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Text Classification with Belief Augmented FramesPowerPoint Presentation

Text Classification with Belief Augmented Frames

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### Text Classification withBelief Augmented Frames

Colin Tan

Department of Computer Science,

School of Computing,

National University of Singapore.

Outline

- What are Belief Augmented Frames?
- Motivation behind Belief Augmented Frames
- Representing Beliefs in BAFs
- Some Definitions
- Belief Augmented Frame Logic (BAF-Logic)
- Applying BAF-Logic to Text Classification
- Experiment Protocol and Results
- Conclusions

What are Belief Augmented Frames?

- Belief Augmented Frames (BAF) combine classical AI frames with belief measures.
- Frame-based system to structure knowledge and relations between entities.
- Belief measures provide uncertain reasoning on existence of entities and the relationships between them.

Motivation behind Belief Augmented Frames

- Why Belief Measures?
- Statistical Measures
- Standard tool for modeling uncertainty.
- Essentially, if the probability that a proposition E is true is p, then the probability of that E is false is 1-p.
- P(E) = p
- P(not E) = 1-p

- This relationship essentially leaves no room for ignorance. Either the proposition is true with a probability of p, or it is false with a probability of 1-p.
- This can be counter-intuitive at times.

- Statistical Measures

Motivation behind Belief Augmented Frames

- Why Belief Measures?
- [Shortliffe75] cites a study in which, given a set of symptoms, doctors were willing to declare with certainty x that a patient was suffering from a disease D, yet were unwilling to declare with certainty 1-x that the patient was not suffering from D.

Motivation behind Belief Augmented Frames

- Why Belief Measures?
- To allow for ignorance our research focuses on belief measures.
- The ability to model ignorance is inherent in belief systems.
- E.g. in Dempster-Shafer Theory [Dempster67], if our belief in E1 and E2 are 0.1 and 0.3 respectively, then the ignorance is (1 – (0.1 + 0.3)) = 0.6.

Motivation behind Belief Augmented Frames

- Why Frames?
- Frames are a powerful form of representation.
- Intuitively represents relationships between objects using slot-filler pairs.
- Simple to perform reasoning based on relationships.

- Hierarchical
- Can perform generalizations to create general models derived from a set of frames.

- Intuitively represents relationships between objects using slot-filler pairs.

- Frames are a powerful form of representation.

Belief Representation in Belief Augmented Frames

- Beliefs are represented by two masses:
- φT: Belief mass supporting a proposition.
- φF: Belief mass refuting a proposition.
- In general φT + φF 1
- Room to model ignorance of the facts.

- Separate belief masses allow us to:
- Draw φTand φFfrom different sources.
- Have different chains of reasoning for φT and φF.

Belief Representation in Belief Augmented Frames

- This ability to derive the refuting masses from different sources and chains of reasoning is unique to BAF.
- In Probabilistic Argumentation Systems (the closest competitor to BAF) for example, p(not E) = 1 – p(E).

Some Definitions

- Degree of Inclination
- The Degree of Inclination is defined as:
- DI = T - F

- DI is in the range of [-1, 1].
- One possible interpretation of DI:

- The Degree of Inclination is defined as:

Some Definitions

- Utility Value
- The Degree of Inclination DI can be re-mapped to the range [0, 1] through the Utility function:
- U = (DI + 1) / 2
- By normalizing U across all relevant propositions it becomes possible to use U as a statistical measure.

- The Degree of Inclination DI can be re-mapped to the range [0, 1] through the Utility function:

Belief Augmented Frame Logic(BAF-Logic)

- Belief Augmented Frame Logic, or BAF-Logic, is used for reasoning with BAFs.
- Throughout the remainder of this presentation, we will consider two propositions A and B, with supporting and refuting masses TA, FA, TB, and FB.

Belief Augmented Frame Logic(BAF-Logic)

- A B:
- TA B = min(TA, TB)
- FA B = max(FA, FB)

- A B:
- TA B = max(TA, TB)
- FA B = min(FA, FB)

- A:
- T A = F A
- F A = T A

Belief Augmented Frame Logic(BAF-Logic)

- BAF-Logic properties that are identical to Propositional Logic:
- Associativity, Commutativity, Distributivity, Idempotency, Absorption, De-Morgan’s Theorem, - elimination.

- Other properties of Propositional Logic work slightly differently in BAF-Logic.
- In particular, some of the properties hold true only if the constituent propositions are at least “probably true” or “probably false”
- I.e. |DIP | 0.5

- In particular, some of the properties hold true only if the constituent propositions are at least “probably true” or “probably false”

Belief Augmented Frame Logic(BAF-Logic)

- An Example:
- Given the following propositions in your knowledge base:
- KB = {(A, 0.7, 0.2), (B, 0.9, 0.1), (C, 0.2, 0.7), (A B R, TONE , FONE,), (A BR, TONE , FONE)}
- We want to derive TR, FR.

- Given the following propositions in your knowledge base:

Belief Augmented Frame Logic(BAF-Logic)

- Combining our clauses regarding R, we obtain:
- R = (A B) (A B)
- = A B ( A B)

- R = (A B) (A B)
- With De-Morgan’s Theorem we can derive R:
- R= A B (A B)

Belief Augmented Frame Logic(BAF-Logic)

- TR = min(TA , TB , max(FA , TB ))
= min(0.7, 0.9, max(0.2, 0.9))

= min(0.7, 0.9, 0.9)

= 0.7

- FR = max(FA , FB , min(TA , FB ))
= max(0.2, 0.1, min(0.7, 0.1))

= max(0.2, 0.1, 0.1)

= 0.2

Belief Augmented Frame Logic(BAF-Logic)

- DIR = TR - FR
= 0.7 – 0.2

= 0.5

- UR = (1 + 0.5) / 2.0
= 0.75

- Suppose now it is known that B C R

Belief Augmented Frame Logic(BAF-Logic)

- Combining our clauses regarding R, we obtain:
- R = (A B) (B C) (A B)
= A B C ( A B)

- R = (A B) (B C) (A B)
- With De-Morgan’s Theorem we can derive R:
- R= A B C (A B)

Belief Augmented Frame Logic(BAF-Logic)

- TR = min(TA , TB , TC , max(FA , TB ))
= min(0.7, 0.9, 0.2, max(0.2, 0.9))

= min(0.7, 0.9, 0.2, 0.9)

= 0.2

- FR = max(FA , FB , FC , min(TA , FB ))
= max(0.2, 0.1, 0.7, min(0.7, 0.1))

= max(0.2, 0.1, 0.7, 0.1)

= 0.7

Belief Augmented Frame Logic(BAF-Logic)

- DIR = TR - FR
= 0.2 – 0.7

= -0.5

- UR = (1 - 0.5) / 2.0
= 0.25

- Here the new evidence that B C R fails to support R, because C is not true (DIC = -0.5)

Text ClassificationFirst Approach

- First Formulation:
- Using Individual Word Scores
- Assuming that a document dibelongs to a class ck, then for every term tijthe following relation holds:
di ck (ti0 ck ti1 ck ti2 ck … ti,n-1 ck)

Text ClassificationFirst Approach

- Likewise, for a document dinot belonging to a class ck, we can derive:
di ckm, mk (ti0 cm ti1 cm ti2 cm … ti,n-1 cm)

- These can be formulated in BAF-Logic:
Tdi ck = min(p(ck | ti0), p(ck | ti1), …, p(ck | ti, n-1))

Fdi ck = max(min(p(cm | ti0), p(cm | ti1), …,

p(cm | ti, n-1)), min(p(cn|ti0),

p(cn|ti1),…,p(cn|ti,n-1)), …)), m, n etc k

Text ClassificationFirst Approach

- The final score of a document di belong to class cj is given by:

- Where:

Text ClassificationFirst Approach

- Individual term probabilities are derived using Bayesian probabilities:

Text ClassificationSecond Approach

- We classify the entire document using Naïve Bayes assumption:

- Trivial to derive the supporting score that di ck.
- It is simply p(ck | di)

Text ClassificationSecond Approach

- Formulating the Refuting Score is straightforward too:
di ck di cm di cn di cp…, m, n, p, etc k

- We can formulate both supporting and refuting scores in BAF-Logic:

Text ClassificationSecond Approach

- We retain the definitions of DI and U from the first approach.

Experiment Protocol

- Using Andrew McCallum’s “Bag of Words” or BOW library.
- Extended “rainbow”, the text-classification front-end, with two BAF classification methods.
- Methods are called BAF1 and BAF2

- Also extended with two PAS methods (see paper for more details)
- Methods are called PAS1 and PAS2

- Extended “rainbow”, the text-classification front-end, with two BAF classification methods.

Experiment Protocol

- Corpus:
- 20 Newsgroups
- 80% (16,000) documents used to generate statistics.
- 20% (4,000) documents used for testing
- Choice of documents for training/testing handled by BOW
- Headers removed from all documents

Experiment Protocol

- Trials
- 10 trials were performed using each classification method.
- Naïve Bayes, tf.idf, kNN, EMM, Max entropy, Probabilistic Indexing, BAF1, BAF2, PAS1, PAS2

- The average was taken from the 10 trials for each method.

- 10 trials were performed using each classification method.

Analysis

- BAF1 performs poorly.
- Using individual word scores appears to be a poor idea.

- BAF2 performs very well.
- Better than the other methods attempted.

- BAF2 Performance slightly better than Naïve Bayers
- Appears that considering a document to belong to another class has a positive effect on classification scores.

Conclusion

- Experiment results show that the use of BAF-Logic to classify documents might be a good idea.
- In addition there are features of BAFs (e.g. daemons attached to slots) that might enhance classification performance further.
- More work should be done on this.
- Understanding better why BAF-Logic works for text classification.
- Improving classification performance.

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