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Table 1: Classification of semantic representations in HPSG

in Basic Concepts of Lexical Resource Semantics
by Frank Richter, Manfred Sailer 2004
"... In PAGE 5: ... Table1 summarizes the possibilities that we are interested in. Each one of them is represented by a particular treatment of Ty2 as an object language.... In PAGE 16: ...NP ALC8BMBKDCCJC8B4DCB5CL everyone V ALCH ALCGBMCH B4ALDDBMCGB4ALDCBMreadBCB4DCBN DDB5B5B5 AY AR1 ALDDALCGBMCGB4ALDCBMreadBCB4DCBN DDB5B5 AY AR2 ALDDALDCBMreadBCB4DCBN DDB5 reads NP ALC9BMBLDDCJC9B4DDB5CL something VP ALCGBMBLDDCJCGB4ALDCBMreadBCB4DCBN DDB5B5CL AY AL CJALCH ALCGBMCH B4ALDDBMCGB4ALDCBMreadBCB4DCBN DDB5B5B5CLB4ALC9BMBLDDCJC9B4DDB5CLB5 S BLDDBKDCCJreadBCB4DCBN DDB5CL AY AL CJALCGBMBLDDCJCGB4ALDCBMreadBCB4DCBN DDB5B5CLCLB4ALC8BMBKDCCJC9B4DCB5CLB5 4 Discontinuous Representation: LRS In this section, we will introduce a new semantic meta-theory, Lexical Resource Semantics (LRS). The taxonomy of semantic systems in Table1 helps us to locate LRS with respect to the systems that we have sketched above. Just like LF-Ty2, LRS representations specify individual readings.... ..."
Cited by 11

Table 2: Examples of correlations in the semantic representations

in Category Specific Semantic Deficits In Focal And Widespread Brain Damage: A Computational Account
by Joseph T. Devlin, Laura M. Gonnerman, Elaine S. Andersen, Mark S. Seidenberg 1998
"... In PAGE 7: ... Natural kinds have reliably more intercorrelations than artifacts. Examples of correlated property pairs are shown in Table2 . The highly interactive nature of the architecture allowed these correlations to be stored as connection strengths within the semantic system (i.... ..."
Cited by 4

Table A1: semantics representation: Lexical Item Semantics

in Structural Priming as Implicit Learning: A Comparison of Models of Sentence Production
by Franklin Chang, Gary S. Dell, Kathryn Bock, Zenzi M. Griffin 2006
Cited by 3

Table A1: semantics representation: Lexical Item Semantics

in Abstract Structural Priming
by Franklin Chang, Franklin Chang

Table 5. Mapping from encoded surface text to semantic representation

in Generating English Plural Determiners from Semantic Representations: A Neural Network Learning Approach
by Gabriele Scheler, Tu Munchen 1996
"... In PAGE 10: ... This is repeated 81 times so that we get generalization gures for all 81 cases.The results for learning and for generalization are given in Table5 . Data on... ..."
Cited by 5

Table 6: Generation from automatically derived semantic representations 100%

in With raised eyebrows or the eyebrows raised? A Neural Network Approach to Grammar Checking for Definiteness
by Gabriele Scheler, Tu Munchen 1996
"... In PAGE 9: ... When we look at the relation between error per pattern and generation performance (cf. Table6 ), a clear picture emerges. While the generation function is fault- tolerant to a degree (app.... ..."
Cited by 1

Table 3.1 Query examples in NLP and their semantic representations

in unknown title
by unknown authors 2005

Table 4.1 Semantic representations of query types and query sentences

in unknown title
by unknown authors 2005

Table 5: Semantically Corresponding Representations of a Goal

in Delta Relations – Semantic Difference of Functional Descriptions
by Michael Stollberg, Uwe Keller 2006
"... In PAGE 36: ...4 are straight forward applicable for functionalities formalized in the ASS model. For demonstration purpose, Table5 shows the modelling of the goal for find- ing best restaurants in a city as a functional description as a functional description DG and its semantically corresponding representation as an FOL formula sim(DG) following the above definitions. Table 5: Semantically Corresponding Representations of a Goal... ..."
Cited by 1

Table 1: Relation Set Count (Total Counts include ex- amples that yielded semantic representations for both EDUs)

in Discourse Parsing: Learning FOL Rules based on Rich Verb Semantic Representations to automatically label Rhetorical Relations
by Rajen Subba
"... In PAGE 6: ... As we add more training data in the future, we will see if rules that are more elaborate than the ones in Figure 6 are learned . 4 Evaluation of the Discourse Parser Table1 shows the sets of relations for which we managed to obtain semantic representations (i.e.... ..."
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