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Semantic Indexing with Typed Terms usingRapid Annotation

16th of August 2005

TKE-05 Workshop on Semantic Indexing, Copenhagen

Chris BiemannUniversity of Leipzig

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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

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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

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Motivating example: untypedDocuments:

1. The government official A. Smith signed a contract over the purchase of 100 tanks from weapon manufacturer B. Miller.

2. „Weapon sales increased“, a government official stated, „especially tanks sell well“

3. A holiday cruise on a yacht invites to take photos of seagulls.

4. 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

Doc 1 -

Doc 2 3 -

Doc 3 0 0 -

Doc 4 2 0 3 -

1

4 3

2

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Motivating example: type PERSON

Documents:

1. The government official A. Smith signed a contract over the purchase of 100 tanks from weapon manufacturer B. Miller.

2. „Weapon sales increased“, a government official stated, „especially tanks sell well“

3. A holiday cruise on a yacht invites to take photos of seagulls.

4. 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

Doc 1 -

Doc 2 0 -

Doc 3 0 0 -

Doc 4 2 0 0 -

1

4 3

2

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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

Doc 1

Anno 1

Doc 2

Anno 2

Doc 3

Anno 3

Doc n

Anno n....

Merged ontology

interface

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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

Doc 1

Annotation

Doc 2

Doc 3 Doc n....

interface

documentcollection

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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

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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

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Graphical annotation tool: colourizing co-occurrences

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Specifying types and relations

• Click on node / edge opens context menu restricted to POS

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Web-based annotation tool for arbitrary candidate sources

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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

dog catLIVING

animalLIVING

IS-A

CO-HYPONYM

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Tool to accept or reject rule-based candidates

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Case study: Annotating Deutscher Wortschatz

www.wortschatz.uni-leipzig.de

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

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Different relations from different sources

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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, b2Qset, b1 b2

2. Access search engine with possible b1, b2

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What Google found:Find documents mentioning at

least two heads of computer companies!

#1 hit 14.08.2005 www.google.com

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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

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QUESTIONS?!?

THANK YOU

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Bonus material

• Co-occurrences

• Co-occurrences of higher orders

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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

( , ) log log !

with number of sentences,

.

sig A B x k x k

n

abx

n

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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.

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Constructed Example IOrd 1 dog terrier cat mouse barking bite yelp

dog - - - X x X

terrier - - - x x X

cat - - x - x -

mouse - - X - x -

barking X X - - - -

bite X X x x - -

yelp x x - - - -

Ord 2 dog terrier cat mouse barking bite yelp

dog 3 1 1 - - -

terrier 3 1 1 - - -

cat 1 1 1 - - -

mouse 1 1 1 - 1 -

barking - - - - 2 2

bite - - - 1 2 2

yelp - - - - 2 2

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Constructed Example II

Ord 3 dog terrier cat mouse barking bite yelp

dog - - - - - -

terrier - - - - -

cat - - - - - -

mouse - - - - - -

barking - - - - 1 1

bite - - - - 1 1

yelp - - - - 1 1

Ord 2 dog terrier cat mouse barking bite yelp

dog x - - - - -

terrier x - - - - -

cat - - - - - -

mouse - - - - - -

barking - - - - x x

bite - - - - x x

yelp - - - - x x