Topic Modeling and Semantic Search

Marijn Koolen


Week 2, 8 February 2016


  1. Semantic Search
    • Semantics in Data and Search Queries
    • Challenges of Big Data
  2. Topic Modelling
    • What are they?
    • Why use them?
    • How do they work?

Administrative Stuff

Part 1: Semantic Search

Meaning and Interpretation

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Meaning and Interpretation

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Knowledge Bases

Semantic Data

Semantic Web

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Resource Description Framework

Semantic Qeuries?

Difficult Information Needs

Queries with Structure

SELECT contract FROM uva_staff WHERE (contract=temp) SET contract=permanent

SPARQL Example 1/4

SELECT ?affiliation WHERE { dbr:Darth_Vader dbp:affiliation ?affiliation }

SPARQL Example 2/4

SELECT ?person WHERE { ?person dbp:affiliation dbr:Sith}

SPARQL Example 3/4

SELECT ?person ?affiliation WHERE { ?person dbp:affiliation ?affiliation }

SPARQL Example 4/4

SELECT count(?person) WHERE { ?person dbp:affiliation dbr:Sith }



Search and Big Data

Semantics and Big Data



Part 2a: Topic Models

What are topic models?

Topic Modelling

Statistical Structure in Language

Statistical Structure in Language

Topic Models

Illustration 1

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Illustration 2

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Statistical Modeling

Topic Modelling Software

Part 2b: Why Use Topic Models?

relevance for research

Suggestive Patterns

Topic Modelling in Digital Humanities

KB Newspaper Archive

Examples with KB Newspaper Archive

Topics in Newspapers

Interpreting Topics

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Part 2c: How Do Topic Models Work?

the technical details

Two Parts of Technicalities


1. Preprocessing Text

2. Indexing Text

2a. Text as Vectors

2b. Inverted Index

Parsing & Units of Data


3. Modeling

Semantic Relatedness

Frequency vs. Importance


4. Analysing Compositions

Topic Models in Literary Studies

Considerations 1/2

Considerations 2/2

Narrative Topic Models

Familiarity with Corpus

Lies, Damned Lies and Statistics


Other Considerations

Mixing Languages

Beyond Text

"This is a case where I'm really being saved by the restrictive feature space of data. If I were interpreting these MALLET results as text, I might notice it, for example, but start to tell a just-so story about how transatlantic shipping and Pacific whaling really are connected. (Which they are; but so is everything else.) The absurdity of doing that with geographic data like this is pretty clear; but interpretive leaps are extraordinarily easy to make with texts."

Sub-Topic Modelling

Experimenting with STM

Control over Topics

Topic Modelling Summary