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Jens-Uwe Moller Natural Language Systems Division, Dept. of Computer Science, Univ. of Hamburg

Towards Learning Dialogue Structures from Speech Data and Domain Knowledge: Challenges to Conceptual Clustering using Multiple and Complex Knowledge Source . Jens-Uwe Moller Natural Language Systems Division,

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Jens-Uwe Moller Natural Language Systems Division, Dept. of Computer Science, Univ. of Hamburg

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  1. Towards Learning Dialogue Structures from Speech Data and Domain Knowledge: Challenges to Conceptual Clustering using Multiple and Complex Knowledge Source Jens-Uwe Moller Natural Language Systems Division, Dept. of Computer Science, Univ. of Hamburg

  2. Overview • Dialog modeling based on a set of units called dialog act • Dialog acts from theory doesn’t fit with a specific domain • Labeling dialog is time consuming and subjective • learn an application specific dialog acts from speech data using conceptual clustering

  3. The learning task • Learning dialog acts from turns • Unsupervised classification (no prior definition of dialog acts is given) • Hierarchy classification with inspectable classifying rules

  4. Features • Domain knowledge: structure of task, task knowledge represented by goals and plans • Word recognizer: word hypotheses • Prosodic data: Pause & Stress mark important unit • Lexical semantics • Syntax (less important in spoken dialog) • Semantics (larger units of lexical semantics)

  5. COWEB • Symbolic machine learning algorithm • Build a classification tree • Distinction between subnodes are made from a function overall attribute • Support probabilistic data • Support multiple overlapping hierarchies (for ambiguous case) • Can handle multiple entries of one attribute (e.g. stream of words)

  6. COWEB (2) • Learning from simultaneous events • Learn from structure data: Conceptual Graphs. • Learn case descriptions from terminological descriptions • Subsumption = correclation criterion over structured data. e.g. subsumption of individuals to classes

  7. Andrew Pargellis, Eric Fosler-Lussier, Alexandros Potamianos, Chin-Hui LeeDialogue Systems Research Dept., Bell Labs, Lucent Technologies Murray Hill, NJ, USA Metrics for Measuring Domain Independence of Semantic Classes

  8. Introduction • Employ semantic classes (concepts) from another domain • Need to identify domain-independent concepts base on comparison across domain • Domain-independent concepts should occur in similar syntactic (lexical) contexts across domains

  9. Comparing concepts across domains • Concept-comparison method • Concept-projection method

  10. Concept-comparison method • Find the similarity between all pairs of concepts across the two domains • Two concepts are similar if their respective bigram contexts are similar • Use left and right context bigram language models

  11. Kullback-Leibler (KL) distance • Compare how san francisco and newark are used in the Travel domain with how comedies and westerns are used in the Movie domain • Distance between two concepts

  12. Concept-projection method • How well a single concept from one domain is represented in another domain. • How the words comedies and westerns are used in both domains • Useful for identifying the degree of domain-independence for a particular concept.

  13. Result: Concept-comparison

  14. Result: Concept-projection

  15. Concept Example

  16. Semi-Automatic Acquisition of Domain-Specific Semantic Structures Siu K.C., Meng H.M. Human-Computer Communications Laboratory Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

  17. Grammar induction • Use unannotated corpora • Portable across domain & language • Output grammar has reasonable coverage of within-domain data and reject out-of-domain data • Amenable to interactive refinement by human • Support optional injection of prior knowledge

  18. Spatial clustering • Use kullback-liebler distance. • use left and right context. • Consider word with pre-set minimum occurrence. (set to 5) • use left and right context. Consider word w1, w2 (later be c1, c2) pair-wise for words that have a least pre-set minimum occurrence. (set to 5)

  19. Temporal clustering • Use Mutual Information (MI). • N-highest MI pairs are clustered (N=5 in experiment) • Do spatial clustering and temporal clustering iteratively • Post-process by human

  20. Automatic Concept identification In goal-oriented conversations Ananlada Chotimongkol and Alexander I. Rudnicky Language Technologies Institute Carnegie Mellon University

  21. Concept identification • First step towards the goal of automatically inferring domain ontologies • Goal-oriented human-human conversation has a clear structure • This structure can be used to automatically identify domain topics, e.g. dialog classfication

  22. Clustering algorithm • Hierarchical clustering • Mutual information based • Criterion=minimize the loss of average mutual information • Kullback-Lierbler based • Criterion=word pair with minimum distance

  23. Evaluation metrics • Reference concept from class-based n-gram model • Cluster concept=majority concept • Precision • Recall • Singularity score (SS) • Quality score (QS)

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