Open Soucre Graph Transducer Interpreter and Grammar Development Environment

Graph and tree transducers have been applied in many NLP areas―among them, machine translation, summarization, parsing, and text generation. In particular, the successful use of tree rewriting transducers for the introduction of syntactic structures in statistical machine translation contributed to their popularity. However, the potential of such transducers is limited because they do not handle graphs and because they ”consume” the source structure in that they rewrite it instead of leaving it intact for intermediate consultations. In this paper, we describe an open source tree and graph transducer interpreter, which combines the advantages of graph transducers and two-tape Finite State Transducers and surpasses the limitations of state-of-the-art tree rewriting transducers. Along with the transducer, we present a graph grammar development environment that supports the compilation and maintenance of graph transducer grammatical and lexical resources. Such an environment is indispensable for any effort to create consistent large coverage NLP-resources by human experts.

[1]  Jason Eisner,et al.  Learning Non-Isomorphic Tree Mappings for Machine Translation , 2003, ACL.

[2]  J. V. Rauff,et al.  Finite State Morphology , 2007 .

[3]  Yaser Al-Onaizan,et al.  Translation with Finite-State Devices , 1998, AMTA.

[4]  Michael Gamon,et al.  An Overview of Amalgam: A Machine-learned Generation Module , 2002, INLG.

[5]  Giorgio Busatto,et al.  An abstract model of hierarchical graphs and hierarchical graph transformation , 2001 .

[6]  Grzegorz Rozenberg,et al.  Handbook of Graph Grammars and Computing by Graph Transformations, Volume 1: Foundations , 1997 .

[7]  Hartmut Ehrig,et al.  Graph-Grammars: An Algebraic Approach , 1973, SWAT.

[8]  Kevin Knight,et al.  An Overview of Probabilistic Tree Transducers for Natural Language Processing , 2005, CICLing.

[9]  Yiannis Kompatsiaris,et al.  Towards content-oriented patent document processing , 2008 .

[10]  Daniel Gildea,et al.  Loosely Tree-Based Alignment for Machine Translation , 2003, ACL.

[11]  Leo Wanner,et al.  On Using a Parallel Graph Rewriting Formalism in Generation , 2001, EWNLG@ACL.

[12]  Srinivas Bangalore,et al.  Exploiting a Probabilistic Hierarchical Model for Generation , 2000, COLING.

[13]  Igor Mel’čuk,et al.  Lexical functions: a tool for the description of lexical relations in a lexicon , 1996 .

[14]  Daniel Marcu,et al.  A Noisy-Channel Approach to Question Answering , 2003, ACL.

[15]  J. Stanley Warford Computer Systems , 1998 .

[16]  Roger Levy,et al.  Tregex and Tsurgeon: tools for querying and manipulating tree data structures , 2006, LREC.

[17]  Alfred V. Aho,et al.  The Theory of Parsing, Translation, and Compiling , 1972 .

[18]  William C. Rounds,et al.  Mappings and grammars on trees , 1970, Mathematical systems theory.

[19]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[20]  James W. Thatcher,et al.  Generalized Sequential Machine Maps , 1970, J. Comput. Syst. Sci..

[21]  G. Olsder Mathematical Systems Theory , 2011 .

[22]  Benoit Lavoie,et al.  A Fast and Portable Realizer for Text Generation Systems , 1997, ANLP.

[23]  Srinivas Bangalore,et al.  Learning Dependency Translation Models as Collections of Finite-State Head Transducers , 2000, Computational Linguistics.

[24]  Alaa A. Kharbouch,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[25]  Leo Wanner,et al.  From measurement data to environmental information : MARQUIS - A Multimodal AiR QUality Information Service for the general public , 2007 .

[26]  Shankar Kumar,et al.  A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation , 2003, NAACL.

[27]  Dekai Wu,et al.  Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora , 1997, CL.