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:
method

Context before the citation:
There are many plausible representations, such as pairs of trees from synchronous tree adjoining grammars (Abeille et al. 1990; Shieber 1994; Candito 1998), lexical conceptual structures (Dorr 1992) and WordNet synsets (Fellbaum 1998; Vossen 1998). The above equation holds regardless of how we represent concepts. ways to generate an assignment of size 1.6 It follows that the probability of generating a pair of bags (B1, B2) with a particular assignment A of size 1 is

Citation Sentence:
There are many plausible representations , such as pairs of trees from synchronous tree adjoining grammars ( Abeille et al. 1990 ; Shieber 1994 ; Candito 1998 ) , lexical conceptual structures ( Dorr 1992 ) and WordNet synsets ( Fellbaum 1998 ; Vossen 1998 ) .

Context after the citation:
Of course, for a representation to be used, a method must exist for estimating its distribution in data. A useful representation will reduce the entropy of the trans distribution, which is conditioned on the concept distribution as shown in Equation 10. This topic is beyond the scope of this article, however. I mention it only to show how the models presented here may be used as building blocks for models that are more psycholinguistically sophisticated.
Background