NP Chunking Task

From GM-RKB
(Redirected from NP chunking task)
Jump to navigation Jump to search

An NP Chunking Task is a phrase chunking task that is restricted to the identification of all base noun phrases.



References

2009

2008

2007

2006

  • (Rus & Ravi, 2006) ⇒ Vasile Rus, and Sireesha Ravi. (2006). “Towards a Base Noun Phrase Parser using Web Counts.” In: Journal of Computing Sciences in Colleges, 21(5).
    • ABSTRACT: Syntactic parsing is an important processing step for various language processing applications including Information Extraction, Question Answering, and Machine Translation. Parsing base Noun Phrases is one particular parsing case that has not been addressed so far in the literature. In this paper we present a semester-long research project that aimed at investigating the base Noun Phrase parsing problem and efficiently implementing a base Noun Phrase parser based on a statistical model and web counts. Using web counts, instead of manually annotated data, to induce the parameters of the statistical model makes our method unsupervised. Although, this was a project for a graduate independent study class we plan to use it as a team project for an undergraduate class.

2003

  • (Sha & Pereira, 2003a) ⇒ Fei Sha, and Fernando Pereira. (2003). “Shallow Parsing with Conditional Random Fields.” In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (HLT-NAACL 2003). doi:10.3115/1073445.1073473
    • QUOTE: Figure 1 shows the base NPs in an example sentence. Following Ramshaw and Marcus (1995), the input to the NP chunker consists of the words in a sentence annotated automatically with part-of-speech (POS) tags. The chunker's task is to label each word with a label indicating whether the word is outside a chunk (O), starts a chunk (B), or continues a chunk (I). For example, the tokens in first line of Figure 1 would be labeled BIIBIIOBOBIIO.

      [Rockwell International Corp.] ['s Tulsa unit] said it signed a tentative agreement extending [its contract] with [Boeing Co.] to provide structural parts for [Boeing] 's [747 jetliners] .

2002

  • http://www.ai.mit.edu/projects/jmlr/papers/volume2/tks02a/html/node14.html
    • Noun phrase parsing is similar to noun phrase chunking but this time the goal is to find noun phrases at all levels. This means that just like in the clause identification task we need to be able to recognize embedded phrases. The following example sentence will illustrate this:
      • In (early trading ) in (Hong Kong ) (Monday ), (gold ) was quoted at (( $ 366.50 ) (an ounce ) ) .
    • This sentence contains seven noun phrases of which the one containing the final four words of the sentence consists of two embedded noun phrases. If we use the same approach as for clause identification, retrieving brackets of all phrase levels in one step and balancing these, we will probably not detect this noun phrase because it starts and ends together with other noun phrases. Therefore we will use a different approach here.
    • We will recover noun phrases at different levels by performing repeated chunking [Tjong Kim Sang(2000a)].

2001

2000

1999

1996

1995

  • (Ramshaw & Marcus, 1995) ⇒ Lance Ramshaw, and Mitch Marcus. (1995). “Text Chunking Using Transformation-based Learning.” In: Proceedings of the Third ACL Workshop on Very Large Corpora (WVLC 1995).
    • The goal of the "baseNP" chunks was to identify essentially the initial portions of nonrecursive noun phrases up to the head, including determiners but not including postmodifying prepositional phrases or clauses.
    • [less time], the [other hand], [binary addressing and instruction formats], a [purely binary computer].

1993

  • (Voutilainen, 1993) ⇒ Atro Voutilainen. (1993). “NPTool, a detector of English Noun Phrases.” In: Proceedings of the ACL-1993 Workshop on Very Large Corpora.

1992

1991

  • (Abney, 1991) ⇒ Steven P. Abney. (1991). “Parsing by chunks." In Berwick, Abney, and Tenny, editors, Principle-based Parsing. Kluwer Academic Publishers.