Noun Phrase Coreference Resolution Algorithm

A Noun Phrase Coreference Resolution Algorithm is a Coreference Resolution Algorithm that can determine whether two noun phrases refer to the same entity.

Context:
- It can be implemented by a Noun Phrase Coreference Resolution System to solve a Noun Phrase Coreference Resolution Task.
Example(s):
- the one described in Ng (2010, 2007),
- the one described in Soon et al. (2001),
- …
Counter-Example(s):
See: Coreference Relation, Natural Language Processing, Natural Language Understanding, Markable.

References

2010

(Ng, 2010) ⇒ Vincent Ng. (2010). “Supervised Noun Phrase Coreference Research: The First Fifteen Years.” In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010).
- QUOTE: Noun phrase (NP) coreference resolution, the task of determining which NPs in a text or dialogue refer to the same real-world entity, has been at the core of natural language processing (NLP) since the 1960s. NP coreference is related to the task of anaphora resolution, whose goal is to identify an antecedent for an anaphoric NP (i.e., an NP that depends on another NP, specifically its antecedent, for its interpretation) [see van Deemter and Kibble (2000) for a detailed discussion of the difference between the two tasks]. Despite its simple task definition, coreference is generally considered a difficult NLP task, typically involving the use of sophisticated knowledge sources and inference procedures (Charniak, 1972).

2007

(Ng, 2007) ⇒ Vincent Ng. (2007). “Semantic Class Induction And Coreference Resolution.” In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007).
- QUOTE: In the past decade, knowledge-lean approaches have significantly influenced research in noun phrase (NP) coreference resolution — the problem of determining which NPs refer to the same real-world entity in a document. In knowledge-lean approaches, coreference resolvers employ only morpho-syntactic cues as knowledge sources in the resolution process (e.g., Mitkov (1998), Tetreault (2001)). While these approaches have been reasonably successful (see Mitkov (2002)), Kehler et al. (2004). speculate that deeper linguistic knowledge needs to be made available to resolvers in order to reach the next level of performance. In fact, semantics plays a crucially important role in the resolution of common NPs, allowing us to identify the coreference relation between two lexically dissimilar common nouns (e.g., talks and negotiations) and to eliminate George W. Bush from the list of candidate antecedents of the city, for instance. As a result, researchers have re-adopted the once-popular knowledge-rich approach, investigating a variety of semantic knowledge sources for common noun resolution, such as the semantic relations between two NPs (e.g., Ji et al. (2005)), their semantic similarity as computed using WordNet (e.g., Poesio et al. (2004)) or Wikipedia (Ponzetto and Strube, 2006), and the contextual role played by an NP (see Bean and Riloff (2004)).

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 (...)
  Specifically, a coreference relation denotes an identity of reference and holds between two textual elements known as markables, which can be definite noun phrases, demonstrative noun phrases, proper names, appositives, sub-noun phrases that act as modifiers, pronouns, and so on. Thus, our coreference task resolves general noun phrases and is not restricted to a certain type of noun phrase such as pronouns.(...) The ability to link coreferring noun phrases both within and across sentences is critical to discourse analysis and language understanding in general.