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Weakly supervised methods 
for information extraction 


  PhD defense   Koen Deschacht

Supervisors :  Prof. Marie­Francine Moens                 
               Prof. Danny De Schreye


                                                             1
Overview




           2
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


Examples

Word sense disambiguation in (WSD)
Semantic role labeling (SRL)
Visual entity detection



                                                   3
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


WSD: Determine meaning of a word 

He kicked the ball in the goal.
At a formal ball attendees wear evening attire.
He stood on the balls of his feet.



                                                   4
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


SRL: Who is doing what, where ? 

John broke the window with a stone.
John broke the window with little doubt.
The window broke.



                                                   5
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


Who/what is present in the image?

          Hillary Clinton
          Bill Clinton




                                                   6
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


Common approach:

Word sense disambiguation
Semantic role labeling
Visual entity detection

and many, many more...
                                                   7
Information extraction

 Detect and classify structures in unstructured 
   Text 
   Images / video


Common approach:

Word sense disambiguation
Semantic role labeling       Supervised machine
Visual entity detection      learning methods

and many, many more...
                                                   8
Supervised machine learning

 Statistical methods that are trained on many 
 annotated examples
 SRL : 113.000 verbs
 WSD : 250.000 words
 Learn soft rules from the data




                                                 9
Example: WSD

Ball = round object
 1. He kicked the ball in the goal.
 2. Ricardo blocks the ball as Benzema tries to shoot.
 3. Patrice Evra almost kicked the ball in his own goal.
 …

Ball = formal dance
 1. Obama and his wife danced at the inaugural ball.
 2. Casey Gillis was dressed in a white ball gown.
 3. Dance Unlimited's Spring Ball takes place tomorrow.
 ...

                                                           10
Example: WSD

Soft rules : 
  ­ If “kicked”
  ­ If “goal”        ball = “round object”
  ­ ... 

  ­ If “dance”
  ­ If “gown”        ball = “formal dance”
  ­ ...

 Machine learning methods can combine many  
 complimentary and/or contradicting rules


                                               11
Supervised machine learning

 Current state­of­the­art machine learning 
 methods

                                 Manually annotated corpus 
 Machine learning method 
                                  needed for every new task, 
  often independent of task 
                                  language or domain
 Successful for many tasks
                                 Features need to be 
 Flexible, fast development 
                                  manually engineered
  for new tasks
                                 High variation of language 
 Only some expert 
                                  limits performance even 
  knowledge needed
                                  with large training corpora


                                                                12
Solution: use unlabeled data

 Unlabeled data: cheap, available for many 
 domains and languages
 Semi­supervised learning
   Optimize single function that incorporates labeled 
   and unlabeled data
   Violation of assumptions cause deteriorating results 
   when adding more unlabeled data
 Unsupervised learning
   First learn model on unlabeled data, then use model 
   in supervised machine learning method
                                                           13
Distributional hypothesis

 It is possible to determine the meaning of a 
 word by investigating its occurrence in a corpus.

Example:



           What does “pulque” mean?




                                                     14
Distributional hypothesis

 It is possible to determine the meaning of a 
 word by investigating its occurrence in a corpus.

Example:
“It takes a maguey plant twelve years before it is mature 
enough to produce the sap for pulque.”
“The consumption of pulque peaked in the 1800’s.”
“After the Conquest, pulque lost its sacred character, and 
both indigenous and Spanish people began to drink it.”
“In this way, the making of pulque passed from being a 
home­made brew to one commercially produced.”
                                                              15
Latent words language model

 Directed Bayesian model that models likely 
 synonyms of a word, depending on context.
 Automatically learns synonyms and related 
 words.




                                               16
Latent words language model

We   hope   there   is   an   increasing   need   for   reform
 
                          Original sentence




                                                                 17
Latent words language model

 We     hope     there   is     an    increasing    need     for       reform
  
We      hope     there   is     an    increasing    need     for       reform
 I     believe   this    was   the    enormous     chance     of    restructuring
They   think     that    's     no    important    demand     to       change
You     feel      it     are   some   increased    potential that      peace
 ...     ...      ...    ...    ...       ...         ...     ...        ...


                               Automatically learned synonyms




                                                                                    18
Latent words language model

 We       hope     there   is     an    increasing    need     for       reform
  
We        hope     there   is     an    increasing    need     for       reform
 I       believe   this    was   the    enormous     chance     of    restructuring
They      think    that    's     no    important    demand     to       change
You       feel      it     are   some   increased    potential that      peace
 ...       ...      ...    ...    ...       ...         ...     ...        ...


       Time to compute all possible combinations:          
                                       ~ very, very long...
       Approximate: consider only most likely                 
                                                 ~ pretty fast
                                                                                      19
LWLM: quality

 Measure how well the model can predict new, 
 previously unseen texts in terms of perplexity

      Model     Reuters   APNews     EnWiki
      ADKN      114.96     134.42     161.41
      IBM       108.38     125.65     149.21
      LWLM      108.78     124.57     151.98
    int. LWLM    96.45     112.81     138.03

 LWLM outperforms other language models

                                                  20
LWLM for information extraction

 Word sense disambiguation
   standard           + cluster features              + hidden words
    66.32%                 66.97%                                 67.61%

 Semantic role labeling
              90%

              80%

              70%

              60%
                                                 standard
              50%                                + clusters
              40%                                + hidden words
                 5%           20%          50%                100%


 Latent words : help with underspecification and 
               ambiguity
                                                                           21
Automatic annotation of images & video

 Texts describe content of images
 Extract information in structured format
   Entities
   Attributes
   Actions
   Locations




                                            22
Automatic annotation of images & video

 Texts describe content of images
 Extract information in structured format
   Entities
   Attributes
   Actions
   Locations




                                            23
Annotation of entities in images

 Extract entities from descriptive news text that 
 are present in the image.
 Former President Bill Clinton, left, looks on as an honor guard 
 folds the U.S. flag during a graveside service for Lloyd Bentsen 
 in Houston, May 30, 2006. Bentsen, a former senator and 
 former treasury secretary, died last week at the age of 85.

                                                         service
                                                         Lloyd Bentsen
                                      Bill Clinton
                                                         Houston
                                      guard             age
                                      flag              ...

                                                                          24
Annotation of entities in images

 Assumption: 
   Entity is present in image if important in 
   descriptive text and possible to perceive visually.
 Salience: 
   Dependent on text
   Combines analysis of discourse and syntax
 Visualness:
   Independent of text 
   Extracted from semantic database

                                                         25
Annotation of entities in images

 Former President Bill Clinton, left, looks on as an honor guard 
 folds the U.S. flag during a graveside service for Lloyd Bentsen 
 in Houston, May 30, 2006. Bentsen, a former senator and 
 former treasury secretary, died last week at the age of 85.

                                      Bill Clinton      service
                                      guard
                                                         Lloyd Bentsen
                                                         Houston
                                      flag              age
                                                         ...




                                                                          26
Salience

 Is the entity important in descriptive text?
 Discourse model
   Important entities are referred to by other entities 
   and terms.
   Graph models entities, co­referents and other terms 
   Eigenvectors find most important entities
 Syntactic model
   Important entities appear high in parse tree
   Important entities have many children in tree

                                                           27
Visualness
 Can the entity be perceived visually?
 Similarity measure on entities in WordNet
   s(“car”,“truck”) = 0.88       s(“thought”,“house”) = 0.23
   s(“car”,“horse”) = 0.38       s(“house”,“building”) = 0.91
   s(“horse”, “cow”) = 0.79      s(“car”, “house”) = 0.40

 Visual seeds                 “person”, “vehicle” , “animal”, ...

 Non­visual seeds             “thought”, “power”, “air”, …

 Visualness: 
   combine similarity measure and seeds
   “entities close to visual seeds will be visual”
                                                                    28
Annotation of entities: Results

 Appearance model : combine visualness and 
                    salience

 All entities   + visualness   + salience   + salience + visualness
   26.66%         62.78%        59.56%               69.39%



 Appearance model dramatically increases 
 accuracy!



                                                                      29
Scene location annotation

 Annotate location of every scene in sitcom series 
 Input : video and transcript

                            Shot of Buffy opening the 
                            refrigerator and taking 
                            out a carton of milk. 
                            Buffy sniffs the milk and 
                            puts it on the counter. In 
                            the background we see 
                            Dawn opening a cabinet 
                            to get out a box of cereal. 
                            Buffy turns away.
                                                           30
Scene location annotation

 Annotate location of every scene in sitcom series




  Dawn's room                the kitchen




      the living room               the street
                                                     31
Scene segmentation

 Segment transcript and video in scenes
       Scene cut classifier in text
       Shot cut detector in video

               Transcript
               Shot of Buffy opening the refrigerator and taking out a carton of milk. 
  Scene cuts




               Buffy sniffs the milk and puts it on the counter. In the background we 
               see Joyce drinking coffee and Dawn opening a cabinet to get out a box 
               of cereal.    ...
               Buffy & Riley move into the living room. They sit on the sofa. 
               Buffy nods in resignation.   Smooch. Riley gets up.   
               Cut to a shot of a bright red convertible driving down the street. Giles 
               is at the wheel, Buffy beside him and Dawn in the back. Classical 
               music plays on the radio. 
               ....
                                                                                           32
Scene segmentation

 Segment transcript and video in scenes
   Scene cut classifier in text
   Shot cut detector in video




                                          33
Scene segmentation

 Segment transcript and video in scenes
   Scene cut classifier in text
   Shot cut detector in video
                                  Shot of Buffy opening the 
                                  refrigerator and taking out a 
                                  carton of milk. 
                                  ...
                                  Buffy & Riley move into the 
                                  living room. They sit on the 
                                  sofa. 
                                  …
                                  Cut to a shot of a bright red 
                                  convertible driving down the 
                                  street.
                                  ....
                                                                   34
Location detection and propagation

 Detect locations in text
 Shot of Buffy opening the refrigerator and taking out a carton of 
 milk. ...
 Buffy & Riley move into the living room. They sit on the sofa. 
 Cut to a shot of a bright red convertible driving down the street.



 Propagate locations to other scenes
   Latent Dirichlet allocation: learn correlation 
   locations & other objects (“refrigerator”→“kitchen”)
   Visual reweighting: visually similar scenes should 
   be in the same location
                                                                      35
Location annotation results

 Scene cut classifier          precision       recall       f1­measure
                                91.71%        97.48%             85.16%


 Location detector             precision       recall       f1­measure
                                68.75%        75.54%             71.98%


 Location annotation

       episode   only text   text + LDA    text + LDA + vision
         2       54.72%       58.89%            57.39%
         3       60.11%       65.87%            68.57%



                                                                          36
Contributions    1/2

 The latent words language model
   Best n­gram language model
   Unsupervised learning of word similarities 
   Unsupervised disambiguation of words
 Using the latent words for WSD
   Best WSD system 
 Using the latent words for SRL
   Improvement of s­o­a classifier



                                                 37
Contributions    2/2

 Image annotation : 
   First full analysis of entities in descriptive texts
   Visualness: capture knowledge from WordNet 
   Salience: capture knowledge from syntactic 
   properties
 Location annotation : 
   Automatic annotation of locations from transcripts
   Including new locations
   Including locations that are not explicitly mentioned


                                                           38
Thank  you!

Questions?

Comments?


              39

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