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:
None

Context before the citation:
The M step then treats c as fixed, observed data and adjusts 0 until the predicted vector of total feature counts equals c, using Improved Iterative Scaling (Della Pietra et al., 1997; Chen and  â¢ If arc probabilities (or even A, v, Âµ, Ï) have loglinear parameterization, then the E step must compute c = Ei ecf(xi, yi), where ec(x, y) denotes the expected vector of total feature counts along a random path in fÎ¸ whose (input, output) matches (x, y). To learn the weights A, v, Âµ, Ï, count expected heads/tails for each coin.

Citation Sentence:
The M step then treats c as fixed , observed data and adjusts 0 until the predicted vector of total feature counts equals c , using Improved Iterative Scaling ( Della Pietra et al. , 1997 ; Chen and

Context after the citation:
Rosenfeld, 1999).12 For globally normalized, joint models, the predicted vector is ecf(E*, A*). If the log-linear probabilities are conditioned on the state and/or the input, the predicted vector is harder to describe (though usually much easier to compute).13 12IIS is itself iterative; to avoid nested loops, run only one iteration at each M step, giving a GEM algorithm (Riezler,1999). Alternatively, discard EM and use gradient-based optimization. 13For per-state conditional normalization, let Dj,a be the set of arcs from state j with input symbol a E E; their weights are normalized to sum to 1.
Uses