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:
Various approaches for computing semantic relatedness of words or concepts have been proposed, e.g. dictionary-based (Lesk, 1986), ontology-based (Wu and Palmer, 1994; Leacock and Chodorow, 1998), information-based (Resnik, 1995; Jiang and Conrath, 1997) or distributional (Weeds and Weir, 2005).

Citation Sentence:
Various approaches for computing semantic relatedness of words or concepts have been proposed , e.g. dictionary-based ( Lesk , 1986 ) , ontology-based ( Wu and Palmer , 1994 ; Leacock and Chodorow , 1998 ) , information-based ( Resnik , 1995 ; Jiang and Conrath , 1997 ) or distributional ( Weeds and Weir , 2005 ) .

Context after the citation:
The knowledge sources used for computing relatedness can be as different as dictionaries, ontologies or large corpora. According to Budanitsky and Hirst (2006), there are three prevalent approaches for evaluating SR measures: mathematical analysis, applicationspecific evaluation and comparison with human judgments. Mathematical analysis can assess a measure with respect to some formal properties, e.g. whether a measure is a metric (Lin, 1998).4 However, mathematical analysis cannot tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application. The latter question is tackled by applicationspecific evaluation, where a measure is tested within the framework of a certain application, e.g. word sense disambiguation (Patwardhan et al., 2003) or malapropism detection (Budanitsky and Hirst, 2006).
Background