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The document discusses query formulation approaches for similarity search. It describes term extraction and topic extraction methods. Term extraction involves using algorithms like TF-IDF and RIDF to automatically extract relevant terms from a given corpus. Topic extraction uses topic models to cluster words that frequently occur together and connect words with similar meanings. MALLET and MAUI are tools mentioned for topic modeling, with MALLET providing efficient topic inference and MAUI identifying significant topics in documents.

Technology Education

IST 441 : Progress Report
Query Formulation for Similarity
Search
Student : Nitish Upreti
Customer : Kyle Williams
nzu100@cse.psu.edu
kwilliams@psu.edu

What is Similarity Search?
• Given a sample document and a standard Web
search engine, the goal is to find similar
documents.
• Multiple Applications of Similarity Search :
Plagiarism Detection
Process of locating instances of plagiarism in a
suspicious document from the web.
Research Paper Recommendation
Finding relevant documents for research paper
recommendation.

Query Formulation Approach
Term Extraction
(Automatic extraction of relevant terms from a given corpus)

Enter JateToolkit
Java Automatic Term Extraction toolkit
A library of state-of-the-art term extraction
algorithms and framework for developing term
extraction algorithms.
https://code.google.com/p/jatetoolkit/

Approaches for Query Formulation
• Term Extraction Algorithms :
– TF-IDF
– RIDF
– Weirdness
– C-value
– GlossEx
– TermEx
(Alpha Phase)
– Justeson & Katz Algorithm
– NC Value Algorithm
– Rake Algorithm
– Chi-squared Algorithm

Query Formulation Approach
Topic Extraction

Topic Extraction
• Topic models provide a simple way to analyze
large volumes of unlabeled text.
• A "topic" consists of a cluster of words that
frequently occur together.
• Using contextual clues, topic models can
connect words with similar meanings and
distinguish between uses of words with
multiple meanings.

MALLET + MAUI
• The MALLET topic model package includes an
extremely fast and efficient methods for
document topic hyper parameter optimization,
and tools for inferring topics for new documents
given trained models.
• MAUI uses candidate generation algorithms to
identify topics in a given document and then
filtering, analyzing the properties, or features, of
the candidate topics and filtering out the most
significant ones.

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Project progress

1. IST 441 : Progress Report Query Formulation for Similarity Search Student : Nitish Upreti Customer : Kyle Williams nzu100@cse.psu.edu kwilliams@psu.edu

2. What is Similarity Search? • Given a sample document and a standard Web search engine, the goal is to find similar documents. • Multiple Applications of Similarity Search : Plagiarism Detection Process of locating instances of plagiarism in a suspicious document from the web. Research Paper Recommendation Finding relevant documents for research paper recommendation.

3. Query Formulation Approach Term Extraction (Automatic extraction of relevant terms from a given corpus)

4. Enter JateToolkit Java Automatic Term Extraction toolkit A library of state-of-the-art term extraction algorithms and framework for developing term extraction algorithms. https://code.google.com/p/jatetoolkit/

5. Approaches for Query Formulation • Term Extraction Algorithms : – TF-IDF – RIDF – Weirdness – C-value – GlossEx – TermEx (Alpha Phase) – Justeson & Katz Algorithm – NC Value Algorithm – Rake Algorithm – Chi-squared Algorithm

6. Continuing On…

7. Query Formulation Approach Topic Extraction

8. Topic Extraction • Topic models provide a simple way to analyze large volumes of unlabeled text. • A "topic" consists of a cluster of words that frequently occur together. • Using contextual clues, topic models can connect words with similar meanings and distinguish between uses of words with multiple meanings.

9. MALLET + MAUI • The MALLET topic model package includes an extremely fast and efficient methods for document topic hyper parameter optimization, and tools for inferring topics for new documents given trained models. • MAUI uses candidate generation algorithms to identify topics in a given document and then filtering, analyzing the properties, or features, of the candidate topics and filtering out the most significant ones.

10. THANK YOU!

Project progress

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