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Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation

Masters Thesis : Saif Mohammad Advisor : Dr. Ted Pedersen

University of Minnesota, DuluthDate: August 1, 2003

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Path Map

Introduction Background Data Experiments Conclusions

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Word Sense DisambiguationHarry cast a bewitching spell

Humans immediately understand spell to mean a charm or incantation

reading out letter by letter or a period of time ? Words with multiple senses – polysemy, ambiguity

Utilize background knowledge and context

Machines lack background knowledge Automatically identifying the intended sense of a word in

written text, based on its context, remains a hard problem Features are identified from the context Best accuracies in latest international event, around

65%

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Why do we need WSD ! Information Retrieval

Query: cricket bat Documents pertaining to the insect and the mammal, irrelevant

Machine Translation Consider English to Hindi translation

head to sar (upper part of the body) or adhyaksh (leader)

Machine Human interaction Instructions to machines

Interactive home system: turn on the lights Domestic Android: get the door

Applications are widespread and will affect our way of life

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TerminologyHarry cast a bewitching spell

Target word – the word whose intended sense is to be identified

spell

Context – the sentence housing the target word and possibly, 1 or 2 sentences around it

Harry cast a bewitching spell

Instance – target word along with its context

WSD is a classification problem wherein the occurrence of the target word is assigned to one of its many possible senses

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Corpus-Based Supervised Machine Learning

A computer program is said to learn from experience … if its performance at tasks … improves with experience

- Mitchell

Task : Word Sense Disambiguation of given test instances

Performance : Ratio of instances correctly disambiguated to the total test instances - accuracy

Experience : Manually created instances such that target words are marked with intended sense – training instances

Harry cast a bewitching spell / incantation

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Path Map

Introduction Background Data Experiments Conclusions

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Decision Trees A kind of classifier

Assigns a class by asking a series of questions Questions correspond to features of the instance Question asked depends on answer to previous question

Inverted tree structure Interconnected nodes

Top most node is called the root

Each node corresponds to a question / feature Each possible value of feature has corresponding branch

Leaves terminate every path from root Each leaf is associated with a class

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Automating Toy Selection for Max

Moving Parts ?

Color ?

Size ?

Car ?

Size ?

Car ? LOVE

LOVESO SO

LOVEHATE

HATE

SO SO

HATE

No

No

No

Yes

Yes

Yes

Blue

Big

Red

Small

Other

Small Big

ROOTNODES

LEAVES

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WSD Tree

Feature 4?

Feature 4 ?

Feature 2 ?

Feature 3 ?

Feature 2 ?

SENSE 4

SENSE 3SENSE 2

SENSE 1

SENSE 3

SENSE 3

0

0

0

1

1

1

0

10

1

0 1

Feature 1 ?

SENSE 1

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Issues…

Why use decision trees for WSD ? How are decision trees learnt ?

ID3 and C4.5algorithms

What is bagging and its advantages

Drawbacks of decision trees bagging

Pedersen[2002]: Choosing the right features is of greater significance than the learning algorithm itself

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Lexical Features Surface form

A word we observe in text Case(n)

1. Object of investigation 2. frame or covering 3. A weird person Surface forms : case, cases, casing An occurrence of casing suggests sense 2

Unigrams and Bigrams One word and two word sequences in text

The interest rate is low Unigrams: the, interest, rate, is, low Bigrams: the interest, interest rate, rate is, is low

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Part of Speech Tagging

Pre-requisite for many Natural Language Tasks Parsing, WSD, Anaphora resolution

Brill Tagger – most widely used tool

Accuracy around 95% Source code available Easily understood rules

Harry/NNP cast/VBD a/DT bewitching/JJ spell/NN

NNP proper noun, VBD verb past, DT determiner, NN noun

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Pre-Tagging Pre-tagging is the act of manually assigning tags to

selected words in a text prior to taggingMona will sit in the pretty chair//NN this time

chair is the pre-tagged word, NN is its pre-tag Reliable anchors or seeds around which tagging is done

Brill Tagger facilitates pre-tagging Pre-tag not always respected !

Mona/NNP will/MD sit/VB in/IN the/DT pretty/RB chair//VB this/DT time/NN

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Contextual Rules Initial state tagger – assigns most frequent tag for a type

based on entries in a Lexicon (pre-tag respected)

Final state tagger – may modify tag of word based on context (pre-tag not given special treatment)

Relevant Lexicon Entries Type Most frequent tag Other possible tags

chair NN(noun) VB(verb) pretty RB(adverb) JJ(adjective)

Relevant Contextual Rules Current Tag New Tag When

NN VB NEXTTAG DT RB JJ NEXTTAG NN

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Guaranteed Pre-Tagging A patch to the tagger provided – BrillPatch

Application of contextual rules to the pre-tagged words bypassed

Application of contextual rules to non pre-tagged words unchanged.

Mona/NNP will/MD sit/VB in/IN the/DT

pretty/JJ chair//NN this/DT time/NN

Tag of chair retained as NN Contextual rule to change tag of chair from NN to VB not applied

Tag of pretty transformed Contextual rule to change tag of pretty from RB to JJ applied

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Part of Speech Features A word in different parts of speech has different senses A word used in different senses is likely to have different

sets of pos around it

Why did jack turn/VB against/IN his/PRP$ team/NNWhy did jack turn/VB left/VBN at/IN the/DT crossing

Features used Individual word POS: P-2, P-1, P0, P1, P2*

P2 = JJ implies P2 is an adjective

Sequential POS: P-1P0, P-1P0 P1, and so on P-1P0 = NN, VB implies P-1 is a noun and P0 is a verb

A combination of the above

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Parse Features Collins Parser used to parse the data

Source code available Uses part of speech tagged data as input

Head word of a phrase the hard work, the hard surface Phrase itself : noun phrase, verb phrase and so on

Parent : Head word of the parent phrase fasten the line, cross the line Parent Phrase

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Sample Parse Tree

VERB PHRASENOUN PHRASE

Harry NOUN PHRASE

SENTENCE

spell

cast

a bewitching

NNP VBD

DT JJ NN

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Path Map

Introduction Background Data Experiments Conclusions

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Sense-Tagged Data Senseval2 data

4328 instances of test data and 8611 instances of training data ranging over 73 different noun, verb and adjectives.

Senseval1 data 8512 test instances and 13,276 training instances, ranging over 35

nouns, verbs and adjectives.

Line, hard, interest, serve data 4,149, 4,337, 4378 and 2476 sense-tagged instances with

line, hard, serve and interest as the head words.

Around 50,000 sense-tagged instances in all !

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Data Processing Packages to convert line hard, serve and interest data to

Senseval-1 and Senseval-2 data formats refine preprocesses data in Senseval-2 data format to make it

suitable for tagging Restore one sentence per line and one line per sentence, pre-tag

the target words, split long sentences posSenseval part of speech tags any data in Senseval-2 data

format Brill tagger along with Guaranteed Pre-tagging utilized

parseSenseval parses data in a format as output by the Brill Tagger

restores xml tags, creating a parsed file in Senseval-2 data format Uses the Collins Parser

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Sample line data instanceOriginal instance:art} aphb 01301041:" There's none there . " He hurried outside to see if there

were any dry ones on the line .

Senseval-2 data format:<instance id="line-n.art} aphb 01301041:"><answer instance="line-n.art} aphb 01301041:"

senseid="cord"/><context><s> " There's none there . " </s> <s> He hurried outside

to see if there were any dry ones on the <head>line</head> . </s>

</context></instance>

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Sample Output from parseSenseval<instance id=“harry"><answer instance=“harry" senseid=“incantation"/><context>Harry cast a bewitching <head>spell</head></context></instance>

<instance id=“harry"><answer instance=“harry" senseid=“incantation"/><context><P=“TOP~cast~1~1”> <P=“S~cast~2~2”> <P=“NPB~Potter~2~2”>

Harry <p=“NNP”/> <P=“VP~cast~2~1”> cast <p=“VB”/>

<P=“NPB~spell~3~3”>a <p=“DT”/> bewitching <p=“JJ”/> spell <p=“NN”/> </P> </P> </P>

</P> </context></instance>

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Issues… How is the target word identified in line, hard and

serve data How the data is tokenized for better quality pos

tagging and parsing How is the data pre-tagged How is parse output of Collins Parser interpreted How is the parsed output XML’ized and brought

back to Senseval-2 data format Idiosyncrasies of line, hard, serve, interest,

Senseval-1 and Senseval-2 data and how they are handled

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Path Map

Introduction Background Data Experiments Conclusions

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Surface Forms Senseval-1 & Senseval-2

Senseval-2 Senseval-1

Majority 47.7% 56.3%

Surface Form

49.3% 62.9%

Unigrams 55.3% 66.9%

Bigrams 55.1% 66.9%

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Individual Word POS (Senseval-1)

All Nouns Verbs Adj.

Majority 56.3% 57.2% 56.9% 64.3%

P-2 57.5% 58.2% 58.6% 64.0

P-1 59.2% 62.2% 58.2% 64.3%

P0 60.3% 62.5% 58.2% 64.3%

P1 63.9% 65.4% 64.4% 66.2%

P-2 59.9% 60.0% 60.8% 65.2%

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Individual Word POS (Senseval-2)

All Nouns Verbs Adj.

Majority 47.7% 51.0% 39.7% 59.0%

P-2 47.1% 51.9% 38.0% 57.9%

P-1 49.6% 55.2% 40.2% 59.0%

P0 49.9% 55.7% 40.6% 58.2%

P1 53.1% 53.8% 49.1% 61.0%

P-2 48.9% 50.2% 43.2% 59.4%

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Combining POS Features

Senseval-2 Senseval-1 line

Majority 47.7% 56.3% 54.3%

P0, P1 54.3% 66.7% 54.1%

P-1, P0, P1 54.6% 68.0% 60.4%

P-2, P-1, P0, P1 , P2 54.6% 67.8% 62.3%

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Effect Guaranteed Pre-tagging on WSD

Guar. P. Reg. P. Guar. P. Reg. PP-1, P0 62.2% 62.1% 50.8% 50.9%

P0, P1 66.7% 66.7% 54.3% 53.8%

P-1, P0, P1 68.0% 67.6% 54.6% 54.7%

P-1P0, P0P1 66.7% 66.3% 54.0% 53.7%

P-2, P-1, P0, P1 , P2

67.8% 66.1% 54.6% 54.1%

Senseval-1 Senseval-2

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Parse Features (Senseval-1)

All Nouns Verbs Adj.Majority 56.3% 57.2% 56.9% 64.3%

Head 64.3% 70.9% 59.8% 66.9%

Parent 60.6% 62.6% 60.3% 65.8%

Phrase 58.5% 57.5% 57.2% 66.2%

Par. Phr. 57.9% 58.1% 58.3% 66.2%

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Parse Features (Senseval-2)

All Nouns Verbs Adj.Majority 47.7% 51.0% 39.7% 59.0%

Head 51.7% 58.5% 39.8% 64.0%

Parent 50.0% 56.1% 40.1% 59.3%

Phrase 48.3% 51.7% 40.3% 59.5%

Par. Phr. 48.5% 53.0% 39.1% 60.3%

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Thoughts… Both lexical and syntactic features perform

comparably But do they get the same instances right ?

How much are the individual feature sets redundant Are there instances correctly disambiguated by

one feature set and not by the other ? How much are the individual feature sets

complementary

Is the effort to combine of lexical and syntactic features justified ?

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Measures Baseline Ensemble: accuracy of a hypothetical ensemble

which predicts the sense correctly only if both individual feature sets do so

Quantifies redundancy amongst feature sets

Optimal Ensemble: accuracy of a hypothetical ensemble which predicts the sense correctly if either of the individual feature sets do so

Difference with individual accuracies quantifies complementarity

We used a simple ensemble which sums up the

probabilities for each sense by the individual feature

sets to decide the intended sense

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Best Combinations

Data Set 1 Set 2 Base Maj. Ens. Opt.Sval2 Unigrams

55.3%

P-1,P0, P1

55.3%43.6% 47.7% 57.0% 67.9%

Sval1 Unigrams 66.9%

P-1,P0, P1 68.0%

57.6% 56.3% 71.1% 78.0%

line Unigrams 74.5%

P-1,P0, P1 60.4%

55.1% 54.3% 74.2% 82.0%

hard Bigrams 89.5%

Head, Par 87.7%

86.1% 81.5% 88.9% 91.3%

serve Unigrams 73.3%

P-1,P0, P1

73.0%58.4% 42.2% 81.6% 89.9%

Interest Bigrams 79.9%

P-1,P0, P1 78.8%

67.6% 54.9% 83.2% 90.1%

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Path Map

Introduction Background Data Experiments Conclusions

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Conclusions Significant amount of complementarity across

lexical and syntactic features Combination of the two justified

Part of speech of word immediately to the right of target word found most useful

Pos of words immediately to the right of target word best for verbs and adjectives

Nouns helped by tags on either side

Head word of phrase particularly useful for adjectives

Nouns helped by both head and parent

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Other Contributions Converted line, hard, serve and interest data

into Senseval-2 data format

Part of speech tagged and Parsed the Senseval2, Senseval-1, line, hard, serve and interest data

Developed the Guaranteed Pre-tagging mechanism to improve quality of pos tagging

Showed that guaranteed pre-tagging improves WSD

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Code, Data, Resources and Publication posSenseval : part of speech tags any data in Senseval-2 data format parseSenseval : parses data in a format as output by the Brill Tagger.

Output is in Senseval-2 data format with part of speech and parse information as xml tags.

Packages to convert line hard, serve and interest data to Senseval-1 and Senseval-2 data formats

BrillPatch : Patch to Brill Tagger to employ Guaranteed Pre-Tagging

http://www.d.umn.edu/~tpederse/data.html

Brill Tagger: http://www.cs.jhu.edu/~brill/RBT1_14.tar.Z Collins Parser: http://www.ai.mit.edu/people/mcollins

“Guaranteed Pre-Tagging for the Brill Tagger”, Mohammad and Pedersen, Fourth International Conference of Intelligent Systems and Text Processing, February 2003, Mexico

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Thank You