collective classification a brief overview and possible connections to email-acts classification...
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Collective Classification A brief overview and possible connections to
email-acts classification
Vitor R. Carvalho
Text Learning Group Meetings,
Carnegie Mellon University
November 10th 2004
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Data Representation
• “Flat” Data – Object: email msgs– Attributes: words, sender, etc– Class: spam/not spam– Usually assumed IID
• Sequential Data– Object: words in text– Attr: capitalized, number, dict– Class: POS (or name/not)
• Relational Data– class+attributes– +links(relations)– Example: webpages
pron namedetnameverb
spamspam
spam Not spam
Not spam
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J. Neville et al., 2003
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Relational Data and Collective Classification
•Different objects interact
•Different types of relations (links)
•Attributes may be correlated
•Examples: – actors, directors, movies, companies– papers, authors, conferences, citations– company, employee, customer,
Classify objects collectively
Use prediction on some objects to improve prediction on related objects
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Collective Classification Methods
• Relational Probability Trees (RPT)
• Iterative methods (Relaxation-based Methods)
• Relational Dependency Networks (RDN)
• Relational Bayesian Networks (RBN/PRM)
• Relational Markov Networks (RMN)
• Other models (ILP based, Vector Space based, etc)
•Overall:
– Lack of direct comparison among methods
– Results are usually compared to “flat” model
– Splitting data into train/test sets can be an issue
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Relational Probability Trees
• Decision Trees applied to Relational data
• Predicts the target class label based on:– same object attributes– attributes + links in “relational neighborhood” (one link away)– counts of attributes and links in the “neighborhood”
• Enhanced feature selection (Chi-square, pruning, randomization tests)
• Results were not exciting
•Neville et al. KDD2003, related work from Blockeel et al. (Artificial Intelligence, 1998), Kramer AAAI-96
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Iterative Methods
• Predicts the target class label based on:– Same object attributes– Attributes and links of relational
neighborhood– CLASS LABEL of neighborhood– Features derived from CLASS LABELS
• Different update strategies:– By threshold in prediction confidence
– By top-N most confident predictions
– Heuristic-based
• Slattery & Mitchell, ICML-2000;Neville & Jensen, AAAI-2000; Chakrabarti et al. ACM-SIGMOD-98
• Some results with Email-acts
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Relational Bayesian Networks (RBN/PRM)
• Bayes Net extended to Relational domain
• Given an “instantiation”, it induces a bayes-net that specifies a joint probability distribution over all attributes of all entities
• Directed graphical model, with acyclicity constraint.
• Exact model - Closed form for parameter estimation – Products of conditional probabilities
• Was applied to simple domains, since the acyclicity constraints is very restrictive to most relational applications
• Friedman et al, IJCAI-99; Getoor et al., ICML-2001; Taskar et al. IJCAI-2001
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Relational Markov Networks (RMN)
• Extension of CRF idea to Relational Domain
• Given an instantiation, it induces a Markov Network that specifies a probability distribution of labels, given links and attributes
• Undirected, Discriminative model
• Parameter estimation is expensive, requires approximate probabilistic inference (belief propagation)
•Taskar et al., UAI2002
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Relational Dependency Networks (RDN)
• Dependency Networks extended to Relational domain
• P(X) = π [ Prob (Xi | Neighbor(Xi)) ]
• Given an “instantiation”, it induces a DN that specifies an “approximate” joint probability distribution over all attributes of all objects
• Undirected graphical model, no acyclicity constraint.
• Approximate model - Simple parameter estimation – approximate inference (Gibbs sampling)
• Neville & Jensen, KDD-MRDM-2003
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Other Models
From Neville et al., 2003
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Comparing Some Results
• Comparing PRM, RMN, SVM and M^3N
• Diff: PRM and RMN• Diff: mSVM and RMN
• RN* (Relational Neighbor) is a very simple Relational Classifier
• RN* (Macskassy et al., 2003)• M^3N(Taskar et al., 2003)
PRM
RMN
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End of overview…now, the email-act problem
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• Strong correlation with previous and next message
• Flat data?
• Sequential data?
• A “verb” has little or no correlation with other “verbs” of same message