1. Semantic Indexing with Typed Terms using Rapid Annotation
Chris Biemann
University of Leipzig
16th of August 2005
TKE-05 Workshop on Semantic Indexing, Copenhagen

2. Outline The benefits of typed terms and relations
Alleviating the ontology bottleneck
Rapid annotation
Sources for annotation candidates
Annotation tools
Case study: Annotation of „Deutscher Wortschatz“
Conclusion

3. Typed terms and relations
The bag of words model treats all terms equally
Document similarity based on all terms
No views on data possible
Typed terms and relations:
Multiple views on documents w.r.t. types
Document similarity restricted to types and augmented by relations
Enables some tasks of Question Answering

4. Motivating example: untyped
Documents:
The government official A. Smith signed a contract over the purchase of 100 tanks from weapon manufacturer B. Miller.
„Weapon sales increased“, a government official stated, „especially tanks sell well“
A holiday cruise on a yacht invites to take photos of seagulls.
The photos show A. Smith on a cruise with B. Miller‘s yacht.
Similarity of terms: Clustering:
Doc 1
Doc 2
Doc 3
Doc 4 - 3 2 1 2 4 3

5. Motivating example: type PERSON
Documents:
The government official A. Smith signed a contract over the purchase of 100 tanks from weapon manufacturer B. Miller.
„Weapon sales increased“, a government official stated, „especially tanks sell well“
A holiday cruise on a yacht invites to take photos of seagulls.
The photos show A. Smith on a cruise with B. Miller‘s yacht.
Similarity of terms: Clustering:
Doc 1
Doc 2
Doc 3
Doc 4 - 2 1 2 4 3

6. The ontology bottleneck
Semantic Web people believe that annotation with ontology relations will enable semantic search, ...
Annotation: Chose an ontology, label all instances in the document
Problems:
New documents have to be annotated all over again
Merging of ontologies
Despite tools, users are reluctant to annotate their documents
interface
Merged ontology
Anno 1
Anno 2
Anno 3
Anno n
....
Doc 1
Doc 2
Doc 3
Doc n

7. Centralized annotation
Types and relations for terms are assigned globally and once-for-all.
No (logically grounded, consistent) ontology, but a free collection of types and relations suited to the problem
Annotation is done for document collections
interface
Annotation
Doc 1
Doc 2
document
collection
....
Doc 3
Doc n

8. Generating Candidates for Annotation
Given N terms from the collection, it is not feasible to present N² pairs to an annotator. Most of the pairs will not be related
Needed: Method that produces terms with similar types and related pairs at high rate
Method here:
Co-occurrence statistics: Pairs of terms that occur significantly often together in sentences/documents.
Co-occurrences of higher orders: pairs of terms that have similar co-occurrence statistics
Co-occurrences reflect syntagmatic and paradigmatic relations, the former are ruled out in higher orders

9. The cats and dogs example
cat co-occurrences: dog, her, food, pet, litter, she, burglar, animal, my, mouse, feline, Garfield, like, Cat, bag
cat order 2: cats, pet, dog, animals, animal, dogs, pets, neutered, her, she, Synindex, like, tabbie, pigs, shelter
cat order 4: pet, pets, cats, dog, pigs, animals, dogs, animal, owners, zoo, wild, birds, rabbits, puppies, tiger

10. Graphical annotation tool: colourizing co-occurrences

11. Specifying types and relations
Click on node / edge opens context menu restricted to POS

12. Web-based annotation tool for arbitrary candidate sources

13. Rule-based candidate generation
If some annotation is already present, then rules can be specified to obtain candidates at even higher rate.
It is possible to guess the type of candidates
Example:
Rule 1: If IS-A(A,B) and PROPERTY(B), then PROPERTY(A) yields LIVING(dog) as candidate
Rule 2: If IS-A(A,B) and COHYPONYM(A,C) then IS-A(C,B) yields IS-A(cat, animal) as candidate
animal LIVING
IS-A
dog
cat LIVING
CO-HYPONYM

14. Tool to accept or reject rule-based candidates

15. Case study: Annotating Deutscher Wortschatz www. wortschatz
Case study: Annotating Deutscher Wortschatz
In terms of numbers:
In 1‘000 hours, annotators could chose between
46 semantic types and
57 relations, and produced
150‘000 type instances and
150‘000 relation instances for over
80‘000 distinct terms, that is text coverage of
90%, with a speed of
5 units per minute

16. Different relations from different sources

17. Example: Query resolution with types and relations
Query: „Find documents mentioning at least two heads of computer companies!“
1. Translate into formal query:
Qset = {B | IS-A(A, computer company), HEAD-OF(B,A)}
b1 Qset, b2Qset, b1  b2
2. Access search engine with possible b1, b2

18. What Google found: Find documents mentioning at least two heads of computer companies!
#1 hit

19. Conclusion
Typed terms and relation can facilitate processing of electronic documents for a wide range of applications
Rapid annotation alleviates the acquisition bottleneck by - globally annotating - local dependencies
Intuitive tools for annotation are highly important to achieve large amounts in short time

20. QUESTIONS?!?
THANK YOU

21. Bonus material
Co-occurrences
Co-occurrences of higher orders

22. Statistical Co-occurrences
occurrence of two or more words within a well-defined unit of information (sentence, nearest neighbors)
Significant Co-occurrences reflect relations between words
Significance Measure (log-likelihood): - k is the number of sentences containing a and b together - ab is (number of sentences with a)*(number of sentences with b) - n is total number of sentences in corpus

23. Iterating Co-occurrences
(sentence-based) co-ocurrences of first order: words that co-occur significantly often together in sentences
co-occurrences of second order:
words that co-occur significantly often in collocation sets of first order
co-occurrences of n-th order: words that co-occur significantly often in collocation sets of (n-1)th order
When calculating a higher order, the significance values of the preceding order are not relevant. A co-occurrence set consists of the N highest ranked co-occurrences of a word.

24. Constructed Example I Ord 1 dog terrier cat mouse barking bite yelp -3 1 - 2

25. Constructed Example II
Ord 2
dog
terrier
cat
mouse
barking
bite
yelp x -
Ord 3
dog
terrier
cat
mouse
barking
bite
yelp - 1