Hidden Markov model-based supertagging in a user-initiative dialogue system

In this paper we outline the advantages of deploying a shallow parser based on Supertagging in an automatic dialogue system in a call center that basically leaves the initiative with the user as far as (s)he wants (in the literature called user‐ initiative or adaptive in contrast to system‐initiativedialogue systems). The Supertagger relies on a Hidden Markov model and is trained with German input texts. The entire design of a Hidden Markov‐based Supertagger with trigrams builds the central issue of this paper. The evaluation of our German Supertagger lags behind the English one. Some of the reasons will be addressed later on. Nevertheless shallow parsing with the Supertags increases the accuracy compared to a basic version of KoHDaS that only relies on recurrent plausibility networks.

[1]  R. F. Brown,et al.  PERFORMANCE EVALUATION , 2019, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).

[2]  Srinivas Bangalore,et al.  Complexity of lexical descriptions and its relevance to partial parsing , 1997 .

[3]  Steven Abney,et al.  Parsing By Chunks , 1991 .

[4]  Eric Brill,et al.  Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach , 1993, ACL.

[5]  Taylor L. Booth,et al.  Probabilistic Representation of Formal Languages , 1969, SWAT.

[6]  Karin Harbusch,et al.  Modelling user-initiative in an automatic help desk system , 1910 .

[7]  Penelope Sibun,et al.  A Practical Part-of-Speech Tagger , 1992, ANLP.

[8]  S. Eddy.  Hidden Markov models. , 1996, Current opinion in structural biology.

[9]  Srinivas Bangalore,et al.  Performance Evaluation of Supertagging for Partial Parsing , 2000 .

[10]  David Flanagan,et al.  Java in a Nutshell , 1996 .

[11]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[12]  F ChenStanley,et al.  An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.

[13]  G. A. Leng.  ON POPULATION. , 1963, Singapore medical journal.

[14]  Ralph Grishman,et al.  The NYU System for MUC-6 or Where’s the Syntax? , 1995, MUC.

[15]  Srinivas Bangalore,et al.  Supertagging: An Approach to Almost Parsing , 1999, CL.

[16]  I. Good.  THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS , 1953 .

[17]  David M. Magerman.  Statistical Decision-Tree Models for Parsing , 1995, ACL.

[18]  Slava M. Katz,et al.  Estimation of probabilities from sparse data for the language model component of a speech recognizer , 1987, IEEE Trans. Acoust. Speech Signal Process..

[19]  Richard M. Schwartz,et al.  Coping with Ambiguity and Unknown Words through Probabilistic Models , 1993, CL.

[20]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[21]  Srinivas Bangalore.  Transplanting supertags from English to Spanish , 1998, TAG+.

[22]  Eugene Charniak,et al.  Statistical language learning , 1997 .

[23]  Srinivas Bangalore,et al.  The Institute For Research In Cognitive Science Disambiguation of Super Parts of Speech ( or Supertags ) : Almost Parsing by Aravind , 1995 .

[24]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[25]  Bernard Mérialdo,et al.  Tagging English Text with a Probabilistic Model , 1994, CL.

[26]  SchwartzRichard,et al.  Coping with ambiguity and unknown words through probabilistic models , 1993 .

[27]  Eric Brill,et al.  Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.

[28]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[29]  Marilyn A. Walker,et al.  Evaluating Response Strategies in a Web-Based Spoken Dialogue Agent , 1998, ACL.

[30]  John D. Lafferty,et al.  Towards History-based Grammars: Using Richer Models for Probabilistic Parsing , 1993, ACL.

[31]  Eugene Charniak,et al.  Statistical Parsing with a Context-Free Grammar and Word Statistics , 1997, AAAI/IAAI.

[32]  Robert C. Berwick,et al.  Principle-Based Parsing: Computation and Psycholinguistics , 1991 .

[33]  Andrés Montoyo,et al.  Combining Supervised-Unsupervised Methods for Word Sense Disambiguation , 2002, CICLing.

[34]  Virginia Teller.  Review of Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition by Daniel Jurafsky and James H. Martin. Prentice Hall 2000. , 2000 .

[35]  Douglas E. Appelt,et al.  FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text , 1997, ArXiv.

[36]  Michael Collins,et al.  A New Statistical Parser Based on Bigram Lexical Dependencies , 1996, ACL.

[37]  Manny Rayner,et al.  Language-Processing Strategies and Mixed-Initiative Dialogues , 1999, Electron. Trans. Artif. Intell..

[38]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[39]  Stefan Wermter.  Hybrid Connectionist Natural Language Processing , 1994 .

[40]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[41]  Aravind K. Joshi,et al.  Mathematical and computational aspects of lexicalized grammars , 1990 .

[42]  Bernard Merialdo,et al.  Hidden Markov Models , 1999 .

[43]  Douglas E. Appelt,et al.  FASTUS: A Finite-state Processor for Information Extraction from Real-world Text , 1993, IJCAI.

[44]  John Anderson,et al.  Pragmatic User Modelling in a Commercial Software System , 1997 .