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:
This section, which elaborates on preliminary results reported in Demner-Fushman and Lin (2005), describes extraction algorithms for population, problems, interventions, outcomes, and the strength of evidence. The automatic extraction of PICO elements from MEDLINE citations represents a key capability integral to clinical question answering.

Citation Sentence:
This section , which elaborates on preliminary results reported in Demner-Fushman and Lin ( 2005 ) , describes extraction algorithms for population , problems , interventions , outcomes , and the strength of evidence .

Context after the citation:
For an example of a completely annotated abstract, see Figure 2. Each individual PICO extractor takes as input the abstract text of a MEDLINE citation and identifies the relevant elements: Outcomes are complete sentences, while population, problems, and interventions are short noun phrases. Our knowledge extractors rely extensively on MetaMap (Aronson 2001), a system for identifying segments of text that correspond to concepts in the UMLS Metathesaurus. Many of our algorithms operate at the level of coarser-grained semantic types called Semantic Groups (McCray, Burgun, and Bodenreider 2001), which capture higher-level generalizations about entities (e.g., CHEMICALS & DRUGS).
Extends