Coreference Resolution Algorithm: Difference between revisions
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↑ 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.
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* (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Coreference#Coreference_resolution Retrieved:2019-3-15. | * (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Coreference#Coreference_resolution 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 expression]]s 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]]. <P> 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: | ** 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 expression]]s 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]]. <P> 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. | ::: a. '''It''''s raining. | ||
::: b. '''It''''s really a shame. | ::: b. '''It''''s really a shame. | ||
::: c. '''It''' takes a lot of work to succeed. | ::: c. '''It''' takes a lot of work to succeed. | ||
::: d. Sometimes '''it''''s the loudest who have the most influence. | ::: d. Sometimes '''it''''s the loudest who have the most influence. | ||
:: Pleonastic uses are not considered referential, and so are not part of coreference. <ref> 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. </ref><references/> | :: Pleonastic uses are not considered referential, and so are not part of coreference. <ref> 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. </ref><references/> | ||
Latest revision as of 01:46, 28 January 2024
A Coreference Resolution Algorithm is a classification algorithm that can be implemented into a Coreference Resolution System (to solve a Coreference Resolution Task.
- Context:
- It can range from being a Heuristic Coreference Resolution Algorithm to being a Data-Driven Coreference Resolution Algorithm (such as a supervised coreference resolution algorithm).
- It can range from being a Rule-based Coreference Resolution ALgorithm, to being a Classifier-based Coreference Resolution Alogrithm.
- …
- Example(s):
- Counter-Example(s):
- See: Coreference Relation, Natural Language Processing, Markable.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Coreference#Coreference_resolution 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:
- 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. 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]
2001
- (Soon et al., 2001) ⇒ Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. (2001). “A Machine Learning Approach to Coreference Resolution of Noun Phrases.” In: Computational Linguistics Journal, 27(4).
- QUOTE: Coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world. It is an important subtask in natural language processing systems (...)
A prerequisite for coreference resolution is to obtain most, if not all, of the possible markables in a raw input text. To determine the markables, a pipeline of natural language processing (NLP) modules is used, as shown in Figure 1. They consist of tokenization, sentence segmentation, morphological processing, part-of-speech tagging, noun phrase identification, named entity recognition, nested noun phrase extraction, and semantic class determination. As far as coreference resolution is concerned, the goal of these NLP modules is to determine the boundary of the markables, and to provide the necessary information about each markable for subsequent generation of features in the training examples.
.
Figure 1 System architecture of natural language processing pipeline
- QUOTE: Coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world. It is an important subtask in natural language processing systems (...)
