Coreference Clustering Task

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A Coreference Clustering Task is a clustering task where all referencers must be grouped into coreference clusters (that share a coreference relation).



  • (Wikipedia, 2019) ⇒ Retrieved:2019-3-15.
    • In computational linguistics, coreference resolution is a well-studied problem in discourse. To derive the correct interpretation of a text, or even to estimate the relative importance of various mentioned subjects, pronouns and other referring expressions must be connected to the right individuals. Algorithms intended to resolve coreferences commonly look first for the nearest preceding individual that is compatible with the referring expression. For example, she might attach to a preceding expression such as the woman or Anne, but not to Bill. Pronouns such as himself have much stricter constraints. Algorithms for resolving coreference tend to have accuracy in the 75% range. As with many linguistic tasks, there is a tradeoff between precision and recall.

      A classic problem for coreference resolution in English is the pronoun it, which has many uses. It can refer much like he and she, except that it generally refers to inanimate objects (the rules are actually more complex: animals may be any of it, he, or she; ships are traditionally she; hurricanes are usually it despite having gendered names). It can also refer to abstractions rather than beings: "He was paid minimum wage, but didn't seem to mind it." Finally, it also has pleonastic uses, which do not refer to anything specific:

a. It's raining.
b. It's really a shame.
c. It takes a lot of work to succeed.
d. Sometimes it's the loudest who have the most influence.
Pleonastic uses are not considered referential, and so are not part of coreference. [1]
  1. Li et al. (2009) have demonstrated high accuracy in sorting out pleonastic it, and this success promises to improve the accuracy of coreference resolution overall.
  2. 2015

    • (Sawhney & Wang, 2015) ⇒ Kartik Sawhney, and Rebecca Wang. (2015). “Coreference Resolution.” f
      • Overview - Coreference resolution refers to the task of clustering different mentions referring to the same entity. This is particularly useful in other NLP tasks, including retrieving information about specific named entities, machine translation, among others. In this report, we discuss our approach, implementation and observations for a few baseline systems, a rule-based system, and a classifier-based system. To quantify the effectiveness of our implementation, we use the MUC and B^3 measures (precision, recall and F1) for coreference evaluation. The difference in the two scoring metrics in how they define a coreference set within a text (in terms of links or in terms of classes or clusters) results in interesting observations as we discuss in the report.