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:
introduction

Context before the citation:
As a result, researchers have re-adopted the once-popular knowledge-rich approach, investigating a variety of semantic knowledge sources for common noun resolution, such as the semantic relations between two NPs (e.g., Ji et al. (2005)), their semantic similarity as computed using WordNet (e.g., Poesio et al. (2004)) or Wikipedia (Ponzetto and Strube, 2006), and the contextual role played by an NP (see Bean and Riloff (2004)). In fact, semantics plays a crucially important role in the resolution of common NPs, allowing us to identify the coreference relation between two lexically dissimilar common nouns (e.g., talks and negotiations) and to eliminate George W. Bush from the list of candidate antecedents of the city, for instance. While these approaches have been reasonably successful (see Mitkov (2002)), Kehler et al. (2004) speculate that deeper linguistic knowledge needs to be made available to resolvers in order to reach the next level of performance.

Citation Sentence:
As a result , researchers have re-adopted the once-popular knowledge-rich approach , investigating a variety of semantic knowledge sources for common noun resolution , such as the semantic relations between two NPs ( e.g. , Ji et al. ( 2005 ) ) , their semantic similarity as computed using WordNet ( e.g. , Poesio et al. ( 2004 ) ) or Wikipedia ( Ponzetto and Strube , 2006 ) , and the contextual role played by an NP ( see Bean and Riloff ( 2004 ) ) .

Context after the citation:
Another type of semantic knowledge that has been employed by coreference resolvers is the semantic class (SC) of an NP, which can be used to disallow coreference between semantically incompatible NPs. However, learning-based resolvers have not been able to benefit from having an SC agreement feature, presumably because the method used to compute the SC of an NP is too simplistic: while the SC of a proper name is computed fairly accurately using a named entity (NE) recognizer, many resolvers simply assign to a common noun the first (i.e., most frequent) WordNet sense as its SC (e.g., Soon et al. (2001), Markert and Nissim (2005)). It is not easy to measure the accuracy of this heuristic, but the fact that the SC agreement feature is not used by Soon et al.âs decision tree coreference classifier seems to suggest that the SC values of the NPs are not computed accurately by this first-sense heuristic. Motivated in part by this observation, we examine whether automatically induced semantic class knowledge can improve the performance of a learning-based coreference resolver, reporting evaluation results on the commonly-used ACE corefer- 
Background