Phrase Chunking Task
A Phrase Chunking Task is a text chunking task where text chunks must be syntactic phrases.
- AKA: PCT, Syntactic Segmentation.
- Context:
- Input: a Linguistic Expression, typically an entire Linguistic Sentence.
- optional: whether Phrase Classification is required.
- optional: the Phrasal Category sought, e.g. only noun phrases, Base Noun Phrases, Verb Phrases, etc.
- output: a Tagged String with Tags that demarcate the (possible non-overlapping) Text Chunks (that correspond to Syntactic Phrases).
- optional: The Phrasal Category of each Phrase.
- Performance Measure:
- It can be solved by a Phrase Chunking System by means of a Phrase Chunking Algorithm.
- It can support a Parsing Task.
- It is an easier task than the Parsing Task.
- Input: a Linguistic Expression, typically an entire Linguistic Sentence.
- Example(s):
- a General Chunking Task, such as:
- (PCT)("He reckons the current account deficit will narrow to only $ 1.8 billion in September.”)
⇒ [NP He], [VP reckons], [NP the current account deficit], [VP will narrow], [PP to], [NP only # 1.8 billion], [PP in], [NP September].
- (PCT)("He reckons the current account deficit will narrow to only $ 1.8 billion in September.”)
- a Base NP Chunking Task.
- a BIO Chunking Task, such as the CoNLL-2000 Shared Task.
- …
- a General Chunking Task, such as:
- Counter-Example(s):
- a Text Chunking Task where the Chunks are not Syntactic Phrases, as in (Abney, 1989).
- (GCT)("I begin with an intuition: when I read a sentence, I read it a chunk at a time”) ⇒ ([I begin] [with an intuition]: [when I read] [a sentence], [I read it] [a chunk] [at a time])
- a Word Mention Segmentation Task.
- a Text Chunking Task where the Chunks are not Syntactic Phrases, as in (Abney, 1989).
- See: NLP Task, Linguistic Expression.
References
2002
- (Zhang, Damerau & Johnson, 2002) ⇒ T. Zhang, Fred Damerau, and D Johnson. (2002). “Text chunking based on a generalization of winnow.” In: The Journal of Machine Learning Research
2000
- (Tjong Kim Sang & Buchholz, 2000) ⇒ Erik Tjong Kim Sang, and Sabine Buchholz. (2000). “Introduction to the CoNLL-2000 Shared Task: Chunking.” In: Proceedings of CoNLL-2000.
1995
- (Ramshaw & Marcus, 1995) ⇒ Lance A. Ramshaw, and Mitch P. Marcus. (1995). “Text Chunking Using Transformation-based Learning.” In: Proceedings of the Third ACL Workshop on Very Large Corpora (WVLC 1995).
1989
- (Abney, 1989) ⇒ Steven P. Abney. (1989). “Parsing By Chunks.” In: The MIT Parsing Volume, 1988-89. Center for Cognitive Science, MIT.
- QUOTE: I begin with an intuition: when I read a sentence, I read it a chunk at a time. For example, the previous sentence breaks up something like this:
(1) [I begin] [with an intuition]: [when I read] [a sentence], [I read it] [a chunk] [at a time]
These chunks correspond in some way to prosodic patterns. It appears, for instance, that the strongest stresses in the sentence fall one to a chunk, and pauses are most likely to fall between chunks. … The work I would like to describe is an attempt to give content to these intuitions, and to show that parsing by chunks has distinct processing advantages, advantages that help explain why the human parser might adopt a chunk-by-chunk strategy. … A typical natural language parser processes text in two stages. A tokenizer/morphological analyzer converts a stream of characters into a stream of words, and the parser proper converts a stream of words into a parsed sentence, or a stream of parsed sentences. In a chunking parser, the syntactic analyzer is decomposed into two separate stages, which I call the chunker and the attacher. The chunker converts a stream of words into a stream of chunks, and the attacher converts the stream of chunks into a stream of sentences.
- QUOTE: I begin with an intuition: when I read a sentence, I read it a chunk at a time. For example, the previous sentence breaks up something like this: