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CSA3180: Natural Language Processing. Shallow Parsing Shallow Parsing and Chunking. Given grammar G and sentence A discover all valid parse trees for G that exactly cover A. Parsing Problem. S. VP. NP. V. Nom. Det. book. N. that. flight. The elephant is in the garden. S. VP. - PowerPoint PPT Presentation
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October 2005 CSA3180: Text Processing II 1
CSA3180: Natural Language Processing
Shallow Parsing
Shallow Parsing and Chunking
October 2006 csa3180: Parsing Algorithms 1 2
Parsing Problem• Given grammar G and sentence A discover all
valid parse trees for G that exactly cover A
S
VP
NPV
DetNom
Nbook
that
flight
October 2006 csa3180: Parsing Algorithms 1 3
The elephant is in the garden
I shot an elephant in my garden
NP
VP
NP
PP
NP
S
October 2006 csa3180: Parsing Algorithms 1 4
I own the garden
I shot an elephant in my garden
NP
VP
PP
NP
S
October 2006 csa3180: Parsing Algorithms 1 5
Parsing as Search
• Search within a space defined by– Start State– Goal State– State to state transformations
• Two distinct parsing strategies:– Top down– Bottom up
• Different parsing strategy, different state space, different problem.
• N.B. Parsing strategy ≠ search strategy
October 2005 CSA3180: Text Processing II 6
Shallow/Chunk Parsing
Goal: divide a sentence into a sequence of chunks.
• Chunks are non-overlapping regions of a text[I] saw [a tall man] in [the park].
• Chunks are non-recursive– A chunk can not contain other chunks
• Chunks are non-exhaustive– Not all words are included in chunks
October 2005 CSA3180: Text Processing II 7
Chunk Parsing Examples
• Noun-phrase chunking:[I] saw [a tall man] in [the park].
• Verb-phrase chunking:The man who [was in the park] [saw me].
• Prosodic chunking:
[I saw] [a tall man] [in the park].
• Question answering:– What [Spanish explorer] discovered [the
Mississippi River]?
October 2005 CSA3180: Text Processing II 8
Motivation
• Locating information– e.g., text retrieval
• Index a document collection on its noun phrases
• Ignoring information– Generalize in order to study higher-level patterns
• e.g. phrases involving “gave” in Penn treebank:– gave NP; gave up NP in NP; gave NP up; gave NP help; gave
NP to NP
– Sometimes a full parse has too much structure• Too nested• Chunks usually are not recursive
October 2005 CSA3180: Text Processing II 9
Representation
• BIO (or IOB)
Trees
October 2005 CSA3180: Text Processing II 10
Comparison with Full Parsing
• Parsing is usually an intermediate stage– Builds structures that are used by later stages of processing
• Full parsing is a sufficient but not necessary intermediate stage for many NLP tasks– Parsing often provides more information than we need
• Shallow parsing is an easier problem– Less word-order flexibility within chunks than between chunks– More locality:
• Fewer long-range dependencies• Less context-dependence• Less ambiguity
October 2005 CSA3180: Text Processing II 11
Chunks and Constituency
Constituents: [[a tall man] [ in [the park]]].
Chunks: [a tall man] in [the park].
• A constituent is part of some higher unit in the hierarchical syntactic parse
• Chunks are not constituents– Constituents are recursive
• But, chunks are typically subsequences of constituents– Chunks do not cross major constituent boundaries
October 2005 CSA3180: Text Processing II 12
Unchunking
• Remove any chunk with a given pattern– e.g., unChunkRule(‘<NN|DT>+’, ‘Unchunk NNDT’)– Combine with Chunk Rule <NN|DT|JJ>+
• Chunk all matching subsequences:– Input:
the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN
– Apply chunk rule
[the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN]– Apply unchunk rule
[the/DT little/JJ cat/NN] sat/VBD on/IN the/DT mat/NN
October 2005 CSA3180: Text Processing II 13
Chinking
• A chink is a subsequence of the text that is not a chunk.• Define a regular expression that matches the sequences
of tags in a chinkA simple chink regexp for finding NP chunks: (<VB.?>|<IN>)+
• First apply chunk rule to chunk everything– Input:
the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN
– ChunkRule('<.*>+', ‘Chunk everything’)
[the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN]– Apply Chink rule above:
[the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN]
October 2005 CSA3180: Text Processing II 14
Merging
• Combine adjacent chunks into a single chunk– Define a regular expression that matches the sequences of tags
on both sides of the point to be merged
• Example:– Merge a chunk ending in JJ with a chunk starting with NN
MergeRule(‘<JJ>’, ‘<NN>’, ‘Merge adjs and nouns’)
[the/DT little/JJ] [cat/NN] sat/VBD on/IN the/DT mat/NN
[the/DT little/JJ cat/NN] sat/VBD on/IN the/DT mat/NN
• Splitting is the opposite of merging
October 2005 CSA3180: Text Processing II 15
Python and NLTK
• Natural Language Toolkit (NLTK)
• http://nltk.sourceforge.net/
• NLTK Slides partly based on Diane Litman Lectures
• Chunk parsing slides partly based on Marti Hearst Lectures
October 2005 CSA3180: Text Processing II 16
Python for NLP
• Python is a great language for NLP:– Simple– Easy to debug:
• Exceptions• Interpreted language
– Easy to structure• Modules• Object oriented programming
– Powerful string manipulation
October 2005 CSA3180: Text Processing II 17
Python Modules and Packages
• Python modules “package program code and data for reuse.” (Lutz)– Similar to library in C, package in Java.
• Python packages are hierarchical modules (i.e., modules that contain other modules).
• Three commands for accessing modules:1.import2.from…import3.reload
October 2005 CSA3180: Text Processing II 18
Import Command
• The import command loads a module:# Load the regular expression module
>>> import re
• To access the contents of a module, use dotted names:
# Use the search method from the re module
>>> re.search(‘\w+’, str)
• To list the contents of a module, use dir:>>> dir(re)
[‘DOTALL’, ‘I’, ‘IGNORECASE’,…]
October 2005 CSA3180: Text Processing II 19
from...import
• The from…import command loads individual functions and objects from a module:
# Load the search function from the re module>>> from re import search
• Once an individual function or object is loaded with from…import, it can be used directly:
# Use the search method from the re module>>> search (‘\w+’, str)
October 2005 CSA3180: Text Processing II 20
Import vs. from...import
Import• Keeps module
functions separate from user functions.
• Requires the use of dotted names.
• Works with reload.
from…import• Puts module functions
and user functions together.
• More convenient names.
• Does not work with reload.
October 2005 CSA3180: Text Processing II 21
Reload
• If you edit a module, you must use the reload command before the changes become visible in Python:
>>> import mymodule
...
>>> reload (mymodule)
• The reload command only affects modules that have been loaded with import; it does not update individual functions and objects loaded with from...import.
October 2005 CSA3180: Text Processing II 22
NLTK Introduction
• The Natural Language Toolkit (NLTK) provides:– Basic classes for representing data relevant to natural
language processing.– Standard interfaces for performing tasks, such as
tokenization, tagging, and parsing.– Standard implementations of each task, which can be
combined to solve complex problems.
October 2005 CSA3180: Text Processing II 23
NLTK Example Modules
• nltk.token: processing individual elements of text, such as words or sentences.
• nltk.probability: modeling frequency distributions and probabilistic systems.
• nltk.tagger: tagging tokens with supplemental information, such as parts of speech or wordnet sense tags.
• nltk.parser: high-level interface for parsing texts.• nltk.chartparser: a chart-based implementation of
the parser interface.• nltk.chunkparser: a regular-expression based
surface parser.
October 2005 CSA3180: Text Processing II 24
Chunk Parsing in NLTK
• Chunk parsers usually ignore lexical content– Only need to look at part-of-speech tags
• Possible steps in chunk parsing– Chunking, unchunking– Chinking– Merging, splitting
• Evaluation– Compare to a Baseline– Evaluate in terms of
• Precision, Recall, F-Measure• Missed (False Negative), Incorrect (False Positive)
October 2005 CSA3180: Text Processing II 25
Chunk Parsing in NLTK
• Define a regular expression that matches the sequences of tags in a chunk
A simple noun phrase chunk regexp:(Note that <NN.*> matches any tag starting with NN)
<DT>? <JJ>* <NN.?>
• Chunk all matching subsequences:
the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN
[the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN]
• If matching subsequences overlap, first 1 gets priority