A Domain Based Approach to Information Retrieval in Digital Libraries

Università degli studi di Bari “Aldo Moro”Dipartimento di Informatica

A Domain Based Approach to Information Retrieval in Digital Libraries

F. Rotella, S. Ferilli, F. [email protected], {fabio.leuzzi, rotella.fulvio}@gmail.com

8th Italian Research Conference on Digital LibrariesBari, Italy, February 9-10, 2012

L.A.C.A.M. http://lacam.di.uniba.it:8000

mailto:[email protected]

Overview

● Introduction & Objectives

● Keyword Extraction

● Word Sense Disambiguation

● Synset Clustering

● A Multistrategy Similarity Measure

● Document Partitioning

● User Query Processing

● A Preliminary Evaluation

● Conclusions & Future Works

A Domain Based Approach to Information Retrieval in Digital Libraries - F. Rotella, S. Ferilli, F. Leuzzi 2

Some repositories leave the responsibility of quality to the authors.

+Anybody can produce and distribute documents.

=Possible low average quality of the repository contents.

Users are often overwhelmed by documents that only apparently are

suitable for satisfying their information needs.

Introduction


Introduction● Possible way out: Information Retrieval systems

● Numerical/statistical manipulation of (key)words has

been widely explored in the literature

● Still unable to fully solve the problem

● Achieving better retrieval performance requires to go

beyond simple lexical interpretation of the user

queries

● Pass through an understanding of their semantic

content and aims

● Ontological taxonomy

● WordNet

● WordNet DomainsA Domain Based Approach to Information Retrieval in Digital Libraries - F. Rotella, S. Ferilli, F. Leuzzi 4

Objectives

Improving fruition of a DL

● Use of advanced techniques for document retrieval

● Try to overcome the ambiguity of natural language

● Inspired by the typical behavior of humans:

● take into account the possible meanings of words

● select the most appropriate one according to the

context of the discourse


Keyword Extraction● Each document in the digital library is progressively split into paragraphs,

sentences, and single words

● Integrated in the DOMINUS framework

● Obtained the syntactic structure of sentences, and the lemmas

● Integrated in the Stanford Parser

● Classical VSM

● TF*IDF weighting

● Two filters:

● Only nouns considered

● The representation of adverbs, verbs and adjectives in WordNet is

different

● Only the top 10% keywords for each document

● To be noise-tolerant

● To limit the possibility of including non-discriminative and very general

words in the representation of a documentA Domain Based Approach to Information Retrieval in Digital Libraries - F. Rotella, S. Ferilli, F. Leuzzi 6

Word Sense DisambiguationDomain Driven

One Domain per Discourse assumption: many uses of a word

in a coherent portion of text tend to share the same domain.

Prevalent domain

individuation

Prevalent domain

individuation

Extraction of all

synsets for each term

Extraction of all

synsets for each term

Extraction of all

domains for each synset

Extraction of all

domains for each synset

Choice of prevalent

domain synset

Choice of prevalent

domain synset


Synset Clustering

Pairwise complete link agglomerative strategy


● Each synset generates a singleton cluster

● For each pair of clusters

● If the complete link property holds

● Merge the involved clusters

A MultistrategySimilarity Measure

Cooperating Techniques for Extracting Conceptual Taxonomies from Text - S. Ferilli, F. Leuzzi, F. Rotella 9

3 components are summed and

normalized, in ]0,1[

● depth (ancestors)

● breadth (direct neighbors)

● breadth (inverse neighbors)

WordNet relationship are considered

A Multistrategy Similarity MeasureCosidered Relationship

member meronimy: the latter synset is a member meronym of the former;

substance meronimy: the latter synset is a substance meronym of the former;

part meronimy: the latter synset is a part meronym of the former;

similarity: the latter synset is similar in meaning to the former;

antonym: specifies antonymous word;

attribute: defines the attribute relation between noun and adjective synset

pairs in which the adjective is a value of the noun;

additional information: additional information about the first word can be

obtained by seeing the second word;

part of speech based: specifies two different relations based on the parts of

speech involved;

participle: the adjective first word is a participle of the verb second word;

hyperonymy: the latter synset is a hypernym of the former.


Document Partitioning

● SynsetWord structure:

● Original word

● TF*IDF weight

● Synset

● The Pairwise Clustering step returned a set of synset clusters

● For each document in the collection

● Each of its SynsetWord votes with its TF*IDF weight

● The first three clusters are chosen from the ranked list

● They represent the intensional description of the document


Users Query ElaborationOverview

● Same grammatical preprocessing as in the previous phase

● Query usually very short

● No keyword extraction: all nouns retained for the next

operations

● WSD Domain Driven unreliable

● For each word, all corresponding synsets in WordNet are kept

● A single lexical query yields many semantic queries

● All possible combinations of synsets


Users Query ElaborationA Brute Force WSD

For each combination:

● a similarity evaluated against each cluster that has at

least one associated document

● using the same similarity function as for clustering

Twofold objective:

● finding the combination of synsets that represents the

best word sense disambiguation

● obtaining the most similar cluster to the involved words


Users Query ElaborationQuery Results

The best combination is used to obtain the list of clusters

ranked by descending relevance, that can be used as an

answer to the user search.

The results are then displayed to the user, in particular are

displayed the first n sets of document such that n is the

minimum value that shows at least 10 results.


A Preliminary EvaluationThe Quality of Clusters


86 documents, 4 topics:

27 general science and physics; 21 music; 15 politics; 23 religion.

Query: Reincarnation and eternal life

Best combination:

● synset: 106191212; lemmas: reincarnation; gloss: the Hindu or Buddhist doctrine that a

person may be reborn successively into one of five classes of living beings (god or human or

animal or hungry ghost or denizen of Hell) depending on the person’s own actions;

● synset: 100006269; lemmas: life; gloss: living things collectively.

Most similar cluster:

● synset: 106191212; lemmas: reincarnation; gloss: the Hindu or Buddhist doctrine that a

person may be reborn successively into one of five classes of living beings (god or human or

animal or hungry ghost or denizen of Hell) depending on the person’s own actions;

● synset: 105943300; lemmas: doctrine, philosophical system, philosophy and school of

thought; gloss: a belief (or system of beliefs) accepted as authoritative by some group or

school;

● synset: 105941423; lemmas: belief; gloss: any cognitive content held as true.

Query: Ornaments and melodies

Best combination:

● synset: 103169390; lemmas: decoration, ornament and ornamentation; gloss: something used to

beautify;

● synset: 107028373; lemmas: air, line, melodic line, melodic phrase, melody, strain and tune; gloss: a

succession of notes forming a distinctive sequence.

Most similar cluster:

● synset: 107025900; lemmas: classical, classical music and serious music; gloss: traditional genre of

music conforming to an established form and appealing to critical interest and developed musical

taste;

● synset: 107033753; lemmas: mass; gloss: a musical setting for a Mass;

● synset: 107026352; lemmas: opera; gloss: a drama set to music, consists of singing with orchestral

accompaniment and an orchestral overture and interludes;

● synset: 107071942; lemmas: genre, music genre, musical genre and musical style; gloss: an

expressive style of music;

● synset: 107064715; lemmas: rock, rock ’n’ roll, rock and roll, rock music, rock’n’roll and rock-and-

roll; gloss: a genre of popular music originating in the 1950s, a blend of black rhythm-and-blues with

white country-and-western.


A Preliminary EvaluationThe Quality of Clusters


# Query Outcomes Precision Recall

1 Ornaments and melodies [1 to 9] music[10 to 11] religion 0.82 (1.0) 0.43 (9/21)

2 Reincarnation and eternal life [1 to 9] religion[10] science 0.9 (1.0) 0.39 (9/23)

3 Traditions and folks[1 to 4] music

[5 to 6] religion[7 to 10] music

0.8 (1.0) 0.38 (8/21)

4 Limits of theory of relativity[1 to 2] science

[3] politics[4 to 5] religion

[6 to 15] science

0.8 0.44 (12/27)

5 Capitalism vs communism

[1 to 3] politics[4] science

[5 to 6] religion[7 to 11] politics

[12] science[13] music

0.61 (0.77) 0.53 (8/15)

6 Markets and new economy

[1] politics[2] music

[3] science[4 to 8] politics

[9 to 10] religion

0.6 (0.7) 0.4 (6/15)

7 Relationship between democracy and parliament[1 to 3] politics

[4] science[5 to 6] politics

[7 to 10] religion

0.5 (0.6) 0.33 (5/15)

A Preliminary EvaluationSynthesis of Outcomes

ConclusionsProposed an approach to extract information from digital libraries

● Go beyond simple lexical matching, toward the semantic content

underlying queries

The approach consists of:

● An off-line preprocessing on the entire corpus

● Find sets of synset as intensional descriptions for the documents

● An on-line phase on the queries

● Find the most suitable sense, evaluating all possible combinations

of synset against each intensional descriptions of the documents

● In order to propose as result the most relevant ones

Preliminary experiments show that this approach can be viable.


Future Works

● Substitution of the ODD assumption with a more elaborated

strategy for WSD

● Avoiding the pre-processing step

● To handle cases when new documents are progressively

included in the collection

● Including adverbs, verbs and adjectives

● To improve the quality of the semantic representatives of the

documents

● To explore other approaches to choose better intensional

descriptions of each document


Technology

A Domain Based Approach to Information Retrieval in Digital Libraries