You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.

Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.

Section Title:
related work

Context before the citation:
This approach is taken, for example, in LKB (Copestake 1992) where lexical rules are introduced on a par with phrase structure rules and the parser makes no distinction between lexical and nonlexical rules (Copestake 1993, 31). Another common approach to lexical rules is to encode them as unary phrase structure rules.

Citation Sentence:
This approach is taken , for example , in LKB ( Copestake 1992 ) where lexical rules are introduced on a par with phrase structure rules and the parser makes no distinction between lexical and nonlexical rules ( Copestake 1993 , 31 ) .

Context after the citation:
A similar method is included in PATR-II (Shieber et al. 1983) and can be used to encode lexical rules as binary relations in the CUF system (Dorre and Eisele 1991; Done and Dorna 1993b) or the TFS system (Emele and Zajac 1990; Emele 1994). The covariation approach described in this paper can be viewed as a domain-specific refinement of such a treatment of lexical rules. The encoding of lexical rules used in the covariation approach is related to the work of van Noord and Bouma (1994), who describe the hand-encoding of a single lexical rule as definite relations and show how these relations can be used to constrain a lexical entry. The covariation approach builds on this proposal and extends it in three ways: First, the approach shows how to detect and encode the interaction of a set of lexical rules.
CompareOrContrast