The phrase "a relation system" is not commonly used in written English and may be unclear without context.
It could be used in discussions related to systems theory, sociology, or data management where relationships between entities are being analyzed.
Example: "In our study, we developed a relation system to better understand the interactions between different social groups."
Alternatives: "a relational framework" or "a relationship model".
In Section 3, we motivate the need for a new relation system and propose a feature-based design methodology in Section 3.1.
Our proposed design methodology tries to provide a methodology for designing a semantic relation system to avoid the problems caused by conventional semantic role labeling system mentioned in the Section 2.1 and to achieve the uniqueness and completeness criteria of relation identification.
In this paper, the mapping from requirements into functions is considered as a fuzzy relation system.
It results in a hierarchical relation system of 24 fine-grained semantic relations, practically achieving the completeness and uniqueness criteria of relation identification.
Therefore, in designing a semantic relation system for CPCs, we encounter the following problems: How many relations are needed to meet the completeness criterion, that is, are all semantic relations between CPCs covered by the proposed classification system?
In the following subsections, by studying the related work and summarizing previous systems and offering our viewpoints, we attempt to answer the above questions and come up with a workable design methodology and a practical semantic relation system.
Based on this methodology, we establish a succinct semantic relation system for consecutive predicative constituents for Chinese, which includes serial verb construction, discourse construction, and other constructions describing serial events.
Towards having a fully automatic system, we also design a relation labeling system for discovering the relation between opinions.
For the second setting, we adopt a 'pipeline' system architecture where we first employ a relation 'identification' system to determine whether a test instance possesses a temporal relation.
Therefore, our semantic relation system adopts a feature-based decision-making methodology described below to achieve the best labeling.
