February 2007CSA3050: Tagging III and Chunking 1 CSA2050: Natural Language Processing Tagging 3 and Chunking Transformation Based Tagging Chunking

February 2007 CSA3050: Tagging III and Chunking

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CSA2050: Natural Language Processing

Tagging 3 and Chunking

• Transformation Based Tagging• Chunking


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Tagging 3 and Chunking Lecture

• Slides based on Mike Rosner and Marti Hearst notes

• Additions from NLTK tutorials


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3 Approaches to Tagging

1. Rule-Based Tagger: ENGTWOL Tagger(Voutilainen 1995)

2. Stochastic Tagger: HMM-based Tagger

3. Transformation-Based Tagger: Brill Tagger(Brill 1995)


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Transformation-Based Tagging

• A combination of rule-based and stochastic tagging methodologies:– like rule-based tagging: rules are used to

specify tags in a certain environment;– like stochastic tagging: machine learning

is used.– Transformation-Based Learning (TBL)


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Transformation Based Error Driven Learning

unannotatedtext

initialstate

annotatedtext

TRUTH learner

transformationrulesdiagram after Brill (1996)


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TBL Requirements

• Initial State Annotator

• List of allowable transformations

• Scoring function

• Search strategy


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Initial State Annotation

• Input– Corpus– Dictionary– Frequency counts for each entry

• Output– Corpus tagged with most frequent tags


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TBL Requirements




• Search strategy


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Transformations

Each transformation comprises• A source tag• A target tag• A triggering environment

Example• NN• VB• Previous tag is TO


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More Examples

Source tag Target Tag Triggering Environment

NN VB previous tag is TO

VBP VB one of the three previous tags is MD

JJR RBR next tag is JJ

VBP VB one of the two previous words is n’t


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Allowable transforms based on fixed schemas

Schema ti-3 ti-2 ti-1 ti ti+1 ti+2 ti+3

1 *2 *3 *4 *5 *6 *7 *8 *9 *


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Set of Possible Transformations

The set of possible transformations is enumerated by allowing

• every possible tag or word• in every possible slot• in every possible schema

This set can get quite large


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TBL Requirements




• Search strategy


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Scoring Function

For a given tagging state of the corpusFor a given transformation

For every word position in the corpus• If the rule applies and yields a correct tag,

increment score by 1• If the rule applies and yields an incorrect tag,

decrement score by 1


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TBL Requirements




• Search strategy


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The Basic Algorithm

• Label every word with its most likely tag

• Repeat the followingwhile improvement > threshold– Examine every possible transformation,

selecting the one that results in the most improved tagging

– Retag the data according to this rule– Append this rule to output list

• Return output list of transformations


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TBL: Remarks• Execution Speed: TBL tagger is slower

than HMM approach.• Learning Speed is slow: Brill’s

implementation over a day (600k tokens)

BUT …

• Learns small number of simple, non-stochastic rules

• Can be made to work faster with Finite State Transducers


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Tagging Unknown Words

• New words added to (newspaper) language 20+ per month

• Plus many proper names …• Increases error rates by 1-2%• Methods

Assume the unknowns are nouns. Assume the unknowns have a probability

distribution similar to words occurring once in the training set.

Use morphological information, e.g. words ending with –ed tend to be tagged VBN.


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Evaluation

• The result is compared with a manually coded “Gold Standard”– Typically accuracy reaches 95-97%– This may be compared with the result for a

baseline tagger (one that uses no context).

• Important: 100% accuracy is impossible even for human annotators.


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A word of caution

• 95% accuracy: every 20th token wrong

• 96% accuracy: every 25th token wrong– an improvement of 25% from 95% to 96% ???




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How much training data is needed?

• When working with the STTS (50 tags) we observed

• a strong increase in accuracy when testing on 10´000, 20´000, …, 50´000 tokens,

• a slight increase in accuracy when testing on up to 100´000 tokens,

• hardly any increase thereafter.


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Summary

• Tagging decisions are conditioned on a wider range of events that HMM models mentioned earlier. For example, left and right context can be used simultaneously.

• Learning and tagging are simple, intuitive and understandable.

• Transformation-based learning has also been applied to sentence parsing.


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The Three Approaches Compared

• Rule Based– Hand crafted rules– It takes too long to come up with good rules– Portability problems

• Stochastic– Find sequence with highest probability (Viterbi)– Result of training not accessible to humans– Large storage needs for intermediate results whilst training

• Transformation– Rules are learned– Small number of rules– Rules can be inspected and modified by humans


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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


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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]?


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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


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Representation

• BIO (or IOB)

Trees


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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


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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


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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)


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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


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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:


– 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


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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:


– 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]


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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


• Splitting is the opposite of merging


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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


• Splitting is the opposite of merging


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Next Sessions…

• NLTK Exercises

Documents

February 2007CSA3050: Tagging III and Chunking 1 CSA2050: Natural Language Processing Tagging 3 and Chunking Transformation Based Tagging Chunking