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:
experiments

Context before the citation:
As stated before, the experiments are run in the ACE'04 framework (NIST, 2004) where the system will identify mentions and will label them (cfXXX Section 4) with a type (person, organization, etc), a sub-type (OrgCommercial, OrgGovernmental, etc), a mention level (named, nominal, etc), and a class (specific, generic, etc). We want to investigate the usefulness of stem ngram features in the mention detection system.

Citation Sentence:
As stated before , the experiments are run in the ACE '04 framework ( NIST , 2004 ) where the system will identify mentions and will label them ( cfXXX Section 4 ) with a type ( person , organization , etc ) , a sub-type ( OrgCommercial , OrgGovernmental , etc ) , a mention level ( named , nominal , etc ) , and a class ( specific , generic , etc ) .

Context after the citation:
Detecting the mention boundaries (set of consecutive tokens) and their main type is one of the important steps of our mention detection system. The score that the ACE community uses (ACE value) attributes a higher importance (outlined by its weight) to the main type compared to other subtasks, such as the mention level and the class. Hence, to build our mention detection system we spent a lot of effort in improving the first step: detecting the mention boundary and their main type. In this paper, we report the results in terms of precision, recall, and F-measure3.
Uses