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:
The formula of our model is the same as <cite>Ninomiya et al. (2006)</cite><cite>'s model 3</cite>. But, <cite>their model</cite> is not a probabilistic model with a reference distribution. Both our model and <cite>their model</cite> consist of the probabilities for lexical entries (= p model1 (T |w)) and the probabilities for phrase structures (= the rest of each formula). The only difference between our model and <cite>their model</cite> is the way of how to train model parameters for phrase structures. In both our model and <cite>their model</cite>, the parameters for lexical entries (= the parameters of p model1 (T |w)) are first estimated from the word and POS sequences independently of the parameters for phrase structures. That is, the estimated parameters for lexical entries are the same in both models, and hence the probabilities of p model1 (T |w) of both models are the same. Note that the parameters for lexical entries will never be updated after this estimation stage; i.e., the parameters for lexical entries are not estimated in the same time with the parameters for phrase structures. The difference of our model and <cite>their model</cite> is the estimation of parameters for phrase structures.
["Similarities", "Differences"]