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Terms in Time and Times in Context: A Graph-based Term-Time Ranking - - PowerPoint PPT Presentation

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Terms in Time and Times in Context: A Graph-based Term-Time Ranking Model Andreas Spitz, Jannik Str otgen, Thomas B ogel and Michael Gertz Heidelberg University Institute of Computer Science Database Systems Research Group

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Terms in Time and Times in Context: A Graph-based Term-Time Ranking Model

Andreas Spitz, Jannik Str¨

  • tgen,

Thomas B¨

  • gel and Michael Gertz

Heidelberg University Institute of Computer Science Database Systems Research Group http://dbs.ifi.uni-heidelberg.de spitz@informatik.uni-heidelberg.de

5th Temporal Web Analytics Workshop Florence, May 18, 2015

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

What happened on June 15, 1215? A simple question. How simple is the answer?

Terms in Time and Times in Context Andreas Spitz 1 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

With structured data: quite simple Based on unstructured text data: much more challenging

Terms in Time and Times in Context Andreas Spitz 2 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Data Set and Approach

A corpus of all English Wikipedia articles:

  • Only text is considered, no info-boxes
  • 3, 079, 620 documents with time expressions

Problem statement, given such a corpus:

  • Extract and normalize temporal expressions (dates)
  • Find key terms that best summarize a given date

Terms in Time and Times in Context Andreas Spitz 3 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Outline

Outline of the approach:

  • Represent date-term co-occurrences efficiently
  • Extract and normalize temporal expressions (dates)
  • Extract content words that co-occur with dates
  • Generate an efficient data structure
  • Based on this representation
  • Identify relevant terms for any given date
  • Identify similar dates for any given date
  • Example applications

Terms in Time and Times in Context Andreas Spitz 4 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Temporal Expressions

  • Normalization, e.g., May 18, 2015 → 2015-05-18
  • Handling relative temporal expressions, e.g., in May
  • Considering the document type

Source: Str¨

  • tgen, Gertz Multilingual and Cross-domain Temporal Tagging (2013)

Terms in Time and Times in Context Andreas Spitz 5 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Coverage of Dates

We use a combination of dates of three granularities:

  • YYYY-MM-DD (day)
  • YYYY-MM (month)
  • YYYY (year)

Percentage of dates that are included in the data per year

  • ●
  • ●
  • ●
  • ●
  • ●
  • ●
  • 25

50 75 100 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

year coverage in %

Terms in Time and Times in Context Andreas Spitz 6 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Terms and Representation

For all sentences s in any Wikipedia document:

Terms in Time and Times in Context Andreas Spitz 7 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Terms and Representation

Identify/normalize dates and remove stop words

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Terms and Representation

Create a bipartite graph Gs = (Ts ∪ Ds, Es) with weights ωs

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Terms and Representation

Satisfy the inclusion condition for dates

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Extraction of Terms and Representation

Satisfy the inclusion condition for dates

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Graph aggregation

Aggregate the sentence-graphs Gs:

  • T := Ts
  • D := Ds
  • E := Es
  • ω(e) := ωs(e)

We obtain G = (T ∪ D, E, ω) with:

  • |T| = 3, 748, 730 terms
  • |D| = 210, 375 dates
  • |E| = 110, 639, 525 edges

Terms in Time and Times in Context Andreas Spitz 8 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Formalising the Question

What happened on June 15, 1215? Which terms in the graph co-occur in a significant manner with the date 1215-06-15?

Terms in Time and Times in Context Andreas Spitz 9 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Ranking

We need a ranking-function from dates D to a list of terms T

  • r : D → R|T|
  • r(d) := ranking of terms t ∈ T by their significance for d

Terms in Time and Times in Context Andreas Spitz 10 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Ranking

We need a ranking-function from dates D to a list of terms T

  • r : D → R|T|
  • r(d) := ranking of terms t ∈ T by their significance for d

Idea: adapt tf-idf to the bipartite graph tf-id f := tf · log 1 d f

  • tf : frequency of term in document
  • df : fraction of documents that contain the term

Terms in Time and Times in Context Andreas Spitz 10 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Adapting tf-idf

How does this relate to the graph?

  • Identify dates with documents, i.e., dates contain terms
  • Term frequency given by edge weights:

tf(d, t) ≈ ω(d, t)

  • Inverse document frequency given by number of neighbours:

id f(t) ≈

|D| deg(t)

tf-id f := tf · log 1 d f ⇒ tf-id f(d, t) := ω(d, t) log |D| deg(t)

Terms in Time and Times in Context Andreas Spitz 11 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

June 15, 1215

Terms in Time and Times in Context Andreas Spitz 12 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

June 15, 1215

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

A Ranking for Dates

Ranking dates by term works analogously: tf-id f(t, d) := ω(t, d) log |T| deg(d)

Terms in Time and Times in Context Andreas Spitz 13 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

A Ranking for Dates

Ranking dates by term works analogously: tf-id f(t, d) := ω(t, d) log |T| deg(d)

Terms in Time and Times in Context Andreas Spitz 13 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Ranking Nodes by Similarity Within a Set

Can we...

  • ... create a ranking for dates by dates?
  • ... or for terms by terms?

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Ranking Nodes by Similarity Within a Set

Can we...

  • ... create a ranking for dates by dates?
  • ... or for terms by terms?

Formally this is a one-mode projection of the bipartite graph:

  • Reduce graph to a single set of nodes T or D
  • Connect nodes that share neighbours in the bipartite graph
  • This results in a very dense graph

⇒ How can we identify relevant edges in the projection?

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Cosine Similarity of Adjacency Vectors

In a lesson from Collaborative Filtering : use a cosine similarity of adjacency vectors cos(ta, tb) := tai · tbi t2

ai ·

t2

bi

Terms in Time and Times in Context Andreas Spitz 15 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Evaluation

Ground truth: U.S. Election Days (1848 - 2013)

  • Recurs annually
  • Always on Tuesday after the first Monday in November

(Nov 2 - Nov 8)

  • Every four years: presidential election

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Evaluation

Ground truth: U.S. Election Days (1848 - 2013)

  • Recurs annually
  • Always on Tuesday after the first Monday in November

(Nov 2 - Nov 8)

  • Every four years: presidential election

Expectation:

  • For a given election day, election days in other years are

ranked highly

  • For presidential election days, other presidential election days

are ranked highly

Terms in Time and Times in Context Andreas Spitz 16 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Precision at k

0.3 0.4 0.5 0.6 1 20 40 60 80 100

k precision election day presidential general

preck := |Election days in top k ranks|

k

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Area Under the ROC Curve

  • 0.8

0.9 1.0 1850 1900 1950 2000

year AUC election day

  • general

presidential

Terms in Time and Times in Context Andreas Spitz 18 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Practical Application: Hot Spots & Key Players

Here: approximation of countries’ activity during given months For each European country c,

  • define its name, e.g. tn(c) = italy,
  • define the countries adjectival form, e.g. ta(c) = italian,
  • compute individual tf-idf scores for terms and combine.

act(c, d) := tf-id f(d, tn(c)) + tf-id f(d, ta(c)) max[tf-id f(d, ·)]

Terms in Time and Times in Context Andreas Spitz 19 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Activity by Country During World War II

German occupation

  • f Czechoslovakia

German invasion

  • f Poland

Soviet invasion

  • f Finland

German assault

  • n Norway

German invasion

  • f France

Battle of Britain and Greco−Italian War

March 1939 September 1939 November 1939 April 1940 May 1940 October 1940 0.0 0.5 1.0 1.5 activity Terms in Time and Times in Context Andreas Spitz 20 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Activity by Country During World War II (2)

Invasion of Yugoslavia and Greece German offensive against the Soviets Allied invasion

  • f Italy

Allied forces land in Normandy (DDay) Liberation of Paris German Capitulation

April 1941 June 1941 September 1943 June 1944 August 1944 May 1945 0.5 1.0 1.5 activity Terms in Time and Times in Context Andreas Spitz 21 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Summary

Approach:

  • Extract dates and terms from unstructured text
  • Construct a bipartite date-term graph
  • Allows ranking dates / terms according to co-occurrences

Benefits:

  • Simple measures already yield good results
  • Efficient: 4GB Memory and real-time queries
  • Flexibility of ranking methods

Terms in Time and Times in Context Andreas Spitz 22 of 24

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Ongoing Work

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Motivation Co-ooccurrence Graphs Term-Ranking Projection Application Summary

Thank you! Questions?

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