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:
This result is consistent with other works using this model with these features (Andrews et al., 2009; Silberer and Lapata, 2012). The 2D models employing feature norms and association norms do significantly better than the text-only model (two-tailed t-test). Table 1 shows our results for each of our selected models with our compositionality evaluation.

Citation Sentence:
This result is consistent with other works using this model with these features ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) .

Context after the citation:
We also see that the SURF visual words are able to provide notable, albeit not significant, improvements over the text-only modality. This confirms that the low-level BoVW features do carry semantic information, and are useful to consider individually. The GIST vectors, on the other hand, perform almost exactly the same as the text-only model. These features, which are usually more useful for comparing overall image likeness than object likeness, do not individually contain semantic information useful for compositionality prediction.
CompareOrContrast