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:
Thus, the second class of SBD systems employs machine learning techniques such as decision tree classifiers (Riley 1989), neural networks (Palmer and Hearst 1994), and maximum-entropy modeling (Reynar and Ratnaparkhi 1997). Automatically trainable software is generally seen as a way of producing systems that are quickly retrainable for a new corpus, for a new domain, or even for another language. Another well-acknowledged shortcoming of rule-based systems is that such systems are usually closely tailored to a particular corpus or sublanguage and are not easily portable across domains.

Citation Sentence:
Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) .

Context after the citation:
Machine learning systems treat the SBD task as a classification problem, using features such as word spelling, capitalization, suffix, and word class found in the local context of a potential sentence-terminating punctuation sign. Although training of such systems is completely automatic, the majority of machine learning approaches to the SBD task require labeled examples for training. This implies an investment in the data annotation phase.
Background