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:
None

Context before the citation:
The language chosen for semantic representation is a flat semantics along the line of (Bos, 1995; Copestake et al., 1999; Copestake et al., 2001). In contrast, the bottom feature structure encodes information that remains local to the node at which adjunction takes place. The top feature structure encodes information that needs to be percolated up the tree should an adjunction take place.

Citation Sentence:
The language chosen for semantic representation is a flat semantics along the line of ( Bos , 1995 ; Copestake et al. , 1999 ; Copestake et al. , 2001 ) .

Context after the citation:
However because we are here focusing on paraphrases rather than fine grained semantic distinctions, the underspecification and the description of the scope relations permitted by these semantics will here be largely ignored and flat semantics will be principally used as a convenient way of describing predicate/arguments and modifiers/modified relationships. Thus the semantic representations we assume are simply set of literals of the form Pn(x1, ... , xn) where Pn is a predicate of arity n and xi is either a constant or a unification variable whose value will be instantiated during processing. Semantic construction proceeds from the derived tree (Gardent and Kallmeyer, 2003) rather than â as is more common in TAG â from the derivation tree. This is done by associating each elementary tree with a semantic representation and by decorating relevant tree nodes with unification variables and constants occuring in associated semantic representation.
CompareOrContrast