Your task is to classify the citation intent within the following provided text from a computational linguistics research paper. The cited work is demarcated by "<cite>" and "</cite>". Determine the purpose of the cited work by selecting from the listed categories:

- Background: The cited paper underpins the subject matter.
- Motivation: The cited paper inspires or provides a rationale for the current research.
- Uses: The current work utilizes concepts or tools from the cited paper.
- Extends: The current work advances ideas or methods from the cited paper.
- Similarities: The current work identifies commonalities with the cited paper.
- Differences: The current work delineates its distinction from the cited paper.
- FutureWork: The cited paper is acknowledged as groundwork for prospective research.

Indicate the intents by listing them in a `json` array, e.g. ["Background", "Uses"]. More than one intent may be applicable. Do not include any extraneous text in your response.

Context with Citation:
In order to overcome this problem, <cite>(Poon and Domingos, 2009</cite> ) group parameters and impose local normalization constraints within each group. Given these normalization constraints, and additional structural constraints satisfied by the model, namely that the clauses should be engineered in such a way that they induce treestructured graphs for every sentence, the parameters can be estimated by a variant of the EM algorithm. The class of such restricted MLNs is equivalent to the class of directed graphical models over the same set of random variables corresponding to fragments of syntactic and semantic structure. Given that the above constraints do not directly fit into the MLN methodology, we believe that it is more natural to regard their model as a directed model with an underlying generative story specifying how the semantic structure is generated and how the syntactic parse is drawn for this semantic structure. This view would facilitate understanding what kind of features can easily be integrated into the model, simplify application of non-parametric Bayesian techniques and expedite the use of inference techniques designed specifically for directed models. Our approach makes one step in this direction by proposing a non-parametric version of such generative model.
["Motivation", "Background", "Differences"]