1. Semi-automatic Product Attribute Extraction from Store Website
Yan Liu
Carnegie Mellon University
Sep 2, 2005

2. Example from Dick’s Sporting Goods
webpage
Free text
Product name
Description
Features
Structured data

3. Applications Direct application More general applications
Product recommendation systems for customers
Price estimates for auction
Sales amount prediction
More general applications
Document organization
prioritization
Question answering
And many more text mining tasks

4. Relationship with Previous Work
Information extraction
Extract from the documents salient facts about prespecified types of events, entities or relationships
Different from information retrieval
Previous work
Finite state machines
Sliding windows
Sequential models, such as HMMs or CRFs
Association and clustering
Major challenges
Few training data
Unclear attribute definition
Making better use of labeled and unlabeled data, Making better use of user feedback using active learning

5. Outline Introduction General framework Detailed algorithms
Experiment results
Conclusion and discussion

6. General Framework Attribute Identification Name-value Assignment
Example:
Input: free text
9.68-lb total weight (4.4-kg)
Attribute Identification
(Semi-supervised learning)
9.68-lb total weight (4.4-kg)
Name-value Assignment
(Statistical and grammatical association)
9.68-lb total weight (4.4-kg)
weight: 9.68-lb, 4.4-kg
weight: CD-lb
weight: CD-kg ….
Feedback
(Active learning)
Output: structured data

7. Attribute Identification
Initial label acquisition
Template matching
Knowledge database
Semi-supervised learning
Yarowsky’s algorithm
Co-training
Co-EM
Co-boosting
Graph-based methods
Phrase identification
Statistical associations between adjacent words
Heuristic grammatical rules

8. Attribute Identification (1) Initial Label Acquisition
Positive labels
Template matching
Extracted templates from data with special format
Noisy data
Knowledge database
Measure units: length, weight, volume and etc
Material
Country
Color
Negative labels
Partial stop word list

9. Attribute Identification (1) Semi-supervised learning
Co-training [Blum & Mitchell, 1998; Collins & Singer, 1999]
Separation of two views
Contextual features
Spelling features
Two kinds of features
Stemmer words (Porter Stemmer)
POS tagging (Brill’s tagger)
Algorithm Psedocode

10. Attribute Identification (1) Phrase Identification
Difference between chunking
Label propagation
Category dependent
Statistical association
Information gain
Mutual information
Yule’s statistic
display team colors
up to 12 inches
display team colors
up to 12 inches

11. Name-value Assignment
Combination of three information sources
Semantic association
Knowledge database
Attribute name generation and pair assignment
Grammatical association
Parsing tree (Minipar)
Attribute name/value generation
Statistical association scores
Yule’s statistic (category dependent)
Pair assignment
Other association sources
Wordnet

12. User Feedback Clustering─based active learning Clustering algorithm
Novelty attribute identification
Merge and splitting attributes
Better use of labeled examples
Clustering algorithm
Sparse data problem
Multiple clustering algorithms
Cluster selection
Within-cluster coherence
Novelty based measurement

13. User Feedback Clustering algorithm
Latent semantic indexing (LSI) [Deerwester et al, 1990]
Singular value decomposition on term─document matrix
Mapping the words into hidden semantic concepts
Similarity measure: cosine similarity
Clustering algorithm using CLUTO
K─means
Bisected K─means
Agglomerative algorithm
Single linkage
Complete linkage
Average linkage

14. User Feedback Cluster Selection
Novelty concepts
Major difference from previous task
Supervised novelty detection is difficult
Tradeoff between novelty and relevancy
Recently studied by the IR community [Carbonell and Goldstein, 1995; Zhang et al, 2003; Zhai et al, 2004]
Cluster selection criterion using maximal marginal relevance (MMR)
Similarity measure
Cosine similarity
KL-divergence

15. Outline Introduction General framework Detailed algorithms
Experiment results
Conclusion and discussion

16. Experiment Setup Dataset Evaluation measures
Free text extracted from product descriptions on
Subsets from two categories
Football (largest category)
52339 entries, words, 2926 predicted feature-value pairs
Tennis (medium category)
3840 entries, words, 419 predicted feature-value pairs
Evaluation measures
Direct evaluation
Precision on feature value pairs
Indirect evaluation in other applications
Recommender systems

17. Experiment Results Examples by steps Initial label acquisition
Semi-supervised learning
Phrase identification
Semantic association
Grammatical association
Statistical association scores

18. Experiment Results Human feedback Examples by active learning
Sample files (link to file)
Total labeling time of 5 mins
Identified concepts
color, graphics, logo, design, fit, size, pocket, pad, set, adjustment, attachment, construction, strap
Examples by active learning

19. Experiment Results Precision on most frequent feature-value pairs
Most frequent 600 pairs
Assignment of 5 labels
Fully correct, incorrect names, incorrect values, incorrect associations, nonsense:
Human labeling of approximately 6 hours
Thanks to Katharin and Marko
Results

20. Conclusion Product attribute identification is a difficult task
Few training data
Making use of labeled and unlabeled data by semi-supervised learning
Unclear attribute definition
Novelty attribute identification by active learning
A framework of active learning combined with semi-supervised learning

21. Text Learning Techniques
Text processing
Stemming (Porter stemmer)
POS tagging (Brill’s parser)
Text chunking and parsing (Minipar)
Word semantics (Wordnet, dependency-based thesaurus)
Latent semantic indexing (SVDPack)
Machine learning
Semi-supervised learning (Co-training)
Active learning (MMR)
Classification (C4.5 decision tree, FOIL)
Clustering (K-means, CLUTO)
Information theory and statistical associations (Information gain, Yule’s statistic)

22. Future Work
Associations of product attributes across categories or websites
More effective active learning algorithms
Graphical models with application to information extraction

23. Questions?