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Aspect Guided Text Categorization with Unobserved Labels Dan Roth, Yuancheng Tu University of Illinois at Urbana-Champaign.

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Presentation on theme: "Aspect Guided Text Categorization with Unobserved Labels Dan Roth, Yuancheng Tu University of Illinois at Urbana-Champaign."— Presentation transcript:

1 Aspect Guided Text Categorization with Unobserved Labels Dan Roth, Yuancheng Tu University of Illinois at Urbana-Champaign

2 Text Categorization An archetypical Multi-Class Classification (MCC) problem  F : X → Y  a document, d ∈ X, a collection of classes Y = {c 1, c 2,..., c N } Sports Health Business … Science C1 C2 C3 … C N 2

3 Motivation: what are we missing? Class labels (Y) contain information which can help classification How can we explore the label space? C1 C2 C3 … C N Sports Health Business … Science 3

4 Car Navigation Command Classification 4 XY

5 Aspect Variables 5 X Y find nearest restaurant Show me where I can eat nearby Find nearest restaurant ActionDetailModifierTopicManner z1z1 z2z2 z3z3 z4z4 z5z5 Null

6 Significance of the Aspect Variables Predicting better aspects implies predicting better class labels Adding constraints to the aspect space Predicting previously unobserved labels 6 If Topic = “restaurant”, then Action ≠ “turn” Observed Label 1. turn on the radio 2. GPS navigation Unobserved Label turn on GPS

7 Outline Car Command Text Categorization Task  Data and aspects  Unobserved labels Constrained Conditional Model (CCM)  Aspects variables to introduce constraints  Objective function  Training and Inference Experimental Results  Standard multiclass classification setting  Predicting Unobserved Labels Conclusion 7

8 x1x1 x6x6 x2x2 x5x5 x4x4 x3x3 x7x7 X Y y1y1 Adding Constraints by Hidden Aspects Intuition: introduce structure on hidden variables

9 Adding Constraints Through Hidden Aspects x1x1 x6x6 x2x2 x5x5 x4x4 x3x3 x7x7 Y Z1 Z2 Z4 Z3 Z5 y1y1 Use constraints to capture the dependencies X

10 Objective Function of CCM 10 Weight Vector for “local” learners Aspect functions Penalty for violating the constraint. How far away is y from a “legal” assignment

11 11 Training and Inference Learning + Inference (L+I)  Ignore constraints during training Inference Based Training (IBT)  Consider constraints during training References to CCM (aka ILP formulation)  Roth&Yih04, Has been shown useful in the context of many NLP problems: SRL, Summarization; Co-reference; Information Extraction; Transliteration  07; Punyakanok et.al 05,08; Chang et.al 07,08; Clarke&Lapata06,07; Denise&Baldrige07;Goldwasser&Roth'08; Martin,Smith&Xing'09

12 12 Learning + Inference x1x1 x6x6 x2x2 x5x5 x4x4 x3x3 x7x7 Z1 Z2 Z5 Z4 Z3 f1(x)f1(x) f2(x)f2(x) f3(x)f3(x) f4(x)f4(x) f5(x)f5(x) X Y Learning + Inference (L+I) Learn models independently Learning + Inference (L+I) Learn models independently

13 13 1111Y’Local Predictions Inference Based Training Example: Perceptron-based Global Learning x1x1 x6x6 x2x2 x5x5 x4x4 x3x3 x7x7 f1(x)f1(x) f2(x)f2(x) f3(x)f3(x) f4(x)f4(x) f5(x)f5(x) X Y 11 YTrue Global Labeling 111 Y’ Apply Constraints:

14 Outline Car Command Text Categorization Task  Data and aspects  Unobserved labels Constrained Conditional Model (CCM)  Aspects variables to introduce constraints  Objective function  Training and Inference Experimental Results  Standard multiclass classification setting  Predicting Unobserved Labels Conclusion 14

15 Car Navigation Command Classification 15 XY

16 Evaluation Metrics Standard Accuracy  The percentage of correctly labeled examples Weighted Aspect-based Metric (WAM)  A weighted Hamming distance computed at the aspect level 16

17 Experiments and Evaluation Standard MCC Setting 17 AlgorithmAccuracy (%)WAM(%) Baseline67.8486.14 MCC(L+I) 71.1889.65 Error Reduction (%) 10.3925.32 AccuracyFless Kappa Human Annotation 75%0.764

18 Experiments and Evaluation Standard MCC Setting 18 AspectsCCMBaselineError Reduction(%) Topic 86.1481.5524.88 Action 88.3182.7232.35 Manner 89.9887.3520.79 Modifier 91.1589.5115.64 Detail 92.6889.5929.68

19 Experiments and Evaluation Predicting Unobserved Labels 19 AlgorithmAccuracy (%)WAM(%) Baseline0.0058.43 MCC(L+I) 28.1670.27 Error Reduction (%) 28.1628.48 Unobserved Label turn on GPS Observed Label 1. turn on the radio 2. GPS navigation

20 Conclusion Summary  Text Categorization with a meaningful, structured label space  A model that exploits the structure by adding hidden aspect variables Adding constraints and reformulating the task as a structure prediction problem Predicting unobserved new labels 20

21 Thank You! AND Questions? 21


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