1. Discovery of Inference Rules for Question Answering
Dekang Lin and Patrick Pantel
Natural Language Engineering 7(4): , 2001
as (mis-)interpreted by Peter Clark

2. Goal Observation: “mismatch” between expressions in qns and text
e.g. “X writes Y” vs. “X is the author of Y”
Need “inference rules” to answer questions
“X writes Y”  “X is the author of Y”
“X manufactures Y”  “X’s Y factory”
Question:
Can we learn these inference rules from text?
(aka “paraphrases”, “variants”)
DIRT (Discovering Inference Rules from Text)

3. The limits of word search…
Who is the author of ‘Star Spangled Banner?’ A.
…Francis Scott Key wrote the “Star Spangled Banner” in 1814.
…comedian-acress Roseanne Barr sang her famous shrieking rendition of the “Star Spangled Banner” before a San Diego Padres-Cincinnati Reds game. B.
What does Peugot manufacture?
Chrétien visited Peugot’s newly renovated car factory in the afternoon.

4. Approach Parse sentences in a giant (1GB) corpus
Extract instantiated “paths” from the parse tree, e.g.:
X buys something from Y
X manufactures Y
X’s Y factory
For each path, collect the sets of X’s and Y’s
For a given path (pattern), find other paths where the X’s and Y’s are pretty similar

5. Approach Parse sentences in a giant (1GB) corpus, then:
Extract “paths” from the parse tree, e.g.:
X buys something from Y
X manufactures Y
X’s Y factory
Collect statistics on what the X’s and Y’s are
Compare the X-Y sets:
For a given path (pattern), find other paths where the X’s and Y’s are similar

6. Results (examples)

7. Method: 1. Parse Corpus 1GB newspaper (Reuters?) corpus Use MiniPar
Chart parser
self-trained statistical ranking of parse (“dependency”) trees

8. Method: 2. Extract “paths”

9. Method: 3. Collect the X’s and Y’s

10. Method: 4. Compare the X-Y sets ?…

11. Method: 4. Compare the X-Y sets ?
…and

12. Method: 4. Compare the X-Y sets
1. Characterizing a single X-Y set:
Count frequencies of words for X (and Y)
Weight by ‘saliency’ (slot-X mutual information)

13. Method: 4. Compare the X-Y sets
2. Comparing two X-Y sets
Two paths have high similarity if there are a large number of common features.
Mathematically:

14. Example:
Learned
Inference
rules

15. Example:
vs. Hand-crafted
inference
rules (by ISI)

16. Results

17. Observations Little overlap in manual and automatic rules
DIRT performance varies a lot
Much better with verb rather than noun roots
If less than 2 modifiers, no paths found
For some TREC examples, no “correct” rules found
“X leaves Y”  “X flees Y”
Where X’s and Y’s are similar, can get agent-patient the wrong way round
E.g. “X asks Y” vs. “Y asks X”

18. The Big Question
Can we acquire the vast amount of common-sense knowledge from text?
Lin and Pantel suggests: “yes” (at least in a semi-automated way)