Abstract
Papers discussing anaphora resolution algorithms or systems usually focus on the intrinsic evaluation of the algorithm/system and not on the issue of extrinsic evaluation. In the context of anaphora resolution, extrinsic evaluation concerns the impact of an anaphora resolution module on a larger NLP system of which it is part. In this paper we explore the extent to which the well-known anaphora resolution system MARS [1] can improve the performance of three NLP applications: text summarisation, term extraction and text categorisation. On the basis of the results so far we conclude that the deployment of anaphora resolution has a positive albeit limited impact.
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Mitkov, R., Evans, R., Orăsan, C., Ha, L.A., Pekar, V. (2007). Anaphora Resolution: To What Extent Does It Help NLP Applications?. In: Branco, A. (eds) Anaphora: Analysis, Algorithms and Applications. DAARC 2007. Lecture Notes in Computer Science(), vol 4410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71412-5_13
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DOI: https://doi.org/10.1007/978-3-540-71412-5_13
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