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We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing fo...
Context in source publication
Context 1
... texts of the input RTE problem after entering the system via the user interface (see Fig- ure 1) go first through the syntactic processing and semantic construction of the first system module. To this end, they are analyzed by components of an XML-based middleware archi- tecture Heart of Gold (see Figure 2). It allows for a flexible integration of various shallow and deep linguistics-based and semantics-oriented NLP components, and thus constitutes a sufficiently complex research instrument for experimenting with novel processing strategies. ...
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Citations
... Another kind of deep methods involves first transferring natural language to logic representation and then conducting strict logic inference based on the logic representations (de Salvo Braz et al., 2006;Tatu and Moldovan, 2006;Wotzlaw and Coote, 2013). Through logic inference, some implicit knowledge behind the text can be mined. ...
... To my knowledge, MRS has not been used extensively in entailment decision systems. Notable examples of approaches that use MRSs are Wotzlaw and Coote (2013), and Bergmair (2010). ...
... In Wotzlaw and Coote (2013), the authors present an entailment recognition system which combines high-coverage syntactic and semantic text analysis with logical inference supported by relevant background knowledge. Their system combines deep and shallow linguistic analysis, and transforms the results into scope-resolved h 1 , h 4 :proper q 0 : 5 (ARG0 x 6 , RSTR h 5 , BODY h 7 ), h 8 :named 0 : 5 (ARG0 x 6 , CARG Japan), h 2 : deny v to 6 : 12 (ARG0 e 3 , ARG1 x 6 , ARG2 h 10 , ARG3 i 9 ), h 11 : be v there 19 : 22 (ARG0 e 12 , ARG1 x 13 ), h 14 :udef q 23 : 37 (ARG0 x 13 , RSTR h 15 , BODY h 16 ), h 17 : real a 1 23 : 27 (ARG0 e 18 , ARG1 x 13 ), h 17 : barrier n to 28 : 37 MRS representations. ...
... Our approach uses a semantic representation formalism called Minimal Recursion Semantics (MRS), which, to our knowledge, has not been used extensively in entailment decision systems. Notable examples of systems that use MRS are Wotzlaw and Coote (2013), and Bergmair (2010). In Wotzlaw and Coote (2013), the authors present an entailment recognition system which combines high-coverage syntactic and semantic text analysis with logical inference supported by relevant background knowledge. ...
... Notable examples of systems that use MRS are Wotzlaw and Coote (2013), and Bergmair (2010). In Wotzlaw and Coote (2013), the authors present an entailment recognition system which combines high-coverage syntactic and semantic text analysis with logical inference supported by relevant background knowledge. MRS is used as an intermediate format in transforming the results of the linguistic analysis into representations used for logical reasoning. ...
In this paper we present our participation in the Semeval 2014 task “Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment”. Our results demonstrate that using generic tools for semantic analysis is a viable option for a system that recognizes textual entailment. The invested effort in developing such tools allows us to build systems for reasoning that do not require training.
