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 work of <cite>(Poon and Domingos, 2009</cite> ) models joint probability of the dependency tree and its latent semantic representation using Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) , selecting parameters (weights of first-order clauses) to maximize the probability of the observed dependency structures. For each sentence, the MLN induces a Markov network, an undirected graphical model with nodes corresponding to ground atoms and cliques corresponding to ground clauses. The MLN is a powerful formalism and allows for modeling complex interaction between features of the input (syntactic trees) and latent output (semantic representation), however, unsupervised learning of semantics with general MLNs can be prohibitively expensive. The reason for this is that MLNs are undirected models and when learned to maximize likelihood of syntactically annotated sentences, they would require marginalization over semantic representation but also over the entire space of syntactic structures and lexical units. Given the complexity of the semantic parsing task and the need to tackle large datasets, even approximate methods are likely to be infeasible.
["Motivation", "Background"]