Sentence examples for distinguish the relation from inspiring English sources

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In this paper, we aim to distinguish the relation between both inter- or intra-sentential CPCs; not only discourse construction and SVC are included but also sentences modified by a prepositional phrase or a complemental phrase are all taken into account.

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In Literal Equivalence we distinguish the relations of Token-Token, which appear when different modalities refer exactly to the same entity, uniquely identified as such, and Type-Token, including cases where one modality refers to a class of entities and the other refers to one or more members of the class.

Spearman's correlation and factor analysis were used to distinguish the statistical relation between different ions and contamination source in the study area.

The book aims to show that Brentano distinguishes the intentional relation from two other relations with which it might be confused, namely, causality and reference, which also hold between thoughts and their objects.

From the biological point of view, our findings strongly support the efforts to discover reliable methods for the classification of subjects with pain, and encourages speculation about possible ways to distinguish the patients' states in relation to the proteomic pain pattern hidden in their CSF.

('Weak reduction' is a technical term used in CL to distinguish this relation on the set of terms from some other relations, one of which is called 'strong reduction'.) A term that is either of the form SMNP or of the form KMN is a redex, and the leading combinators (S and K, respectively) are the heads of the redexes.

By adopting rights discourses with specific content of property rights, the actors strove to distinguish the adjustment of property relations based on laws and the compensation of allocation and resettlement based on administrative directives.

Firstly, we can distinguish the directions of social relations.

Moreover, Jürges relation cannot distinguish the difference between CHTC of different building wall surfaces, i.e., windward, leeward and side walls, but expresses the CHTC for walls generally.

To distinguish the impact of exercise in relation to graded exposures (i.e. the different levels of health graded in the frailty index) with four different outcomes (improved health status, same status, worse status or death) we employed a multi-state model [36], [37], [38] (Appendix S1).

Our algorithms, Causal Predictor (CP) and Relaxed Causal Predictor (RCP) distinguish the direct and indirect causality relations from the non-causal interactions, thus significantly reducing the number of miss-predicted edges.

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