Word Sense Disambiguation and Intelligent Information Access

Word Sense Disambiguation
and
Intelligent Information Access

Pierpaolo Basile

basilepp@di.uniba.it
Department of Computer Science
University of Bari “A. Moro” (ITALY)

29 May 2009

Pierpaolo Basile (basilepp@di.uniba.it) WSD and IIA 29/05/09 1 / 55

Outline

1 Introduction
2 WSD Strategies
JIGSAW
JIGSAWz
HYDE : a hybrid strategy for WSD
COMBY : a combined strategy for WSD
3 WSD at Work
Information Filtering: ITR - ITem Recommender
Information Retrieval: Semantic Search
4 Conclusions and Future Work


Introduction Word Sense Disambiguation


Word Sense Disambiguation (WSD) is the problem of selecting a
sense for a word from a set of predeﬁned possibilities
sense inventory usually comes from a dictionary or thesaurus
polysemous word: having more than one possible meaning, e.g.
bank1 :
1 sloping land (especially the slope beside a body of water);
2 a ﬁnancial institution that accepts deposits and channels the money
into lending activities;
3 a long ridge or pile;
4 an arrangement of similar objects in a row or in tiers;
knowledge intensive methods, supervised learning, and (sometimes)
bootstrapping approaches

1
First four meanings in WordNet 3.0

Introduction Word Sense Disambiguation

Brief History

1949: noted as problem for Machine Translation
1950s - 1960s: semantic networks, AI approaches
1970s - 1980s: rule based systems, rely on hand crafted knowledge
sources
1990s: WordNet, corpus based approaches, sense tagged text
2000s: Hybrid Systems, minimizing or eliminating use of sense tagged
text, taking advantage of the Web, domain WSD


Introduction Intelligent Information Access


Problems
Explosion of irrelevant, unclear, inaccurate information
Users overloaded with a large amount of information impossible to
absorb




Problems
absorb

Consequences
Searching is time consuming
Need for intelligent solutions able to support users in ﬁnding
documents




Problems
absorb

Consequences
Searching is time consuming
Need for intelligent solutions able to support users in ﬁnding
documents

Solution
Intelligent Information Access: user-centric and semantically rich
approach to access information



WSD in Information Access

Machine Translation
Translate “plant” from English to Italian
Is it a “pianta” or a “impianto/stabilimento”?
Information Retrieval
Find all Web Pages about “bat”
The sport equipment or the nocturnal mammal ?
Question Answering
What is George Millers position on gun control?
The psychologist or US congressman?
Knowledge Acquisition
Add to KB: Herb Bergson is the mayor of Duluth, Minnesota or
Georgia?



WSD and Intelligent Information Access

Natural Language Processing can enhance Intelligent Information
Access
keywords not appropriate for representing content, due to polysemy,
synonymy, multi-word concepts
WSD provides semantics: concepts identiﬁcation in documents
Humans are able to comprehend the meaning of a text
Natural Language Processing and WSD convert human linguistic
abilities into more formal representations that are easier for computer
programs to understand


WSD Strategies JIGSAW

JIGSAW

JIGSAW
Knowledge-based WSD algorithm
Exploits WordNet senses
Three different strategies for: nouns, verbs and adjectives/adverbs
Main motivation: the effectiveness of a WSD algorithm is strongly
influenced by the PoS-tag



JIGSAW

JIGSAW
Knowledge-based WSD algorithm
Exploits WordNet senses
Three different strategies for: nouns, verbs and adjectives/adverbs
Main motivation: the effectiveness of a WSD algorithm is strongly
influenced by the PoS-tag

WordNet [Mil95]
Lexical reference database designed by Princeton University
English nouns, verbs, adverbs and adjectives organized into SYNonym
SETs (SYNSET)
Semantic relations among synsets



WordNet



WordNet

1 Synset Rank
2 Occurrences in SemCor
3 Oﬀset
4 SYNonym-SET

Gloss: synset deﬁnition
Examples of usage
Synset description = gloss + examples



JIGSAW algorithm

The algorithm
Input
d = (w1 , w2 , . . . , wh ) document
Output
X = (s1 , s2 , . . . , sk ) k ≤h
each si obtained by disambiguating wi based on the context of each
word
some words not recognized by WordNet
groups of words recognized as a single concept



JIGSAWnouns

The idea
Based on Resnik [Res95] algorithm for disambiguating noun groups
Given a set of nouns N = {n1 , n2 , . . . , nn } from document d
each ni has an associated sense inventory Si = {si1 , si2 , . . . , sik } of
possible senses
Goal: assigning each wi with the most appropriate sense sih ∈ Si ,
maximizing the similarity of ni with the other nouns in N

The strategy
Computing Semantic Similarity exploiting “noun hierarchy”
Give more credit to senses that are hyponym of the Most Speciﬁc
Subsumer (MSS)
Combine MSS information with Semantic Similarity



JIGSAWnouns



JIGSAWnouns

Final synset score
Linear combination between semantic similarity (with MSS
information) and synset rank in WordNet:

ϕ(sik ) = α ∗ sim(sik , N) + β ∗ R(k) (α + β = 1) (1)
R(k) takes into account the synset rank in WordNet:

k
R(k) = 1 − 0.8 ∗ (2)
n−1



JIGSAWnouns

Diﬀerences between JIGSAWnouns and Resnik
Leacock-Chodorow measure to compute similarity (instead of
Information Content)
Gaussian factor G, which takes into account the distance between
words in the text
Factor R, which takes into account the synset frequency score in
WordNet



JIGSAWverbs
The idea
Try to establish a relation between verbs and nouns (distinct IS-A
hierarchies in WordNet)
Verb wi disambiguated using:
nouns in the context C of wi
nouns into the description (gloss + WordNet usage examples) of each
candidate synset for wi



JIGSAWverbs
The idea
Try to establish a relation between verbs and nouns (distinct IS-A
hierarchies in WordNet)
Verb wi disambiguated using:
nouns in the context C of wi
nouns into the description (gloss + WordNet usage examples) of each
candidate synset for wi

The strategy
For each candidate synset sik of wi
computes nouns(i, k): the set of nouns in the description for sik
for each wj in C and each synset sik computes the highest similarity
maxjk
maxjk is the highest similarity value for wj wrt the nouns related to the
k-th sense for wi (using Leacock-Chodorow measure)
using G and R factors (JIGSAWnouns ) to weight semantic similarity


JIGSAWverbs : The algorithm

I play basketball and soccer. wi = play C = {basketball, soccer }
1 (70) play - (participate in games or sport; “We played hockey all
afternoon”; “play cards”; “Pele played for the Brazilian teams in
many important matches”)
2 (29) play - (play on an instrument; “The band played all night long”)
3 ...
Build nouns set for each sik :
1 nouns(play,1): game, sport, hockey, afternoon, card, team, match
2 nouns(play,2): instrument, band, night
3 ...




wi = play
C = {basketball, soccer }
nouns(play,1): game, sport, hockey, afternoon, card, team, match




Finally, an overall similarity score, ϕ(i, k), among sik and the whole
context C is computed:

wj ∈C Gauss(position(wi ), position(wj )) · maxjk
ϕ(i, k) = R(k) · (3)
h Gauss(position(wi ), position(wh ))

The synset assigned to wi is the one with the highest ϕ value



JIGSAWothers

Based on the WSD algorithm proposed by Banerjee and Pedersen
[BP02, BP03] (inspired to Lesk [Les86])
Idea: computes the overlap between the glosses of each candidate
sense (including related synsets) for the target word to the glosses of
all words in its context
assigns the synset with the highest overlap score
if ties occur, the most common synset in WordNet is chosen
Given the sentence: “I bought a bottle of aged wine”
the context is C = {bottle, wine}
the first two synsets for aged are:
1 (advanced in years; ”aged members of the society”; ”elderly residents
could remember the construction of the first skyscraper”; ”senior
citizen”);
2 (of wines, fruit, cheeses; having reached a desired or final condition;
”mature well-aged cheeses”)


WSD Strategies JIGSAWz

JIGSAWz : ZIPF distribution

Zipf’s law: the frequency of an event is inversely proportional to its
rank in the frequency table
similar to words distribution: the most frequent word occurs
approximately twice the second most frequent word, which occurs
twice the fourth most frequent word, ...


JIGSAWz

Modify R factor using ZIPF distribution:

1/k s
f (k; N; s) = N
(4)
s
n=1 1/n
where:
N is the number of word meanings
k is the word meaning rank. We adopt the WordNet synset rank
s is the value of the exponent characterizing the distribution
Compute the frequency of the word meaning in SemCor
Approximate s using the Pearson’s chi-square χ2 test method



NLP tools for the evaluation

WSD requires pre-processing steps: tokenization, stemming,
PoS-tagging and lemmatization
META (MultilanguagE Text Analyzer) [BdG+ 08] implements several
NLP tasks and provides tools for semantic indexing of documents:
Text normalization and tokenization
Stemming (SNOWBALL library)
Lemmatization
English: WordNet Morphological Analyzer
Italian: Morph-it! and Lemmagen tool (Ripple Down Rule learning)
POS-tagging based on ACOPOST T3 (HMM - Hidden Markov Model)
Entity recognition based on SVM classiﬁer (YAMCHA)
WSD: English/Italian



JIGSAW Evaluation

SensEval-3 All-Words Task
disambiguation of all words contained into English texts
sense inventory: WordNet 1.7.1
2.041 words
inter-annotators agreement rate was approximately 72,5%

EVALITA WSD All-Words Task
disambiguation of all words contained into Italian texts
sense inventory: ItalWordNet
about 5,000 words
no information about inter-annotators agreement rate



JIGSAW Evaluation: Results
JIGSAW at SensEval-3 All-Words Task
system P R A(%) F
1st sense 0.624 0.651 100 0.651
BestUnsupervised 0.583 0.582 100 0.582
JIGSAW 0.525 0.525 100 0.525
JIGSAWz 0.606 0.606 100 0.606

JIGSAW at EVALITA WSD All-Words Task [BS07]
system P R A(%) F
1st sense 0.648 0.614 94.7 0.631
Random 0.483 0.458 94.7 0.470
JIGSAW 0.598 0.567 94.7 0.582
JIGSAWz 0.639 0.606 94.7 0.622


WSD Strategies HYDE : a hybrid strategy for WSD

Supervised Learning for WSD

Supervised Learning for WSD
Exploits machine learning techniques to induce models of word usage from
large text collections
annotated corpora are tagged manually using semantic classes chosen
from a sense inventory
each sense-tagged occurrence of a particular word is transformed into
a feature vector, which is then used in an automatic learning process



Problems and Motivation
Knowledge-based methods
outperformed by supervised methods
high coverage: applicable to all words in unrestricted text




Supervised methods
high precision
low coverage: applicable only to those words for which annotated
corpora are available




Supervised methods
high precision
low coverage: applicable only to those words for which annotated
corpora are available

Solution
HYDE : combination of Knowledge-based (JIGSAW ) methods and
Supervised Learning can improve WSD eﬀectiveness [BdLS08]
Knowledge-based methods improve coverage
Supervised Learning strategies improve precision


Supervised Learning
Exploited features
nouns: the first noun, verb or adjective before the target noun, within
a (left) window of at most three words to the left and its PoS-tag
verbs: the first word before and the first word after the target verb
and their PoS-tag
adjectives: six nouns (before and after the target adjective)
adverbs: the same as adjectives but adjectives rather than nouns are
used

Training corpus: MultiSemCor
1 Italian translations of the SemCor texts
2 automatically aligning Italian and English texts
3 automatically transferring the word sense annotations from English
(WordNet) to the aligned Italian (MultiWordNet) words


Supervised Learning

K-NN algorithm for WSD
Learning: build a vector for each annotated word
Classiﬁcation:
build a vector vf for each word in the text
compute similarity between vf and the training vectors
rank the training vectors in decreasing order according to the similarity
value
choose the most frequent sense in the ﬁrst K vectors



HYDE Evaluation

Dataset: EVALITA WSD All-Words Task Dataset
Two strategies:

Integrating JIGSAW into a supervised learning method
supervised method is applied to words for which training examples are
provided
JIGSAW is applied to words not covered by the ﬁrst step

Integrating supervised learning into JIGSAW
JIGSAW is applied to assign a sense to the words which can be
disambiguated with a high level of conﬁdence
remaining words are disambiguated by the supervised method



HYDE Evaluation: Baselines

Baselines for EVALITA WSD All-Words Task Dataset
Setting P R F A (%)
1st sense 0.648 0.614 0.631 94.7
Random 0.484 0.484 0.484 100.0
JIGSAW 0.639 0.606 0.622 94.7
K-NN 0.797 0.336 0.473 42.2



HYDE : Evaluation results
1st sense (0.631), Random (0.470), JIGSAW (0.622), K-NN (0.484)
Integrating JIGSAW into a supervised learning method
Setting P R F A (%)
K-NN + JIGSAW 0.624 0.591 0.607 94.7
K-NN + JIGSAW (ϕ ≥ 0.80) 0.693 0.337 0.453 48.6
K-NN + JIGSAW (ϕ ≥ 0.60) 0.680 0.410 0.512 60.3
K-NN + JIGSAW (ϕ ≥ 0.40) 0.652 0.452 0.534 69.3
K-NN + JIGSAW (ϕ ≥ 0.20) 0.652 0.452 0.534 69.3

Integrating supervised learning into JIGSAW
Setting P R F A (%)
JIGSAW (ϕ ≥ 0.80) + K-NN 0.715 0.392 0.556 55.6
JIGSAW (ϕ ≥ 0.60) + K-NN 0.688 0.440 0.537 64.0
JIGSAW (ϕ ≥ 0.40) + K-NN 0.651 0.484 0.555 74.4


WSD Strategies COMBY : a combined strategy for WSD

COMBY : a combined strategy for WSD

COMBY WSD framework: combines the output data of several WSD
algorithms
run a set of WSD algorithms on a sense-annotated corpus (TRC )
obtain a set of output data O = {o1 , o2 , .., oN } where each oi is the
output provided by the i − th algorithm
each output oi contains for each word instance wj in TRC a list of pairs
(< synset1 , score1 >, ..., < synsetk , scorek >, ..., < synsetl , scorel >)
combination step: run WSD algorithms on a not sense-annotated
corpus (TSC ):
run the WSD algorithms on a diﬀerent dataset (TSC )
obtain a set of output data
combination of outputs: voting strategies and supervised methods



Combination strategies

Voting strategies
1 simple voting: the sense that has the majority of votes is chosen
2 simple voting using the information about the synset score: the vote
for each synset is the sum of all scores in each WSD system
3 simple voting using diﬀerent weights for each system according to the
WSD performance in TRC



Combination strategies

Voting strategies
1 simple voting: the sense that has the majority of votes is chosen
2 simple voting using the information about the synset score: the vote
for each synset is the sum of all scores in each WSD system
3 simple voting using diﬀerent weights for each system according to the
WSD performance in TRC

Supervised methods
1 several classiﬁcation algorithms using the WEKA package
2 Support Vector Machine adopting the open-source software LIBSVM
3 using unsupervised predictions into a supervised system



COMBY evaluation

Dataset
TRC training: SemCor 1.7.1
TRS testing: SensEval-3 All-Words Task
1s t sense baseline: (F=0,651)

Involved WSD systems
JIGSAW : a knowledge-based WSD algorithm that exploits WordNet
as knowledge-base.
AitorKB: graph-based method for performing knowledge-based WSD
[AS08]
TS: exploits Topic Signatures to disambiguate nouns [AdL04]
RIC : automatically builds examples from the Web using a new
approach based on the “monosemous relative” method [MAW06]



COMBY evaluation: voting strategies
Performance of each systems
System P R F A(%)
JIGSAW 0.554 0.554 0.554 100.0
TS 0.458 0.215 0.292 46.9
RIC 0.397 0.396 0.396 99.8
AitorKB 0.600 0.600 0.600 100.0

Voting strategies
Strategy P R F A(%)
Simple 0.587 0.587 0.587 100.0
Z-Score 0.575 0.575 0.575 100.0
Rank 0.615 0.615 0.615 100.0



COMBY evaluation: supervised combination
Combination using WEKA
Classiﬁer P R F A(%)
Naive Bayes 0.653 0.653 0.653 100.0
Decision Trees 0.649 0.649 0.649 100.00
Ada Boost 0.647 0.647 0.647 100.0
K-NN 0.643 0.643 0.643 100.0
SMO (SVM) 0.653 0.653 0.653 100.0

Combination using LIBSVM and a supervised system (Knn.ehu [AdL07])
System P R F A(%)
LIBSVM 0.654 0.654 0.654 100.0
Knn.ehu 0.667 0.667 0.667 100.0
Knn.ehu+predictions 0.671 0.671 0.671 100.0

WSD at Work

WSD at Work

Exploit WSD techniques in real application scenarios
Information Filtering
Content-based recommending system
User profiles compared against item descriptions to provide
recommendations
Problems: keywords not appropriate for representing content, due to
polysemy, synonymy, multi-word concepts

Selection of documents, from a fixed collection, which satisfy a user’s
one-off information need (query)
Problems: polysemy and synonymy


WSD at Work Information Filtering: ITR - ITem Recommender

Information Filtering: ITR - ITem Recommender

ITR - ITem Recommender [SDLB07]: framework for Intelligent User
Profiling based on:
Word Sense Disambiguation for detecting relevant concepts
representing user interests
Naive Bayes text categorization algorithm for learning user profiles
from disambiguated documents
Concept-based user profiles:
Bag-of-Synset: a synset vector corresponds to a document, instead of a
word vector
synsets provided by JIGSAW
recognition of n-grams
synonyms represented by the same synsets



ITR evaluation

EachMovie Dataset: Project conducted by Compaq Research Centre
(1996-1997)
Dataset of user-movie ratings
About 2.8 millions ratings
72,916 users
1,628 items (movies) divided in 10 categories (Genre)
Discrete rating on a 6-point scale
Movie content crawled from the Internet Movie Database (IMDb)
10 movie categories/genres
933 randomly selected users
100 users for each category, only for Category 2 Animation, 33 users
selected
Each user rated between 30 and 100 movies
Goal: compare performance of keyword-based proﬁles vs.
synset-based proﬁles



ITR evaluation results
Performance of the two versions of ITR on 10 diﬀerent ‘genre’ EachMovie
dataset
Precision Recall F1
Id ITR ITR ITR ITR ITR ITR
Genre BOW BOS BOW BOS BOW BOS

1 0.70 0.74 0.83 0.89 0.76 0.80
2 0.51 0.57 0.62 0.70 0.54 0.61
3 0.76 0.86 0.84 0.96 0.79 0.91
4 0.92 0.93 0.99 0.99 0.96 0.96
5 0.56 0.67 0.66 0.80 0.59 0.72
6 0.75 0.78 0.89 0.92 0.81 0.84
7 0.58 0.73 0.67 0.83 0.71 0.79
8 0.53 0.72 0.65 0.89 0.58 0.79
9 0.70 0.77 0.83 0.91 0.75 0.83
10 0.71 0.75 0.86 0.91 0.77 0.81
Mean 0.67 0.75 0.78 0.88 0.73 0.81


WSD at Work Information Retrieval: Semantic Search

Information Retrieval Evaluation

Two kinds of evaluation
SemEval-2007 Task 1: indexing of a documents collection for Cross
Language IR [BDG+ 07]
application-driven task
ﬁxed cross-language information retrieval system
participants disambiguate text by assigning WordNet synsets (29,681
documents)

CLEF 2008: Ad-Hoc Robust WSD task: classical IR benchmark using
Cross Language dataset [BCS08]
166,726 documents
160 topics in English and Spanish



SemEval-2007 Task 1 results

SemEval-2007 task 1 results
system IR documents CLIR
no expansion 0.3599 0.1446
full expansion 0.1610 0.2676
1st sense 0.2862 0.2637
ORGANIZERS 0.2886 0.2664
JIGSAW 0.3030 0.1373
PART-B 0.3036 0.1734

Performance of each system

system precision recall attempted
ORGANIZERS 0.591 0.566 95.76%
JIGSAW 0.484 0.338 69.98%
PART-B 0.334 0.186 55.68%



CLEF 2008: system setup

N-Levels model [BCG+ 08]: each document has N levels of
representations
Each level has:
local feature weighting
local similarity function
Global ranking function: merges the results of diﬀerent levels
N-Levels for CLEF 2008:
2 levels: stemming (TF/IDF) and synset (SF/IDF)
Global ranking function: Z-Score normalization and CombSUM
aggregation strategy



CLEF 2008: results
N-levels results on CLEF 2008
Run MONO CROSS N-Levels WSD MAP
MONO1TDnus2f X - - - 0.168
MONO11nus2f X - - - 0.192
MONO12nus2f X - - - 0.145
MONO13nus2f X - - - 0.154
MONO14nus2f X - - - 0.068
MONOwsd1nus2f X - - X 0.180
MONOwsd11nus2f X - - X 0.186
MONOwsd12nus2f X - X X 0.220
MONOwsd13nus2f X - X X 0.227
CROSS1TDnus2f X X - - 0.025
CROSS1nus2f X X - - 0.015
CROSSwsd1nus2f X X - X 0.071
CROSSwsd11nus2f X X X X 0.060
CROSSwsd12nus2f X X X X 0.072


Conclusions and Future Work

Conclusions

The problem of word ambiguity into the context of intelligent
information access is exploited
Several WSD methods are proposed and evaluated:
JIGSAW knowledge-based algorithm
HYDE combination of knowledge-based and supervised approaches
COMBY combination of unsupervised methods
Evaluation: Senseval-3 All Words Task and EVALITA All Words Task
Languages diﬀerent from English: knowledge-based and a hybrid
strategy for Italian WSD are proposed
Evaluation in real application scenarios: Information Filtering and
WSD can enhance real applications in the domain of Intelligent
Information Access



Future Work

Include information about a speciﬁc domain into the WSD process
More investigation on the interaction between IR and WSD is needed
document expansion
query disambiguation/expansion
word polysemy
Other semantic features could be exploited: Named Entity and Entity
Relation



That’s all folks!



For Further Reading I

E. Agirre and O.L. de Lacalle.
Publicly available topic signatures for all WordNet nominal senses.
In Proceedings of the 4th International Conference on Languages
Resources and Evaluations (LREC 2004), 2004.
E. Agirre and O.L. de Lacalle.
UBC-ALM: Combining k-NN with SVD for WSD.
pages 342–345, 2007.
Eneko Agirre and Aitor Soroa.
Using the Multilingual Central Repository for Graph-Based Word
Sense Disambiguation.
In European Language Resources Association (ELRA), editor,
Proceedings of the Sixth International Language Resources and
Evaluation (LREC’08), Marrakech, Morocco, may 2008.



For Further Reading II

Pierpaolo Basile, Annalina Caputo, Anna Lisa Gentile, Marco
Degemmis, Pasquale Lops, and Giovanni Semeraro.
Enhancing Semantic Search using N-Levels Document Representation.
In Stephan Bloehdorn, Marko Grobelnik, Peter Mika, and Duc Thanh
Tran, editors, SemSearch, volume 334 of CEUR Workshop
Proceedings, pages 29–43. CEUR-WS.org, 2008.
P. Basile, A. Caputo, and G. Semeraro.
Uniba-Sense at Clef 2008: SEmantic N-levels Search Engine.
In F. Borri, A. Nardi, and C. Peters, editors, Results of
Cross-Language Evaluation Forum 2008 (CLEF 2008), page 9, 2008.
ISSN: 1818-8044.



For Further Reading III

P. Basile, M. Degemmis, A.L. Gentile, P. Lops, and G. Semeraro.
UNIBA: JIGSAW Algorithm for Word Sense Disambiguation.
In Proceedings of the 4th ACL 2007 International Worshop on
Semantic Evaluations (SemEval-2007), pages 398–401. Association for
Computational Linguistics (ACL), 2007.
P. Basile, M. de Gemmis, A.L. Gentile, L. Iaquinta, P. Lops, and
G. Semeraro.
META - MultilanguagE Text Analyzer.
In Proceedings of the Language and Speech Technnology Conference -
LangTech 2008, Rome, Italy, February 28-29, pages 137–140, 2008.



For Further Reading IV

Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, and Giovanni
Semeraro.
Combining Knowledge-based Methods and Supervised Learning for
Eﬀective Italian Word Sense Disambiguation.
In Rodolfo Delmonte and Johan Bos, editors, Symposium on
Semantics in Systems for Text Processing, STEP 2008, Venice, Italy,
September 22-24, 2008, Proceedings, volume 1 of Research in
Computational Semantics, pages 5–16. College Publications, 2008.
S. Banerjee and T. Pedersen.
An Adapted Lesk Algorithm for Word Sense Disambiguation Using
WordNet.
In CICLing ’02: Proceedings of the Third International Conference on
Computational Linguistics and Intelligent Text Processing, pages
136–145, London, UK, 2002. Springer-Verlag.



For Further Reading V

S. Banerjee and T. Pedersen.
Extended gloss overlaps as measure of semantic relatedness.
In Proceedings of 18th International Joint Conference on Artiﬁcial
Intelligence (IJCAI), pages 805–810, Acapulco Mexico, 2003.
Pierpaolo Basile and Giovanni Semeraro.
JIGSAW: An algorithm for word sense disambiguation.
Intelligenza Artiﬁciale, 4(2):53–54, 2007.
M. Lesk.
Automatic sense disambiguation using machine readable dictionaries:
how to tell a pine cone from an ice cream cone.
In Proceedings of ACM SIGDOC Conference, pages 24–26, 1986.



For Further Reading VI

D. Martinez, E. Agirre, and X. Wang.
Word relatives in context for word sense disambiguation.
In Proc. of the 2006 Australasian Language Technology Workshop,
pages 42–50, 2006.
G. A. Miller.
WordNet: a lexical database for English.
Commun. ACM, 38(11):39–41, 1995.
P. Resnik.
Disambiguating noun groupings with respect to WordNet senses.
In Proceedings of the Third Workshop on Very Large Corpora, pages
54–68. Association for Computational Linguistics, 1995.



For Further Reading VII

G. Semeraro, M. Degemmis, P. Lops, and P. Basile.
Combining Learning and Word Sense Disambiguation for Intelligent
User Proﬁling.
In Proceedings of the Twentieth International Joint Conference on
Artiﬁcial Intelligence IJCAI-07, pages 2856–2861, 2007.
M. Kaufmann, San Francisco, California. ISBN: 978-I-57735-298-3.


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