ul type=disc>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
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style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'><a
href="http://www.aikm.ai"></header><nav>Home</a><o:p></o:p></span></li>
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href="http://www.aikm.ai/research">Research</a><o:p></o:p></span></li>
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href="http://www.aikm.ai/team">Team</a><o:p></o:p></span></li>
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href="http://www.aikm.ai/contact">Contact</a><o:p></o:p></span></li>
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<!--<h2><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'></nav><main>Overview<o:p></o:p></span></h2>-->
<h2>Overview</h2>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>At AIKm, our
Natural Language Processing (NLP) research focuses on developing advanced
techniques to automatically extract valuable knowledge from unstructured text
data. This includes documents, emails, social media content, and other
text-based sources within enterprise environments.<o:p></o:p></span></p>
<div style='border:none;border-left:solid #0066CC 4.5pt;padding:0cm 0cm 0cm 11.0pt;
margin-bottom:15.0pt'>
<h3 style='background:aliceblue'><span style='font-family:"Arial",sans-serif;
mso-fareast-font-family:"Times New Roman";color:#333333'>Key Research Areas<o:p></o:p></span></h3>
<ul type=disc>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
auto;mso-list:l0 level1 lfo2;tab-stops:list 36.0pt;background:aliceblue'><span
style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'>Named
Entity Recognition (NER) for enterprise-specific entities<o:p></o:p></span></li>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
auto;mso-list:l0 level1 lfo2;tab-stops:list 36.0pt;background:aliceblue'><span
style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'>Relation
Extraction to identify connections between entities<o:p></o:p></span></li>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
auto;mso-list:l0 level1 lfo2;tab-stops:list 36.0pt;background:aliceblue'><span
style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'>Text
Summarization for efficient information digestion<o:p></o:p></span></li>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
auto;mso-list:l0 level1 lfo2;tab-stops:list 36.0pt;background:aliceblue'><span
style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'>Sentiment
Analysis for customer feedback and internal communications<o:p></o:p></span></li>
<li class=MsoNormal style='color:#333333;mso-margin-top-alt:auto;mso-margin-bottom-alt:
auto;mso-list:l0 level1 lfo2;tab-stops:list 36.0pt;background:aliceblue'><span
style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman"'>Topic
Modeling to organize large document collections<o:p></o:p></span></li>
</ul>
</div>
<h2><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>Current Projects<o:p></o:p></span></h2>
<h3><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>1. Enterprise-Specific Named Entity Recognition<o:p></o:p></span></h3>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>We're developing
customizable NER models that can be easily adapted to recognize domain-specific
entities in various industries. This project aims to improve the accuracy of
entity extraction in specialized enterprise contexts.<o:p></o:p></span></p>
<div style='border:solid #DDDDDD 1.0pt;mso-border-alt:solid #DDDDDD .75pt;
padding:11.0pt 11.0pt 11.0pt 11.0pt'>
<p class=MsoNormal style='background:#F4F4F4'><span style='font-family:"Courier New";
mso-fareast-font-family:"Times New Roman";color:#333333'># Example of using Enterprise
NER model</span><span lang=EN-HK style='font-family:"Courier New";mso-fareast-font-family:
"Times New Roman";color:#333333;mso-ansi-language:EN-HK'><o:p></o:p></span></p>
<p class=MsoNormal style='background:#F4F4F4'><span style='font-family:"Courier New";
mso-fareast-font-family:"Times New Roman";color:#333333'>from ml_ekm import
EnterpriseNER ner_model = EnterpriseNER.load("finance_model") text =
"ACME Corp reported Q2 earnings of $2.5B, exceeding analyst
expectations." entities = ner_model.extract_entities(text) print(entities)
# Output: [('ACME Corp', 'ORG'), ('Q2', 'TIME'), ('$2.5B', 'MONEY')] <o:p></o:p></span></p>
</div>
<h3><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>2. Intelligent Document Summarization<o:p></o:p></span></h3>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>Our team is
working on advanced text summarization techniques that can distill key
information from lengthy documents, reports, and email threads. This research
aims to save time and improve information retention for busy professionals.<o:p></o:p></span></p>
<h3><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>3. Cross-lingual Knowledge Extraction<o:p></o:p></span></h3>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>As enterprises
become increasingly global, we're developing methods to extract and align
knowledge across multiple languages. This project focuses on creating a unified
knowledge base from multilingual sources.<o:p></o:p></span></p>
<h2><span style='font-family:"Arial",sans-serif;mso-fareast-font-family:"Times New Roman";
color:#333333'>Collaborate with Us<o:p></o:p></span></h2>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>We're always
looking for industry partners to collaborate on real-world NLP applications. If
you're interested in applying our research to your enterprise or contributing
to our ongoing projects, we'd love to hear from you.<o:p></o:p></span></p>
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style='color:white;background:#0066CC;text-decoration:none;text-underline:none'>Explore
Collaboration Opportunities</span></b></a> <o:p></o:p></span></p>-->
</main>
<!--<footer>
<p><span style='font-family:"Arial",sans-serif;color:#333333'>© 2024 Machine
Learning Institute for Enterprise Knowledge Management. All rights reserved.<o:p></o:p></span></p>
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<p>© 2024 Machine Learning Institute for Enterprise Knowledge Management, AIKm. All rights reserved. Email: [email protected] </p>
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