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:
related work

Context before the citation:
Griffiths et al. (2007) helped pave the path for cognitive-linguistic multimodal research, showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis (Deerwester et al., 1990) in the prediction of association norms. cue word and name the first (or several) associated words that come to mind (e.g., Nelson et al. (2004)), and feature norms, where subjects are given a cue word and asked to describe typical properties of the cue concept (e.g., McRae et al. (2005)). 1http://stephenroller.com/research/ emnlp13

Citation Sentence:
Griffiths et al. ( 2007 ) helped pave the path for cognitive-linguistic multimodal research , showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis ( Deerwester et al. , 1990 ) in the prediction of association norms .

Context after the citation:
Andrews et al. (2009) furthered this work by showing that a bimodal topic model, consisting of both text and feature norms, outperformed models using only one modality on the prediction of association norms, word substitution errors, and semantic interference tasks. In a similar vein, Steyvers (2010) showed that a different feature-topic model improved predictions on a fill-in-the-blank task. Johns and Jones (2012) take an entirely different approach by showing that one can successfully infer held out feature norms from weighted mixtures based on textual similarity. Silberer and Lapata (2012) introduce a new method of multimodal integration based on Canonical Correlation Analysis, and performs a systematic comparison between their CCA-based model and others on association norm prediction, held out feature prediction, and word similarity.
Background