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:
Against the background of a growing interest in multilingual NLP, multilingual anaphora /coreference resolution has gained considerable momentum in recent years (Aone and McKee 1993; Azzam, Humphreys, and Gaizauskas 1998; Harabagiu and Maiorano 2000; Mitkov and Barbu 2000; Mitkov 1999; Mitkov and Stys 1997; Mitkov, Belguith, and Stys 1998). The last decade of the 20th century saw a number of anaphora resolution projects for languages other than English such as French, German, Japanese, Spanish, Portuguese, and Turkish. The inclusion of the coreference task in the Sixth and Seventh Message Understanding Conferences (MUC-6 and MUC-7) gave a considerable impetus to the development of coreference resolution algorithms and systems, such as those described in Baldwin et al. (1995), Gaizauskas and Humphreys (1996), and Kameyama (1997).

Citation Sentence:
Against the background of a growing interest in multilingual NLP , multilingual anaphora / coreference resolution has gained considerable momentum in recent years ( Aone and McKee 1993 ; Azzam , Humphreys , and Gaizauskas 1998 ; Harabagiu and Maiorano 2000 ; Mitkov and Barbu 2000 ; Mitkov 1999 ; Mitkov and Stys 1997 ; Mitkov , Belguith , and Stys 1998 ) .

Context after the citation:
Other milestones of recent research include the deployment of probabilistic and machine learning techniques (Aone and Bennett 1995; Kehler 1997; Ge, Hale, and Charniak 1998; Cardie and Wagstaff 1999; the continuing interest in centering, used either in original or in revised form (Abracos and Lopes 1994; Strube and Hahn 1996; Hahn and Strube 1997; Tetreault 1999); and proposals related to the evaluation methodology in anaphora resolution (Mitkov 1998a, 2001b). For a more detailed survey of the state of the art in anaphora resolution, see Mitkov (forthcoming). The papers published in this issue reflect the major trends in anaphora resolution in recent years. Some of them describe approaches that do not exploit full syntactic knowledge (as in the case of Palomar et al.'s and Stuckardt's work) or that employ machine learning techniques (Soon, Ng, and Lim); others present centering-based pronoun resolution (Tetreault) or discuss theoretical centering issues (Kibble).
Background