Semantic Graph from English Sentences

In this paper we describe our progress towards building an Interlingua based machine translation system, by capturing the semantics of the source language sentences in the form of Universal Networking Language (UNL) graphs from which the target language sentences can be produced. There are two stages to the UNL graph generation: first, the conceptual arguments of a situation are identified in the form of semantically relatable sequences (SRS) which are potential candidates for linking with semantic relations; next, the conceptual relations such as instrument, source, goal, reason or agent are recognized, irrespective of their different syntactic configurations. The system has been tested against gold standard UNL expressions collected from various sources like Oxford Advanced Learners’ Dictionary, XTAG corpus and Framenet corpus. Results indicate the promise and effectiveness of our approach on the difficult task of interlingua generation from text.

[1]  Roger C. Schank,et al.  Conceptual dependency: A theory of natural language understanding , 1972 .

[2]  Martha Palmer,et al.  Verbnet: a broad-coverage, comprehensive verb lexicon , 2005 .

[3]  Josef Ruppenhofer,et al.  FrameNet: Theory and Practice , 2003 .

[4]  Daniel Jurafsky,et al.  Automatic Labeling of Semantic Roles , 2002, CL.

[5]  Treebank Penn,et al.  Linguistic Data Consortium , 1999 .

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

[7]  Pushpak Bhattacharyya,et al.  Lexical Resources for Semantics Extraction , 2008, LREC.

[8]  Toon Witkam.  DLT - an industrial R&D project for multilingual machine translation , 1988, COLING.

[9]  Noam Chomsky,et al.  Lectures on Government and Binding , 1981 .

[10]  W. J. Hutchins,et al.  Recent Developments in Machine Translation , 1988 .

[11]  Bonnie J. Dorr,et al.  Parameterization of the Interlingua in Machine Translation , 1992, COLING.

[12]  Yorick Wilks,et al.  ULTRA: A Multi-lingual Machine Translator , 1991, MTSUMMIT.

[13]  김두식,et al.  English Verb Classes and Alternations , 2006 .

[14]  Josef Ruppenhofer,et al.  FrameNet II: Extended theory and practice , 2006 .

[15]  Teruko Mitamura,et al.  The KANT System: Fast, Accurate, High-Quality Translation in Practical Domains , 1992, COLING.

[16]  Pushpak Bhattacharyya,et al.  Interlingua-based English–Hindi Machine Translation and Language Divergence , 2001, Machine Translation.

[17]  A. S. Hornby,et al.  Oxford Advanced Learners Dictionary Of Current English - 5/E , 2002 .

[18]  Christian Boitet,et al.  Pros and Cons of the Pivot and Transfer Approaches in Multilingual Machine Translation , 1988 .