1. Automatic Detection of Causal Relations for Question Answering
2003
Roxana Girju
Baylor University

2. content
Background
Main Task
Result Analysis
Application in QA

3. Background Causation relations expressed in English
Explict (cause, lead to, kill, dry…)
Implict (no keyword)
Previous Work
Using knowledge-based inferences

4. Main Task classifier A classifier based in machine learning
input: sentence(<NP1, verb, NP2>)
vectorization
main task
output: YES or NO
classifier

5. Vectorization
Training Example
(entityNP1,psychological-featureNP1,abstractionNP1, stateNP1,eventNP1,actNP1,groupNP1,possessionNP1,phenomeno nNP1;
verb;
entityNP2,psychological-featureNP2,abstractionNP2, stateNP2,eventNP2,actNP2,groupNP2,possessionNP2,phenomeno nNP2;
target)

6. Sentence: Earthquake generates Tsunami
Vector:
<f, f, f, f, f, f, f, f, t, generate, f, f, f, f, f, t, f, f, f>
The complete training example：
<f, f, f, f, f, f, f, f, t, generate, f, f, f, f, f, t, f, f, f, YES>

7. How to build the training set?
Step1: find the sentences (Where did the data come from?)
Step2: select features (How to vectorization?)

8. Find the Sentences Step1: find NP pairs contain causation relationship
WordNet 1.7 contains 429 such NP pairs, the most frequent being medicine.(about 58.28%)

9. Step2: For each pair of causation nouns determined above, search the Internet and retain only sentences containing the pair. From these sentences,determine automatically all the parterns <NP1 verb/verb_expression NP2>

10. Step3: searching the text collection and retained 120 sentences containing the verb
60*120 = 7200 ( corpus A)
Step4: extracting 6523 relationships of the type <NP1 verb NP2> from sentences.
2101 are causal relations, while 4422 are not (manually annotate)

11. Select Features Both lexical and semantic features
Lexical features: verb/verb_expression
Semantic features:
9 noun hierarchies in WordNet: entity, psychological feature, abstraction, state, event, act, group, possession, and phenomenon.

12. Training Algorithm C4.5 decision tree
Inductive bias: a preference for the shorter tree that places high information gain attributes closer to the root

13. Result Analysis
683 relationships of the type <NP1 verb NP2> in corpus B
102/(115+38) = 66.67%

14. Reasons for Errors
Mostly the fact that the causal pattern is very ambiguous
Incorrect parsing of noun phrases
The use of the rules with smaller accuracy(e.g. 63%)
The lack of named entities in WordNet

15. Application in QA
50 questions tested, 61% precision for QA system with the causation module, and 36% precision for QA system without the module.

16. Thanks!